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This project was designed to find when a new climate normal emerges beyond different thresholds of natural climate variability with the goal to help natural resource managers, other practitioners, and scientists concerned with emerging climate signals. Estimates are provided for the time (year) when a biologically-relevant temperature signal emerges (time of emergence - ToE) above natural variability considering an early industrial period climate and the strength of the signal (degrees C) at the ToE. The year-to-year \u201cnatural\u201d variability is estimated as the noise in which the signal must persistently surpass for the emergence of a climate change signal. A time series of the signal to noise ratio is provided from which the ToE is estimated. The input data for the estimates include multiple observations as well as multiple large ensembles of Global Climate Models (GCMs). The GCMs include estimates of signal that may emerge in the future under a moderate emission scenario (RCP4.5/SSP2-45). 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The input data for the estimates include multiple observations as well as multiple large ensembles of Global Climate Models (GCMs). The GCMs include estimates of signal that may emerge in the future under a moderate emission scenario (RCP4.5/SSP2-45). 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Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid southwestern U.S.A. streams at the southern-most edge of all cutthroat trout distributions; thus making RGCT particularly vulnerable to the anticipated warming and drying in this region. However, RGCT may possess a portfolio of life-history traits that aide in their persistence, attributes commonly observed in trailing edge populations. We used otolith and multistate capture-mark-recapture data collected along a temperature and stream drying gradient to determine how these environmental constraints influence life-history trait expression (length- and age-at-maturity), demography, and extirpation risk in RGCT populations from northern New Mexico, U.S.A. We found the rate at which RGCT reached maturity was highest at warm to intermediate stream temperatures, which was the demographic trait most strongly linked to RGCT persistence. Interestingly, older life-stages contributed more to population growth as temperatures decreased, providing further evidence of strong temperature effects controlling life-history trait expression in RGCT. Precipitation, however, had little effect on RGCT population dynamics and was likely influenced by the uncharacteristically wet years (2016-2017) during this study. Regardless, our results suggest that RGCT persistence depends on temperature-influenced diversity in life-history trait expression (e.g., longevity, age-at-maturity). Plasticity in trait expression is an important life-history characteristic of coldwater species, and may be vital for trailing edge populations to persist in a changing climate.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/kbmw-6z60","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.5f773de882ce20f3301008a2.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f773de882ce20f3301008a2","keyword":["Drought","Growth","Southwestern Cutthroat Trout","Survival","USGS:5f773de882ce20f3301008a2","biota"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-106.5454, 35.9157, -105.3369, 36.6772","theme":["geospatial"],"title":"Influence of Stream Woody Debris on Eight Populations of Rio Grande Cutthroat Trout in Northern New Mexico"},"description":"The impacts of climate change on cold water species will likely manifest in populations at the trailing edge of their distribution. Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid southwestern U.S.A. streams at the southern-most edge of all cutthroat trout distributions; thus making RGCT particularly vulnerable to the anticipated warming and drying in this region. However, RGCT may possess a portfolio of life-history traits that aide in their persistence, attributes commonly observed in trailing edge populations. We used otolith and multistate capture-mark-recapture data collected along a temperature and stream drying gradient to determine how these environmental constraints influence life-history trait expression (length- and age-at-maturity), demography, and extirpation risk in RGCT populations from northern New Mexico, U.S.A. We found the rate at which RGCT reached maturity was highest at warm to intermediate stream temperatures, which was the demographic trait most strongly linked to RGCT persistence. Interestingly, older life-stages contributed more to population growth as temperatures decreased, providing further evidence of strong temperature effects controlling life-history trait expression in RGCT. Precipitation, however, had little effect on RGCT population dynamics and was likely influenced by the uncharacteristically wet years (2016-2017) during this study. Regardless, our results suggest that RGCT persistence depends on temperature-influenced diversity in life-history trait expression (e.g., longevity, age-at-maturity). 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Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid southwestern U.S.A. streams at the southern-most edge of all cutthroat trout distributions; thus making RGCT particularly vulnerable to the anticipated warming and drying in this region.  We collected stream temperature and stream drying to determine how these environmental constraints influence life-history trait expression (length- and age-at-maturity), demography, and extirpation risk in RGCT populations from northern New Mexico, U.S.A. We found the rate at which RGCT reached maturity was highest at warm to intermediate stream temperatures, which was the demographic trait most strongly linked to RGCT persistence. Interestingly, older life-stages contributed more to population growth as temperatures decreased, providing further evidence of strong temperature effects controlling life-history trait expression in RGCT.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/kbmw-6z60","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.5f776cd582ce20f3301009ea.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f776cd582ce20f3301009ea","keyword":["Drought","Growth","Southwestern Cutthroat Trout","Survival","USGS:5f776cd582ce20f3301009ea","biota"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-106.5454, 35.9157, -105.3369, 36.6772","theme":["geospatial"],"title":"Influence of Stream Temperature on Eight Populations of Rio Grande Cutthroat Trout in Northern New Mexico"},"description":"The impacts of climate change on cold water species will likely manifest in populations at the trailing edge of their distribution. Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid southwestern U.S.A. streams at the southern-most edge of all cutthroat trout distributions; thus making RGCT particularly vulnerable to the anticipated warming and drying in this region.  We collected stream temperature and stream drying to determine how these environmental constraints influence life-history trait expression (length- and age-at-maturity), demography, and extirpation risk in RGCT populations from northern New Mexico, U.S.A. We found the rate at which RGCT reached maturity was highest at warm to intermediate stream temperatures, which was the demographic trait most strongly linked to RGCT persistence. Interestingly, older life-stages contributed more to population growth as temperatures decreased, providing further evidence of strong temperature effects controlling life-history trait expression in RGCT.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/9460fdf8-d61f-45f2-ac65-9b6c7ea35d97","harvest_record_raw":"https://catalog.data.gov/harvest_record/9460fdf8-d61f-45f2-ac65-9b6c7ea35d97/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f776cd582ce20f3301009ea","keyword":["Drought","Growth","Southwestern Cutthroat Trout","Survival","USGS:5f776cd582ce20f3301009ea","biota"],"last_harvested_date":"2026-09-03T19:08:47.436081","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"influence-of-stream-temperature-on-eight-populations-of-rio-grande-cutthroat-trout-in-nort","spatial_centroid":{"lat":36.220299999999995,"lon":-106.06199999999998},"spatial_shape":{"coordinates":[[[-106.5454,35.9157],[-106.5454,36.6772],[-105.3369,36.6772],[-105.3369,35.9157],[-106.5454,35.9157]]],"type":"Polygon"},"theme":["geospatial"],"title":"Influence of Stream Temperature on Eight Populations of Rio Grande Cutthroat Trout in Northern New Mexico","type":"dataset"},{"_score":13.016239,"_sort":[1788462494849,13.016239,0,"3a27e67d-a255-4954-a852-5f363262b055"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Climate Adaptation Science Centers","hasEmail":"mailto:casc-data@usgs.gov"},"description":"The impacts of climate change on cold water species will likely manifest in populations at the trailing edge of their distribution. Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid southwestern U.S.A. streams at the southern-most edge of all cutthroat trout distributions; thus making RGCT particularly vulnerable to the anticipated warming and drying in this region. However, RGCT may possess a portfolio of life-history traits that aide in their persistence, attributes commonly observed in trailing edge populations. We used stream discharge (flow) data collected along a temperature and stream drying gradient to determine how these environmental constraints influence life-history trait expression (length- and age-at-maturity), demography, and extirpation risk in RGCT populations from northern New Mexico, U.S.A. Discharge (cubic meter/second) was collected throughout the eight populations across the three seasons (summer, fall, spring) for two years.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/kbmw-6z60","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.5f773c9182ce20f330100894.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f773c9182ce20f330100894","keyword":["Drought","Growth","Southwestern Cutthroat Trout","Survival","USGS:5f773c9182ce20f330100894","biota"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-106.5454, 35.9157, -105.3369, 36.6772","theme":["geospatial"],"title":"Influence of Stream Discharge on Eight Populations of Rio Grande Cutthroat Trout in Northern New Mexico"},"description":"The impacts of climate change on cold water species will likely manifest in populations at the trailing edge of their distribution. Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid southwestern U.S.A. streams at the southern-most edge of all cutthroat trout distributions; thus making RGCT particularly vulnerable to the anticipated warming and drying in this region. However, RGCT may possess a portfolio of life-history traits that aide in their persistence, attributes commonly observed in trailing edge populations. We used stream discharge (flow) data collected along a temperature and stream drying gradient to determine how these environmental constraints influence life-history trait expression (length- and age-at-maturity), demography, and extirpation risk in RGCT populations from northern New Mexico, U.S.A. Discharge (cubic meter/second) was collected throughout the eight populations across the three seasons (summer, fall, spring) for two years.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/bb515b6a-63f1-4293-a9d2-99f7bd7661b4","harvest_record_raw":"https://catalog.data.gov/harvest_record/bb515b6a-63f1-4293-a9d2-99f7bd7661b4/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f773c9182ce20f330100894","keyword":["Drought","Growth","Southwestern Cutthroat Trout","Survival","USGS:5f773c9182ce20f330100894","biota"],"last_harvested_date":"2026-09-03T19:08:14.849569","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"influence-of-stream-discharge-on-eight-populations-of-rio-grande-cutthroat-trout-in-northe","spatial_centroid":{"lat":36.220299999999995,"lon":-106.06199999999998},"spatial_shape":{"coordinates":[[[-106.5454,35.9157],[-106.5454,36.6772],[-105.3369,36.6772],[-105.3369,35.9157],[-106.5454,35.9157]]],"type":"Polygon"},"theme":["geospatial"],"title":"Influence of Stream Discharge on Eight Populations of Rio Grande Cutthroat Trout in Northern New Mexico","type":"dataset"},{"_score":14.71401,"_sort":[1788462272126,14.71401,0,"5aeeea19-80fb-4702-a904-a5c738ac17da"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Climate Adaptation Science Centers","hasEmail":"mailto:casc-data@usgs.gov"},"description":"The impacts of climate change on cold water species will likely manifest in populations at the trailing edge of their distribution. Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid southwestern U.S.A. streams at the southern-most edge of all cutthroat trout distributions; thus making RGCT particularly vulnerable to the anticipated warming and drying in this region. However, RGCT may possess a portfolio of life-history traits that aide in their persistence, attributes commonly observed in trailing edge populations. We used otolith and multistate capture-mark-recapture data collected along a temperature and stream drying gradient to determine how these environmental constraints influence life-history trait expression (length- and age-at-maturity), demography, and extirpation risk in RGCT populations from northern New Mexico, U.S.A. We found the rate at which RGCT reached maturity was highest at warm to intermediate stream temperatures, which was the demographic trait most strongly linked to RGCT persistence. Interestingly, older life-stages contributed more to population growth as temperatures decreased, providing further evidence of strong temperature effects controlling life-history trait expression in RGCT. Precipitation, however, had little effect on RGCT population dynamics and was likely influenced by the uncharacteristically wet years (2016-2017) during this study. Regardless, our results suggest that RGCT persistence depends on temperature-influenced diversity in life-history trait expression (e.g., longevity, age-at-maturity). Plasticity in trait expression is an important life-history characteristic of coldwater species, and may be vital for trailing edge populations to persist in a changing climate.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/kbmw-6z60","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.5f776c6c82ce20f3301009e6.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f776c6c82ce20f3301009e6","keyword":["Drought","Growth","Southwestern Cutthroat Trout","Survival","USGS:5f776c6c82ce20f3301009e6","biota"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-106.5454, 35.9157, -105.3369, 36.6772","theme":["geospatial"],"title":"Influences of Water Quality on Eight Populations of Rio Grande Cutthroat Trout in Northern New Mexico"},"description":"The impacts of climate change on cold water species will likely manifest in populations at the trailing edge of their distribution. Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid southwestern U.S.A. streams at the southern-most edge of all cutthroat trout distributions; thus making RGCT particularly vulnerable to the anticipated warming and drying in this region. However, RGCT may possess a portfolio of life-history traits that aide in their persistence, attributes commonly observed in trailing edge populations. We used otolith and multistate capture-mark-recapture data collected along a temperature and stream drying gradient to determine how these environmental constraints influence life-history trait expression (length- and age-at-maturity), demography, and extirpation risk in RGCT populations from northern New Mexico, U.S.A. We found the rate at which RGCT reached maturity was highest at warm to intermediate stream temperatures, which was the demographic trait most strongly linked to RGCT persistence. Interestingly, older life-stages contributed more to population growth as temperatures decreased, providing further evidence of strong temperature effects controlling life-history trait expression in RGCT. Precipitation, however, had little effect on RGCT population dynamics and was likely influenced by the uncharacteristically wet years (2016-2017) during this study. Regardless, our results suggest that RGCT persistence depends on temperature-influenced diversity in life-history trait expression (e.g., longevity, age-at-maturity). 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The action increases species persistence by increasing spatial redundancy, but it could also be used to supplement extant populations. We released 34 Eleutherodactylus coqui to determine initial, post-release survival under two treatments \u2013 non-translocated (N = 14), and translocated (N=20) to a different location 0.8 km away, but sharing similar habitat and environmental conditions. We defined \u201cinitial\u201d as the first 17 days post-release, a period where we hypothesized that translocated individuals would have lower survival rates because they transition from known-familiar habitat to novel-unfamiliar habitat. Daily survival rates (DSR) were better explained by a model with constant survival and no treatment effect (DSR = 0.999 \u00b1 0.001). The best supported model (AICc \u2264 2) indicated that temperature where frogs perched when captured (in-situ), negatively influenced daily survival, but the effect was weak (95%CIs overlapped 0). All but one of the frogs recaptured gained weight after 17 days post-release (average gain = 0.28 \u00b1 0.13 g), suggesting that transmitter/harness setup did not affect foraging behavior. The average daily distance travelled per individual was 0.76 \u00b1 0.22 m, being significantly higher for translocated individuals (1.19 \u00b1 0.35 m). Findings suggested that managed translocations have the potential to become a useful conservation tool, but challenges remain before it can be considered an integral part of post-translocation monitoring, particularly those Eleutherodactylus species with lower body mass.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P97LZMD9","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.64f22de0d34e09595517191e.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_64f22de0d34e09595517191e","keyword":["Adaptation Strategy","Climate Change","Coqui","Eleutherodactylus","Managed Translocations","Puerto Rico","Survival","Telemetry","USGS:64f22de0d34e09595517191e","West-central Puerto Rico","biota"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-66.978789, 18.140291, -66.972018, 18.143500","theme":["geospatial"],"title":"Post-release survival of translocated Eleutherodactylus coqui in Puerto Rico"},"description":"Translocating individuals of Eleutherodactylus frogs to alternative, suitable habitat is an adaptation strategy designed to minimize the impact of projected warming and drying in Puerto Rico. The action increases species persistence by increasing spatial redundancy, but it could also be used to supplement extant populations. We released 34 Eleutherodactylus coqui to determine initial, post-release survival under two treatments \u2013 non-translocated (N = 14), and translocated (N=20) to a different location 0.8 km away, but sharing similar habitat and environmental conditions. We defined \u201cinitial\u201d as the first 17 days post-release, a period where we hypothesized that translocated individuals would have lower survival rates because they transition from known-familiar habitat to novel-unfamiliar habitat. Daily survival rates (DSR) were better explained by a model with constant survival and no treatment effect (DSR = 0.999 \u00b1 0.001). The best supported model (AICc \u2264 2) indicated that temperature where frogs perched when captured (in-situ), negatively influenced daily survival, but the effect was weak (95%CIs overlapped 0). All but one of the frogs recaptured gained weight after 17 days post-release (average gain = 0.28 \u00b1 0.13 g), suggesting that transmitter/harness setup did not affect foraging behavior. The average daily distance travelled per individual was 0.76 \u00b1 0.22 m, being significantly higher for translocated individuals (1.19 \u00b1 0.35 m). 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All indices were computed using meteorological data from the 20km dynamically downscaled ERA-Interim reanalysis.The fire weather indices here are:\n-Evaporative Demand Drought Index (EDDI) for 1, 15, 52, and 90-day -Standardized Precipitation Evapotranspiration Index (SPEI) for 1, 15, 52, and 90-day \n-Reference Evapotranspiration (et0) used to compute EDDI \n-Precipitation minus et0 used to compute SPEI \n-Vapor Pressure Deficit (VPD) \n-Growing Season Index (GSI) for daily and 21-day moving average","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1428CGK","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.da3da7ae-dde3-4829-b087-d3cbe95de8bd.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_da3da7ae-dde3-4829-b087-d3cbe95de8bd","keyword":["USGS:da3da7ae-dde3-4829-b087-d3cbe95de8bd","climate change","environment","external research support","fire","historical","weather","wildland"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-179.9900, 50.1500, -129.7200, 72.2000","theme":["geospatial"],"title":"Daily historical fire weather indices averaged over 21 Alaska Predictive Service Areas (PSAs) at elevations at or below 600m for 1979-2017"},"description":"This database contains the daily historical fire weather indices averaged over 21 Alaska Predictive Service Areas (PSAs) at elevations at or below 600m for 1979-2017. All indices were computed using meteorological data from the 20km dynamically downscaled ERA-Interim reanalysis.The fire weather indices here are:\n-Evaporative Demand Drought Index (EDDI) for 1, 15, 52, and 90-day -Standardized Precipitation Evapotranspiration Index (SPEI) for 1, 15, 52, and 90-day \n-Reference Evapotranspiration (et0) used to compute EDDI \n-Precipitation minus et0 used to compute SPEI \n-Vapor Pressure Deficit (VPD) \n-Growing Season Index (GSI) for daily and 21-day moving average","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/dc4f8e4d-11a1-45a2-b09b-5a1534b73358","harvest_record_raw":"https://catalog.data.gov/harvest_record/dc4f8e4d-11a1-45a2-b09b-5a1534b73358/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_da3da7ae-dde3-4829-b087-d3cbe95de8bd","keyword":["USGS:da3da7ae-dde3-4829-b087-d3cbe95de8bd","climate change","environment","external research support","fire","historical","weather","wildland"],"last_harvested_date":"2026-09-03T18:56:47.890676","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"daily-historical-fire-weather-indices-averaged-over-21-alaska-predictive-service-1979-2017","spatial_centroid":{"lat":58.970000000000006,"lon":-159.882},"spatial_shape":{"coordinates":[[[-179.99,50.15],[-179.99,72.2],[-129.72,72.2],[-129.72,50.15],[-179.99,50.15]]],"type":"Polygon"},"theme":["geospatial"],"title":"Daily historical fire weather indices averaged over 21 Alaska Predictive Service Areas (PSAs) at elevations at or below 600m for 1979-2017","type":"dataset"},{"_score":29.936298,"_sort":[1788461294472,29.936298,3,"1d3044bc-eea0-4b75-bc76-21f0cac5a5de"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Climate Adaptation Science Centers","hasEmail":"mailto:casc-data@usgs.gov"},"description":"The CoRE (Contractions or Range Expansions) database contains a library of published literature and data on species range shifts in response to climate change. Through a systematic review of publications returned from searches on Google Scholar, Web of Science, and Scopus, we selected primary research articles that documented or attempted to document species-level distribution shifts in animal or plant species in response to recent anthropogenic climate change. We extracted data in four broad categories: (i) basic study information (study duration, location, data quality and methodological factors); (ii) basic species information (scientific names and taxonomic groups); (iii) information on the observed range shifts (range dimension, occupancy or abundance shift, and range edge); and (iv) the description of the shift (range shift direction, magnitude of the shift, and whether it supported our hypotheses). We also took note of climate drivers mentioned and details on species vulnerability and adaptive capacity.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P99VP2TW","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.64147d6fd34eb496d1ceb497.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_64147d6fd34eb496d1ceb497","keyword":["Africa","Arctic Ocean","Asia","Atlantic Ocean","Australia","Europe","Global","North America","Pacific Ocean","South America","USGS:64147d6fd34eb496d1ceb497","biodiversity","boundaries","climate change","depth","distribution","distribution shift","effects of climate change","elevation","global change","global warming","habitat extent","latitude","occupancy","precipitation","range","species","species redistribution","temperature","vulnerability","warming"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-180.0000, -90.0000, 180.0000, 90.0000","theme":["geospatial"],"title":"CoRE (Contractions or Range Expansions) Database: Global Database of Species Range Shifts from 1802-2019"},"description":"The CoRE (Contractions or Range Expansions) database contains a library of published literature and data on species range shifts in response to climate change. Through a systematic review of publications returned from searches on Google Scholar, Web of Science, and Scopus, we selected primary research articles that documented or attempted to document species-level distribution shifts in animal or plant species in response to recent anthropogenic climate change. We extracted data in four broad categories: (i) basic study information (study duration, location, data quality and methodological factors); (ii) basic species information (scientific names and taxonomic groups); (iii) information on the observed range shifts (range dimension, occupancy or abundance shift, and range edge); and (iv) the description of the shift (range shift direction, magnitude of the shift, and whether it supported our hypotheses). We also took note of climate drivers mentioned and details on species vulnerability and adaptive capacity.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/27bb89f0-e6c5-4b74-b83d-952cecc47a0a","harvest_record_raw":"https://catalog.data.gov/harvest_record/27bb89f0-e6c5-4b74-b83d-952cecc47a0a/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_64147d6fd34eb496d1ceb497","keyword":["Africa","Arctic Ocean","Asia","Atlantic Ocean","Australia","Europe","Global","North America","Pacific Ocean","South America","USGS:64147d6fd34eb496d1ceb497","biodiversity","boundaries","climate change","depth","distribution","distribution shift","effects of climate change","elevation","global change","global warming","habitat extent","latitude","occupancy","precipitation","range","species","species redistribution","temperature","vulnerability","warming"],"last_harvested_date":"2026-09-03T18:48:14.472744","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":3,"publisher":"U.S. Geological Survey","slug":"core-contractions-or-range-expansions-database-global-database-of-species-range--1802-2019","spatial_centroid":{"lat":-18.0,"lon":-36.0},"spatial_shape":{"coordinates":[[[-180.0,-90.0],[-180.0,90.0],[180.0,90.0],[180.0,-90.0],[-180.0,-90.0]]],"type":"Polygon"},"theme":["geospatial"],"title":"CoRE (Contractions or Range Expansions) Database: Global Database of Species Range Shifts from 1802-2019","type":"dataset"},{"_score":14.655771,"_sort":[1788460971786,14.655771,0,"0c94e6d6-8ef2-42fb-b5be-2a3837088248"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Climate Adaptation Science Centers","hasEmail":"mailto:casc-data@usgs.gov"},"description":"The impacts of climate change on cold water species will likely manifest in populations at the trailing edge of their distribution. Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid southwestern U.S.A. streams at the southern-most edge of all cutthroat trout distributions; thus making RGCT particularly vulnerable to the anticipated warming and drying in this region. However, RGCT may possess a portfolio of life-history traits that aide in their persistence, attributes commonly observed in trailing edge populations. We used otolith and multistate capture-mark-recapture data collected along a temperature and stream drying gradient to determine how these environmental constraints influence life-history trait expression (length- and age-at-maturity), demography, and extirpation risk in RGCT populations from northern New Mexico, U.S.A. We found the rate at which RGCT reached maturity was highest at warm to intermediate stream temperatures, which was the demographic trait most strongly linked to RGCT persistence. Interestingly, older life-stages contributed more to population growth as temperatures decreased, providing further evidence of strong temperature effects controlling life-history trait expression in RGCT. Precipitation, however, had little effect on RGCT population dynamics and was likely influenced by the uncharacteristically wet years (2016-2017) during this study. Regardless, our results suggest that RGCT persistence depends on temperature-influenced diversity in life-history trait expression (e.g., longevity, age-at-maturity). Plasticity in trait expression is an important life-history characteristic of coldwater species, and may be vital for trailing edge populations to persist in a changing climate.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/kbmw-6z60","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.5f773fd782ce20f3301008ad.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f773fd782ce20f3301008ad","keyword":["Drought","Growth","Southwestern Cutthroat Trout","Survival","USGS:5f773fd782ce20f3301008ad","biota"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-106.5454, 35.9157, -105.3369, 36.6772","theme":["geospatial"],"title":"Influences of Water Chemistry on Eight Populations of Rio Grande Cutthroat Trout in Northern New Mexico"},"description":"The impacts of climate change on cold water species will likely manifest in populations at the trailing edge of their distribution. Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid southwestern U.S.A. streams at the southern-most edge of all cutthroat trout distributions; thus making RGCT particularly vulnerable to the anticipated warming and drying in this region. However, RGCT may possess a portfolio of life-history traits that aide in their persistence, attributes commonly observed in trailing edge populations. We used otolith and multistate capture-mark-recapture data collected along a temperature and stream drying gradient to determine how these environmental constraints influence life-history trait expression (length- and age-at-maturity), demography, and extirpation risk in RGCT populations from northern New Mexico, U.S.A. We found the rate at which RGCT reached maturity was highest at warm to intermediate stream temperatures, which was the demographic trait most strongly linked to RGCT persistence. Interestingly, older life-stages contributed more to population growth as temperatures decreased, providing further evidence of strong temperature effects controlling life-history trait expression in RGCT. Precipitation, however, had little effect on RGCT population dynamics and was likely influenced by the uncharacteristically wet years (2016-2017) during this study. Regardless, our results suggest that RGCT persistence depends on temperature-influenced diversity in life-history trait expression (e.g., longevity, age-at-maturity). Plasticity in trait expression is an important life-history characteristic of coldwater species, and may be vital for trailing edge populations to persist in a changing climate.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/786ac359-5fad-4f3a-ab1c-83a9c56c52f2","harvest_record_raw":"https://catalog.data.gov/harvest_record/786ac359-5fad-4f3a-ab1c-83a9c56c52f2/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f773fd782ce20f3301008ad","keyword":["Drought","Growth","Southwestern Cutthroat Trout","Survival","USGS:5f773fd782ce20f3301008ad","biota"],"last_harvested_date":"2026-09-03T18:42:51.786286","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"influences-of-water-chemistry-on-eight-populations-of-rio-grande-cutthroat-trout-in-northe","spatial_centroid":{"lat":36.220299999999995,"lon":-106.06199999999998},"spatial_shape":{"coordinates":[[[-106.5454,35.9157],[-106.5454,36.6772],[-105.3369,36.6772],[-105.3369,35.9157],[-106.5454,35.9157]]],"type":"Polygon"},"theme":["geospatial"],"title":"Influences of Water Chemistry on Eight Populations of Rio Grande Cutthroat Trout in Northern New Mexico","type":"dataset"},{"_score":23.650677,"_sort":[1788460599451,23.650677,0,"4058c750-ea06-4308-bb43-903202d36d12"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Climate Adaptation Science Centers","hasEmail":"mailto:casc-data@usgs.gov"},"description":"Climate change influences apex predators in complex ways, due to their important trophic position, capacity for resource plasticity, and sensitivity to numerous anthropogenic stressors. Bald eagles, an ecologically and culturally significant apex predator, congregate seasonally in high densities on salmon spawning rivers across the Pacific Northwest. One of the largest eagle concentrations is in the Skagit River watershed, which connects the montane wilderness of North Cascades National Park to the Puget Sound. Using multiple long-term datasets, we evaluated the relationship between local bald eagle abundance, chum and coho salmon availability and phenology, and the number and timing of flood events in the Skagit River. We analyzed both changes over time as a reflection of climate change impacts, as well as differences between managed and unmanaged portions of the river. We found that peaks in chum salmon and bald eagle presence have advanced at remarkably similar rates (~0.45 days/year), suggesting synchronous phenological responses within this trophic relationship.Yet the temporal relationship between chum salmon spawning and flood events, which remove salmon carcasses from the system, has not remained constant. This has resulted in a paradigm shift whereby the peak of chum spawning now occurs before the first flood event of the season rather than after. The interval between peak chum and first flood event was a significant predictor of bald eagle presence: as this interval grew over time (by nearly a day per year), bald eagle counts declined, with a steady decrease in bald eagle observations since 2002. River section was also an important factor, with fewer flood events and more eagle observations occurring in the river section experiencing direct hydroelectric flow management.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P91VEXEW","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.5b7daa78e4b045b1dc7beb95.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5b7daa78e4b045b1dc7beb95","keyword":["North Cascades National Park","Skagit River","USGS:5b7daa78e4b045b1dc7beb95","bald eagle","biota","climate change","flood","hydroelectric","phenology","salmon","trophic interaction"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-127.0020, 45.5410, -116.9824, 49.7990","theme":["geospatial"],"title":"Biological and Hydrological Data from the Skagit River Ecosystem (Washington, USA) 1968-2016"},"description":"Climate change influences apex predators in complex ways, due to their important trophic position, capacity for resource plasticity, and sensitivity to numerous anthropogenic stressors. 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We found that peaks in chum salmon and bald eagle presence have advanced at remarkably similar rates (~0.45 days/year), suggesting synchronous phenological responses within this trophic relationship.Yet the temporal relationship between chum salmon spawning and flood events, which remove salmon carcasses from the system, has not remained constant. This has resulted in a paradigm shift whereby the peak of chum spawning now occurs before the first flood event of the season rather than after. The interval between peak chum and first flood event was a significant predictor of bald eagle presence: as this interval grew over time (by nearly a day per year), bald eagle counts declined, with a steady decrease in bald eagle observations since 2002. River section was also an important factor, with fewer flood events and more eagle observations occurring in the river section experiencing direct hydroelectric flow management.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/2dbd1f3a-3c96-4906-bce5-f7cb0d14b3ce","harvest_record_raw":"https://catalog.data.gov/harvest_record/2dbd1f3a-3c96-4906-bce5-f7cb0d14b3ce/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5b7daa78e4b045b1dc7beb95","keyword":["North Cascades National Park","Skagit River","USGS:5b7daa78e4b045b1dc7beb95","bald eagle","biota","climate change","flood","hydroelectric","phenology","salmon","trophic interaction"],"last_harvested_date":"2026-09-03T18:36:39.451374","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"biological-and-hydrological-data-from-the-skagit-river-ecosystem-washington-usa-1968-2016","spatial_centroid":{"lat":47.2442,"lon":-122.99416},"spatial_shape":{"coordinates":[[[-127.002,45.541],[-127.002,49.799],[-116.9824,49.799],[-116.9824,45.541],[-127.002,45.541]]],"type":"Polygon"},"theme":["geospatial"],"title":"Biological and Hydrological Data from the Skagit River Ecosystem (Washington, USA) 1968-2016","type":"dataset"},{"_score":39.021362,"_sort":[1788460377052,39.021362,0,"685ebf21-e0fd-4f74-b9fb-33c4d2e94c64"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Climate Adaptation Science Centers","hasEmail":"mailto:casc-data@usgs.gov"},"description":"These data include interview scripts/protocols and notes and transcripts resulting from group and individual interviews with individuals involved with the generation and application of climate adaptation science across the southeastern US. 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Text density and spacing also varies but, in total, this dataset includes over 65 pages of typed notes and transcriptions of data recorded from the interviews.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/a940-6y31","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.9b823210-dadd-44fd-9c26-3a3c985fb2e9.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_9b823210-dadd-44fd-9c26-3a3c985fb2e9","keyword":["Co-production","Evaluation","Interview","USGS:9b823210-dadd-44fd-9c26-3a3c985fb2e9","biota","social sciences","society"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-91.6300, 24.4800, -75.2000, 36.6400","theme":["geospatial"],"title":"Interview Data Collected from 2019 to 2021 for the Development of a Survey to Measure Use of Climate Adaptation Science by Management Partners in the Southeastern US"},"description":"These data include interview scripts/protocols and notes and transcripts resulting from group and individual interviews with individuals involved with the generation and application of climate adaptation science across the southeastern US. 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Understanding the mechanisms behind their responses is essential to formulate adaptation strategies for their conservation.  Puerto Rico harbors 15 endemic Eleutherodactylus frogs considered vulnerable to extinction due to poor vagility and sensitivity to environmental variability.  Herein are reported the effects of four temperature treatments (15, 20, 25, and 30 degrees Centigrade) on metabolic rates associated with specific dynamic action (SDA) and standard metabolic rates (SMR) of four representative species of Eleutherodactylus employing a respirometer.  All species in either experiment increased their excretion of CO2 with increasing temperature.  CO2 excretion rates were higher immediately post-ingestion, subsiding to low levels by the third day (72 hours).  SMR excretion rates of E. juanariveroi and E. antillensis increased up to 20 degrees Centigrade and then curbed.  Rates of E. coqui increased linearly, whereas rates of E. wightmanae increased markedly from 20 to 25 degrees Centigrade, perishing at 30 degrees Centigrade.  E. antillensis, E. wightmanae and E. juanariveroi exhibited a change in metabolic rates between 20 degrees Centigrade and 25 degrees Centigrade, the same range where occupancy shifts from lower to higher probability for all species.  Climate projections suggest that species will be exposed to 2-3 additional hours during evenings at \u226525 degrees Centigrade below 300 m, and about 1 hour at 400-500 m.  Species occurring in low elevations (\u2264400 m) may have to compensate for the additional energy expenditure induced by increased exposure and adjust their evening time budget.  A continuing warming trend could begin to infringe on habitats of high elevation specialists like E. wightmanae.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P940RY1P","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.65009778d34ed30c2057f6e5.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_65009778d34ed30c2057f6e5","keyword":["Adaptation Strategy","Climate Change","Coqu\u00ed","Coqu\u00ed  llanero","Coqu\u00ed chur\u00ed","Coqu\u00ed melodioso","Eleutherodactylus","Eleutherodactylus antillensis","Eleutherodactylus coqui","Eleutherodactylus juanariveroi","Eleutherodactylus wightmanae","Physiology","Puerto Rico","Specific Dynamic Action","Standard Metabolic Rate","USGS:65009778d34ed30c2057f6e5","West-Central Puerto Rico","biota"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-67.00094, 18.14352, -66.97879, 18.21016","theme":["geospatial"],"title":"Physiological Analysis of Eleutherodactylus Specimens in West-Central Puerto Rico, 2021-2022"},"description":"Amphibians are vulnerable to extinction owing, partly, to altered physiological processes induced by projected global warming and drying.  Understanding the mechanisms behind their responses is essential to formulate adaptation strategies for their conservation.  Puerto Rico harbors 15 endemic Eleutherodactylus frogs considered vulnerable to extinction due to poor vagility and sensitivity to environmental variability.  Herein are reported the effects of four temperature treatments (15, 20, 25, and 30 degrees Centigrade) on metabolic rates associated with specific dynamic action (SDA) and standard metabolic rates (SMR) of four representative species of Eleutherodactylus employing a respirometer.  All species in either experiment increased their excretion of CO2 with increasing temperature.  CO2 excretion rates were higher immediately post-ingestion, subsiding to low levels by the third day (72 hours).  SMR excretion rates of E. juanariveroi and E. antillensis increased up to 20 degrees Centigrade and then curbed.  Rates of E. coqui increased linearly, whereas rates of E. wightmanae increased markedly from 20 to 25 degrees Centigrade, perishing at 30 degrees Centigrade.  E. antillensis, E. wightmanae and E. juanariveroi exhibited a change in metabolic rates between 20 degrees Centigrade and 25 degrees Centigrade, the same range where occupancy shifts from lower to higher probability for all species.  Climate projections suggest that species will be exposed to 2-3 additional hours during evenings at \u226525 degrees Centigrade below 300 m, and about 1 hour at 400-500 m.  Species occurring in low elevations (\u2264400 m) may have to compensate for the additional energy expenditure induced by increased exposure and adjust their evening time budget.  A continuing warming trend could begin to infringe on habitats of high elevation specialists like E. wightmanae.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/6e2461d1-e1b5-4f77-8106-1d929920fea1","harvest_record_raw":"https://catalog.data.gov/harvest_record/6e2461d1-e1b5-4f77-8106-1d929920fea1/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_65009778d34ed30c2057f6e5","keyword":["Adaptation Strategy","Climate Change","Coqu\u00ed","Coqu\u00ed  llanero","Coqu\u00ed chur\u00ed","Coqu\u00ed melodioso","Eleutherodactylus","Eleutherodactylus antillensis","Eleutherodactylus coqui","Eleutherodactylus juanariveroi","Eleutherodactylus wightmanae","Physiology","Puerto Rico","Specific Dynamic Action","Standard Metabolic Rate","USGS:65009778d34ed30c2057f6e5","West-Central Puerto Rico","biota"],"last_harvested_date":"2026-09-03T18:24:31.406606","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":3,"publisher":"U.S. Geological Survey","slug":"physiological-analysis-of-eleutherodactylus-specimens-in-west-central-puerto-ric-2021-2022","spatial_centroid":{"lat":18.170175999999998,"lon":-66.99208},"spatial_shape":{"coordinates":[[[-67.00094,18.14352],[-67.00094,18.21016],[-66.97879,18.21016],[-66.97879,18.14352],[-67.00094,18.14352]]],"type":"Polygon"},"theme":["geospatial"],"title":"Physiological Analysis of Eleutherodactylus Specimens in West-Central Puerto Rico, 2021-2022","type":"dataset"},{"_score":41.351837,"_sort":[1788459658996,41.351837,0,"6add1eb4-67bd-456e-a413-24c1fc6dea84"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Diana Zamora-Reyes","hasEmail":"mailto:dzamora-reyes@usgs.gov"},"description":"This data release includes the downscaled climate inputs from twenty-four Weather Generator Scenarios and hydrologic outputs using the Basin Characterization Model (BCM) version 8 (v8), described in Flint and others (2021a), with a 270 by 270-meter spatial resolution at a monthly time steps from water years 1916 to 2018 for the Santa Ana River watershed. \nThe twenty-four Weather Generator Scenarios are defined as: \nScenario 1: Baseline\nScenario 2: -25% average (ave) precipitation (ppt), +2 degrees Celsius (C)\nScenario 3: -25% ave ppt, +3C\nScenario 4: -25% ave ppt, +4C\nScenario 5: -25% ave ppt, +5C\nScenario 6: -12% ave ppt, +1C\nScenario 7: -12% ave ppt, +2C\nScenario 8: -12% ave ppt, +3C\nScenario 9: -12% ave ppt, +4C\nScenario 10: -12% ave ppt, +5C \nScenario 11: 0% ave ppt, +1C\nScenario 12: 0% ave ppt, +2C\nScenario 13: 0% ave ppt, +3C\nScenario 14: 0% ave ppt, +4C\nScenario 15: 0% ave ppt, +5C\nScenario 16: +12% ave ppt, +1C\nScenario 17: +12% ave ppt, +2C\nScenario 18: +12% ave ppt, +3C\nScenario 19: +12% ave ppt, +4C\nScenario 20: +12% ave ppt, +5C\nScenario 21: +25% ave ppt, +2C\nScenario 22: +25% ave ppt, +3C\nScenario 23: +25% ave ppt, +4C\nScenario 24: +25% ave ppt, +5C\nThis data release provides outputs from twenty-four Weather Generator Scenarios. For each scenario, three datasets are included: (1) monthly climate variables, (2) hydrology BCM variables, and (3) water-year summaries (72 datasets in total). The monthly climate variables child items contain precipitation (PPT), maximum air temperature (TMX), minimum air temperature (TMN), and potential evapotranspiration (PET). The monthly hydrology BCM variables child items contain actual evapotranspiration (AET), climatic water deficit (CWD), snowpack or snow water equivalent (PCK), recharge (RCH), runoff (RUN), and soil moisture storage (STR). The water-year summaries child items contain annual average summaries of each of the monthly climate and monthly hydrology BCM variables for each scenario.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1QGH9FX","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.6a0657fcb66b01f7f6adc53a.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6a0657fcb66b01f7f6adc53a","keyword":["Atmospheric and Climatic Processes","California","Evaporation","Geospatial Datasets","Hydrology","Mathematical Modeling","Modeling","Permeability","Precipitation (atmospheric)","Santa Ana","Snow and Ice Cover","Soil Moisture","Streamflow","Surface Water (non-marine)","Transpiration","USGS:6a0657fcb66b01f7f6adc53a","United States","Water Budget","Water Cycle","Water Resources","Watershed Management","climatologyMeteorologyAtmosphere","elevation","geoscientificInformation"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-118.0003, 33.5547, -116.5584, 34.3869","theme":["geospatial"],"title":"Santa Ana River 270-meter Basin Characterization Model using Weather Generator Scenarios - Monthly Climate and Hydrology"},"description":"This data release includes the downscaled climate inputs from twenty-four Weather Generator Scenarios and hydrologic outputs using the Basin Characterization Model (BCM) version 8 (v8), described in Flint and others (2021a), with a 270 by 270-meter spatial resolution at a monthly time steps from water years 1916 to 2018 for the Santa Ana River watershed. \nThe twenty-four Weather Generator Scenarios are defined as: \nScenario 1: Baseline\nScenario 2: -25% average (ave) precipitation (ppt), +2 degrees Celsius (C)\nScenario 3: -25% ave ppt, +3C\nScenario 4: -25% ave ppt, +4C\nScenario 5: -25% ave ppt, +5C\nScenario 6: -12% ave ppt, +1C\nScenario 7: -12% ave ppt, +2C\nScenario 8: -12% ave ppt, +3C\nScenario 9: -12% ave ppt, +4C\nScenario 10: -12% ave ppt, +5C \nScenario 11: 0% ave ppt, +1C\nScenario 12: 0% ave ppt, +2C\nScenario 13: 0% ave ppt, +3C\nScenario 14: 0% ave ppt, +4C\nScenario 15: 0% ave ppt, +5C\nScenario 16: +12% ave ppt, +1C\nScenario 17: +12% ave ppt, +2C\nScenario 18: +12% ave ppt, +3C\nScenario 19: +12% ave ppt, +4C\nScenario 20: +12% ave ppt, +5C\nScenario 21: +25% ave ppt, +2C\nScenario 22: +25% ave ppt, +3C\nScenario 23: +25% ave ppt, +4C\nScenario 24: +25% ave ppt, +5C\nThis data release provides outputs from twenty-four Weather Generator Scenarios. For each scenario, three datasets are included: (1) monthly climate variables, (2) hydrology BCM variables, and (3) water-year summaries (72 datasets in total). The monthly climate variables child items contain precipitation (PPT), maximum air temperature (TMX), minimum air temperature (TMN), and potential evapotranspiration (PET). The monthly hydrology BCM variables child items contain actual evapotranspiration (AET), climatic water deficit (CWD), snowpack or snow water equivalent (PCK), recharge (RCH), runoff (RUN), and soil moisture storage (STR). The water-year summaries child items contain annual average summaries of each of the monthly climate and monthly hydrology BCM variables for each scenario.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/17c96fc8-05e5-43e8-8d6a-2702bb1f26b4","harvest_record_raw":"https://catalog.data.gov/harvest_record/17c96fc8-05e5-43e8-8d6a-2702bb1f26b4/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6a0657fcb66b01f7f6adc53a","keyword":["Atmospheric and Climatic Processes","California","Evaporation","Geospatial Datasets","Hydrology","Mathematical Modeling","Modeling","Permeability","Precipitation (atmospheric)","Santa Ana","Snow and Ice Cover","Soil Moisture","Streamflow","Surface Water (non-marine)","Transpiration","USGS:6a0657fcb66b01f7f6adc53a","United States","Water Budget","Water Cycle","Water Resources","Watershed Management","climatologyMeteorologyAtmosphere","elevation","geoscientificInformation"],"last_harvested_date":"2026-09-03T18:20:58.996966","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"santa-ana-river-270-meter-basin-characterization-model-using-weather-generator-scenarios-m","spatial_centroid":{"lat":33.88758,"lon":-117.42354},"spatial_shape":{"coordinates":[[[-118.0003,33.5547],[-118.0003,34.3869],[-116.5584,34.3869],[-116.5584,33.5547],[-118.0003,33.5547]]],"type":"Polygon"},"theme":["geospatial"],"title":"Santa Ana River 270-meter Basin Characterization Model using Weather Generator Scenarios - Monthly Climate and Hydrology","type":"dataset"},{"_score":51.919643,"_sort":[1788459516126,51.919643,1,"963c0492-364a-44cb-8b67-3498117ca7cb"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Climate Adaptation Science Centers","hasEmail":"mailto:casc-data@usgs.gov"},"description":"Carbon sequestration and biodiversity are tightly linked, but many models projecting carbon storage change do not account for the role biodiversity plays in the sequestration capacity of terrestrial ecosystems. Here, we link a macroecological model projecting changes in vascular plant richness with empirical biodiversity-biomass stock relationships, to assess the consequences of plant biodiversity loss for carbon storage under multiple climate and land-use change scenarios. Data presented here include global raster files of plant species loss by ecoregion, biomass loss by ecoregion, and carbon loss by ecoregion. Estimates are what is expected over the long term, when ecosystems approach their new equilibrium states, based on climate and land-use changes projected for 2050.This data release is associated with the publication Biodiversity loss reduces global terrestrial carbon storage published in Nature Communications.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13WUFMU","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.65fc456dd34e64ff1548d31b.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_65fc456dd34e64ff1548d31b","keyword":["Biodiversity","Biodiversity-ecosystem functioning relationships","Climate change","Conservation","Scenario Planning","USGS:65fc456dd34e64ff1548d31b","economy","geospatial datasets"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-180.0000, -60.0000, 180.0000, 90.0000","theme":["geospatial"],"title":"Model outputs highlighting how biodiversity loss reduces global terrestrial carbon storage based on climate and land-use changes projected for 2050"},"description":"Carbon sequestration and biodiversity are tightly linked, but many models projecting carbon storage change do not account for the role biodiversity plays in the sequestration capacity of terrestrial ecosystems. Here, we link a macroecological model projecting changes in vascular plant richness with empirical biodiversity-biomass stock relationships, to assess the consequences of plant biodiversity loss for carbon storage under multiple climate and land-use change scenarios. Data presented here include global raster files of plant species loss by ecoregion, biomass loss by ecoregion, and carbon loss by ecoregion. Estimates are what is expected over the long term, when ecosystems approach their new equilibrium states, based on climate and land-use changes projected for 2050.This data release is associated with the publication Biodiversity loss reduces global terrestrial carbon storage published in Nature Communications.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/6a1f27f0-36d6-4fd6-ad35-3b9f189da074","harvest_record_raw":"https://catalog.data.gov/harvest_record/6a1f27f0-36d6-4fd6-ad35-3b9f189da074/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_65fc456dd34e64ff1548d31b","keyword":["Biodiversity","Biodiversity-ecosystem functioning relationships","Climate change","Conservation","Scenario Planning","USGS:65fc456dd34e64ff1548d31b","economy","geospatial datasets"],"last_harvested_date":"2026-09-03T18:18:36.126588","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"model-outputs-highlighting-how-biodiversity-loss-reduces-global-terrestrial-carbon-st-2050","spatial_centroid":{"lat":0.0,"lon":-36.0},"spatial_shape":{"coordinates":[[[-180.0,-60.0],[-180.0,90.0],[180.0,90.0],[180.0,-60.0],[-180.0,-60.0]]],"type":"Polygon"},"theme":["geospatial"],"title":"Model outputs highlighting how biodiversity loss reduces global terrestrial carbon storage based on climate and land-use changes projected for 2050","type":"dataset"},{"_score":55.30838,"_sort":[1788459309718,55.30838,0,"14e1dec9-54a3-41be-a015-1f667aae44eb"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Climate Adapation Science Centers","hasEmail":"mailto:casc-data@usgs.gov"},"description":"These datasets include the components of and results from the Rarity and Climate Sensitivity Index (RCS) and occurrence records used to calculate the index for 29 stream fishes native to the Pacific Northwest (Washington, Idaho, Oregon) of the United States. The RCS is an index that ranks species\u2019 intrinsic sensitivity to climate change based on their area of occurrence and climate niche breadth, the range of environmental conditions for a given species. The RCS uses point occurrences to calculate both metrics. We compiled point occurrences from a variety of sources. Final point occurrences were filtered for quality assurance and received expert review (details in occurrence dataset metadata). We calculated RCS metrics at two spatial grains: 1) Hydrologic Unit Code (HUC) level 12 watersheds and 2) 1 km buffered occurrence points and stream segment level. The area of occurrence for each species was calculated as the total watershed area of all HUC 12 watersheds containing any of the final set of occurrence points or the total area of 1 km buffer around the final set of occurrence points. We calculated climate niche breadth using two sets of environmental variables at both spatial grains, bioclimatic and stream level. Bioclimatic level was calculated by extracting annual mean precipitation, maximum temperature of the warmest month, and minimum temperature of the coldest month from the area of occurrence using the the Parameter-elevation Regressions on Independent Slopes Model (PRISM) dataset. Stream level was calculated by extracting mean August stream temperature, mean stream baseflow, and either predicted streamflow permanence probability or predicted streamflow permanence class from all streams within the watershed area of occurrence or the nearest stream to each point occurrence for the 1 km grain. Stream level data was extracted using compiled streamflow permanence, water temperature, and modeled streamflow data (Sando and Schultz, 2022). Climate niche breadth from both levels is calculated as the area-weighted standard deviation for each variable. The RCS is calculated from the scaled and combined species\u2019 area of occurrence and climate niche breadth, such that an intrinsically sensitive species has a small area of occurrence and a narrow niche breadth.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9WE05SV","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.64da96cbd34ef477cf3ee729.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_64da96cbd34ef477cf3ee729","keyword":["Idaho","North America","Oregon","Pacific Northwest","USGS:64da96cbd34ef477cf3ee729","United States","Washington","area of occupancy","biota","climate niche breadth","climatologyMeteorologyAtmosphere","community ecology","environment","fish","inlandWaters","multispecies study","rarity","vertebrates","vulnerability"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-124.9022, 41.1278, -109.7625, 52.8807","theme":["geospatial"],"title":"Occurrence, Rarity and Climate Sensitivity Index, and Components of 29 Fishes Native to the Pacific Northwest, USA"},"description":"These datasets include the components of and results from the Rarity and Climate Sensitivity Index (RCS) and occurrence records used to calculate the index for 29 stream fishes native to the Pacific Northwest (Washington, Idaho, Oregon) of the United States. The RCS is an index that ranks species\u2019 intrinsic sensitivity to climate change based on their area of occurrence and climate niche breadth, the range of environmental conditions for a given species. The RCS uses point occurrences to calculate both metrics. We compiled point occurrences from a variety of sources. Final point occurrences were filtered for quality assurance and received expert review (details in occurrence dataset metadata). We calculated RCS metrics at two spatial grains: 1) Hydrologic Unit Code (HUC) level 12 watersheds and 2) 1 km buffered occurrence points and stream segment level. The area of occurrence for each species was calculated as the total watershed area of all HUC 12 watersheds containing any of the final set of occurrence points or the total area of 1 km buffer around the final set of occurrence points. We calculated climate niche breadth using two sets of environmental variables at both spatial grains, bioclimatic and stream level. Bioclimatic level was calculated by extracting annual mean precipitation, maximum temperature of the warmest month, and minimum temperature of the coldest month from the area of occurrence using the the Parameter-elevation Regressions on Independent Slopes Model (PRISM) dataset. Stream level was calculated by extracting mean August stream temperature, mean stream baseflow, and either predicted streamflow permanence probability or predicted streamflow permanence class from all streams within the watershed area of occurrence or the nearest stream to each point occurrence for the 1 km grain. Stream level data was extracted using compiled streamflow permanence, water temperature, and modeled streamflow data (Sando and Schultz, 2022). Climate niche breadth from both levels is calculated as the area-weighted standard deviation for each variable. 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This dataset contains results from a simple process-based model, PC2FM, to derive projected analog fire regimes with respect to the potential fire probability concept. This concept is based on the potential energy and fuels available in the background environmental state under pre-industrial conditions for the coterminous US. To map climate-fire analog futures, three key relevant variables are used in addition to fire probability derived from PC2FM: annual temperature, annual precipitation, and precipitation seasonality. Projections of the climate-fire analogs are provided for 20 downscaled climate models under two climate forcing scenarios, Representative Concentration Pathways (RCP 4.5 and 8.5) for two time periods (2040-2069 and 2070-2099) and 655 protected areas in the conterminous U.S.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13FFXWV","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.65ccd3aed34ef4b119cb3b5a.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_65ccd3aed34ef4b119cb3b5a","keyword":["Analog mapping","Climate change adaptation","USGS:65ccd3aed34ef4b119cb3b5a","Wildland fire","climatologyMeteorologyAtmosphere","environment","fires","geoscientificInformation","geospatial datasets","protected areas"],"modified":"2026-07-24T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-126.7383, 23.4834, -66.0938, 50.0642","theme":["geospatial"],"title":"Climate-fire analog mapping to inform adaptive management strategies for wildland fire in protected areas of the conterminous U.S."},"description":"Managing and adapting to changing wildland fire regimes due to human-caused global warming can be facilitated through the use of analog mapping of potential climate-influenced outcomes. This dataset contains results from a simple process-based model, PC2FM, to derive projected analog fire regimes with respect to the potential fire probability concept. This concept is based on the potential energy and fuels available in the background environmental state under pre-industrial conditions for the coterminous US. To map climate-fire analog futures, three key relevant variables are used in addition to fire probability derived from PC2FM: annual temperature, annual precipitation, and precipitation seasonality. Projections of the climate-fire analogs are provided for 20 downscaled climate models under two climate forcing scenarios, Representative Concentration Pathways (RCP 4.5 and 8.5) for two time periods (2040-2069 and 2070-2099) and 655 protected areas in the conterminous U.S.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/100e8247-bb63-4732-a909-0c669fbf5b83","harvest_record_raw":"https://catalog.data.gov/harvest_record/100e8247-bb63-4732-a909-0c669fbf5b83/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_65ccd3aed34ef4b119cb3b5a","keyword":["Analog mapping","Climate change adaptation","USGS:65ccd3aed34ef4b119cb3b5a","Wildland fire","climatologyMeteorologyAtmosphere","environment","fires","geoscientificInformation","geospatial datasets","protected areas"],"last_harvested_date":"2026-09-03T18:14:12.600320","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":2,"publisher":"U.S. Geological Survey","slug":"climate-fire-analog-mapping-to-inform-adaptive-management-strategies-for-wildland-fire-in-","spatial_centroid":{"lat":34.115719999999996,"lon":-102.4805},"spatial_shape":{"coordinates":[[[-126.7383,23.4834],[-126.7383,50.0642],[-66.0938,50.0642],[-66.0938,23.4834],[-126.7383,23.4834]]],"type":"Polygon"},"theme":["geospatial"],"title":"Climate-fire analog mapping to inform adaptive management strategies for wildland fire in protected areas of the conterminous U.S.","type":"dataset"},{"_score":8.057535,"_sort":[1788458965528,8.057535,1,"7cabad6e-bd43-43a5-8765-f83a2f0ddbf0"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Kaitlyn Strickfaden","hasEmail":"mailto:kstrickfaden@uidaho.edu"},"description":"Snow conditions are changing dramatically in the mountains of the interior Pacific Northwest, including eastern Washington, northern Idaho, and western Montana. These changes can both benefit and hinder a variety of wildlife species. The timing and extent of seasonal snowpacks, in addition to snow depth, density, and hardness, can impact the ability of wildlife to access forage, their ability to move across the landscape, and their vulnerability to predators, to name a few. In order to respond effectively to changes in snow conditions, wildlife managers need tools to identify areas and promote conditions that maintain late spring and early summer snowpack for some sensitive species. Managers also require an index of winter severity that includes information on temperature, snow depth, and snow hardness at relevant spatial and temporal scales to adapt management strategies for seasonal conditions. \n \nThis project seeks to advance the understanding of how snow conditions vary and how such variation affects both species of greatest conservation need (e.g., wolverine, hoary marmot, western bumble bee, and mountain goat) and species of economic and recreational importance (e.g., elk and moose) in forests spanning the rain-snow transition zone in the interior Pacific Northwest. To do this, researchers created new tools that managers can use to estimate snow depth, map areas of late season snow (known as \u201csnow refugia\u201d), and estimate winter severity for ungulate species such as elk and moose. Researchers used these novel datasets to predict winter range habitat use by deer and elk in Idaho and to identify linkages between ungulate survival and winter severity. These data were used to create a model predicting snow disappearance dates (SDD) at camera sites and across our entire study area to identify priority areas of conservation for snow-dependent wildlife. The model predicted high-elevation areas, north-facing aspects, and cold-air pools retained snow latest. These data were also used to model the probability of deer presence at camera sites dependent on snow conditions, and it was determined that deer respond negatively to increased snow density and respond slightly positively to increased snow hardness.\n \nThe results of this project will be directly applicable to federal (U.S. Fish and Wildlife Service), state (Idaho Department of Fish and Game), and tribal (Coeur D\u2019Alene Tribe) managers in the region. Providing natural resource managers with tools to identify locations of snow retention for sensitive and listed species is critical for identifying habitats to conserve or modify in order to facilitate species recovery. Lastly, a winter severity model will provide wildlife managers with a much-needed tool for predicting future climate change effects on ungulates and adjusting management strategies accordingly.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/bma6-xn17","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.4bc80965-54cf-4bb0-a1f0-4c52a23769b0.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_4bc80965-54cf-4bb0-a1f0-4c52a23769b0","keyword":["Idaho","Latah","Moscow Mountain","USGS:4bc80965-54cf-4bb0-a1f0-4c52a23769b0","biota","external research support","snow and ice cover"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-116.86000, 46.78000, -116.83000, 46.82000","theme":["geospatial"],"title":"Estimating the Spatial and Temporal Extent of Snowpack Properties in Complex Terrain: Data Release"},"description":"Snow conditions are changing dramatically in the mountains of the interior Pacific Northwest, including eastern Washington, northern Idaho, and western Montana. These changes can both benefit and hinder a variety of wildlife species. The timing and extent of seasonal snowpacks, in addition to snow depth, density, and hardness, can impact the ability of wildlife to access forage, their ability to move across the landscape, and their vulnerability to predators, to name a few. In order to respond effectively to changes in snow conditions, wildlife managers need tools to identify areas and promote conditions that maintain late spring and early summer snowpack for some sensitive species. Managers also require an index of winter severity that includes information on temperature, snow depth, and snow hardness at relevant spatial and temporal scales to adapt management strategies for seasonal conditions. \n \nThis project seeks to advance the understanding of how snow conditions vary and how such variation affects both species of greatest conservation need (e.g., wolverine, hoary marmot, western bumble bee, and mountain goat) and species of economic and recreational importance (e.g., elk and moose) in forests spanning the rain-snow transition zone in the interior Pacific Northwest. To do this, researchers created new tools that managers can use to estimate snow depth, map areas of late season snow (known as \u201csnow refugia\u201d), and estimate winter severity for ungulate species such as elk and moose. Researchers used these novel datasets to predict winter range habitat use by deer and elk in Idaho and to identify linkages between ungulate survival and winter severity. These data were used to create a model predicting snow disappearance dates (SDD) at camera sites and across our entire study area to identify priority areas of conservation for snow-dependent wildlife. The model predicted high-elevation areas, north-facing aspects, and cold-air pools retained snow latest. These data were also used to model the probability of deer presence at camera sites dependent on snow conditions, and it was determined that deer respond negatively to increased snow density and respond slightly positively to increased snow hardness.\n \nThe results of this project will be directly applicable to federal (U.S. Fish and Wildlife Service), state (Idaho Department of Fish and Game), and tribal (Coeur D\u2019Alene Tribe) managers in the region. Providing natural resource managers with tools to identify locations of snow retention for sensitive and listed species is critical for identifying habitats to conserve or modify in order to facilitate species recovery. Lastly, a winter severity model will provide wildlife managers with a much-needed tool for predicting future climate change effects on ungulates and adjusting management strategies accordingly.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/7eb0363b-fe26-4482-803c-e9dc56fbfd26","harvest_record_raw":"https://catalog.data.gov/harvest_record/7eb0363b-fe26-4482-803c-e9dc56fbfd26/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_4bc80965-54cf-4bb0-a1f0-4c52a23769b0","keyword":["Idaho","Latah","Moscow Mountain","USGS:4bc80965-54cf-4bb0-a1f0-4c52a23769b0","biota","external research support","snow and ice cover"],"last_harvested_date":"2026-09-03T18:09:25.528650","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"estimating-the-spatial-and-temporal-extent-of-snowpack-properties-in-complex-terrain-data-","spatial_centroid":{"lat":46.79600000000001,"lon":-116.848},"spatial_shape":{"coordinates":[[[-116.86,46.78],[-116.86,46.82],[-116.83,46.82],[-116.83,46.78],[-116.86,46.78]]],"type":"Polygon"},"theme":["geospatial"],"title":"Estimating the Spatial and Temporal Extent of Snowpack Properties in Complex Terrain: Data Release","type":"dataset"},{"_score":14.655771,"_sort":[1788458730942,14.655771,0,"cf55ebca-f59b-44f8-aca4-9644dcead096"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Climate Adaptation Science Centers","hasEmail":"mailto:casc-data@usgs.gov"},"description":"The impacts of climate change on cold water species will likely manifest in populations at the trailing edge of their distribution. Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid southwestern U.S.A. streams at the southern-most edge of all cutthroat trout distributions; thus making RGCT particularly vulnerable to the anticipated warming and drying in this region. However, RGCT may possess a portfolio of life-history traits that aide in their persistence, attributes commonly observed in trailing edge populations. We used otolith and multistate capture-mark-recapture data collected along a temperature and stream drying gradient to determine how these environmental constraints influence life-history trait expression (length- and age-at-maturity), demography, and extirpation risk in RGCT populations from northern New Mexico, U.S.A. We found the rate at which RGCT reached maturity was highest at warm to intermediate stream temperatures, which was the demographic trait most strongly linked to RGCT persistence. Interestingly, older life-stages contributed more to population growth as temperatures decreased, providing further evidence of strong temperature effects controlling life-history trait expression in RGCT. Precipitation, however, had little effect on RGCT population dynamics and was likely influenced by the uncharacteristically wet years (2016-2017) during this study. Regardless, our results suggest that RGCT persistence depends on temperature-influenced diversity in life-history trait expression (e.g., longevity, age-at-maturity). Plasticity in trait expression is an important life-history characteristic of coldwater species, and may be vital for trailing edge populations to persist in a changing climate.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/kbmw-6z60","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.5f77376c82ce20f330100872.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f77376c82ce20f330100872","keyword":["Drought","Growth","Southwestern Cutthroat Trout","Survival","USGS:5f77376c82ce20f330100872","biota"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-106.6223, 35.9157, -105.2930, 36.8533","theme":["geospatial"],"title":"Fish Diets from Eight Populations of Rio Grande Cutthroat Trout in Northern New Mexico"},"description":"The impacts of climate change on cold water species will likely manifest in populations at the trailing edge of their distribution. Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid southwestern U.S.A. streams at the southern-most edge of all cutthroat trout distributions; thus making RGCT particularly vulnerable to the anticipated warming and drying in this region. However, RGCT may possess a portfolio of life-history traits that aide in their persistence, attributes commonly observed in trailing edge populations. We used otolith and multistate capture-mark-recapture data collected along a temperature and stream drying gradient to determine how these environmental constraints influence life-history trait expression (length- and age-at-maturity), demography, and extirpation risk in RGCT populations from northern New Mexico, U.S.A. We found the rate at which RGCT reached maturity was highest at warm to intermediate stream temperatures, which was the demographic trait most strongly linked to RGCT persistence. Interestingly, older life-stages contributed more to population growth as temperatures decreased, providing further evidence of strong temperature effects controlling life-history trait expression in RGCT. Precipitation, however, had little effect on RGCT population dynamics and was likely influenced by the uncharacteristically wet years (2016-2017) during this study. Regardless, our results suggest that RGCT persistence depends on temperature-influenced diversity in life-history trait expression (e.g., longevity, age-at-maturity). Plasticity in trait expression is an important life-history characteristic of coldwater species, and may be vital for trailing edge populations to persist in a changing climate.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/60ad2a7f-d83d-440d-a60a-1850dd609d76","harvest_record_raw":"https://catalog.data.gov/harvest_record/60ad2a7f-d83d-440d-a60a-1850dd609d76/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f77376c82ce20f330100872","keyword":["Drought","Growth","Southwestern Cutthroat Trout","Survival","USGS:5f77376c82ce20f330100872","biota"],"last_harvested_date":"2026-09-03T18:05:30.942415","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"fish-diets-from-eight-populations-of-rio-grande-cutthroat-trout-in-northern-new-mexico","spatial_centroid":{"lat":36.29074,"lon":-106.09058},"spatial_shape":{"coordinates":[[[-106.6223,35.9157],[-106.6223,36.8533],[-105.293,36.8533],[-105.293,35.9157],[-106.6223,35.9157]]],"type":"Polygon"},"theme":["geospatial"],"title":"Fish Diets from Eight Populations of Rio Grande Cutthroat Trout in Northern New Mexico","type":"dataset"},{"_score":12.001787,"_sort":[1788457910162,12.001787,0,"ab282729-720b-41e3-9dcb-b8730f0e967f"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Climate Adaptation Science Centers","hasEmail":"mailto:casc-data@usgs.gov"},"description":"The impacts of climate change on cold water species will likely manifest in populations at the trailing edge of their distribution. Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid southwestern U.S.A. streams at the southern-most edge of all cutthroat trout distributions; thus making RGCT particularly vulnerable to the anticipated warming and drying in this region. However, RGCT may possess a portfolio of life-history traits that aide in their persistence, attributes commonly observed in trailing edge populations. We tagged RGCT across eight populations in 2016 and 2017 and used this  capture-mark-recapture data to determine how environmental constraints influenced life-history trait expression (length- and age-at-maturity), demography, and extirpation risk in RGCT populations from northern New Mexico, U.S.A. We found the rate at which RGCT reached maturity was highest at warm to intermediate stream temperatures, which was the demographic trait most strongly linked to RGCT persistence. Interestingly, older life-stages contributed more to population growth as temperatures decreased, providing further evidence of strong temperature effects controlling life-history trait expression in RGCT.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/kbmw-6z60","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.5f7ccda382ce1d74e7db55ca.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f7ccda382ce1d74e7db55ca","keyword":["Drought","Growth","Southwestern Cutthroat Trout","Survival","USGS:5f7ccda382ce1d74e7db55ca","biota"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-106.6168, 35.9157, -105.2380, 36.9148","theme":["geospatial"],"title":"Fish Length, Weight, and Unique Identification from Eight Populations of Rio Grande Cutthroat Trout in Northern New Mexico"},"description":"The impacts of climate change on cold water species will likely manifest in populations at the trailing edge of their distribution. 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Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid southwestern U.S.A. streams at the southern-most edge of all cutthroat trout distributions; thus making RGCT particularly vulnerable to the anticipated warming and drying in this region. However, RGCT may possess a portfolio of life-history traits that aide in their persistence, attributes commonly observed in trailing edge populations. We marked  RGCT across eight populations in 2016 and 2017 and used this  capture-mark-recapture data to determine how environmental constraints influenced life-history trait expression (length- and age-at-maturity), demography, and extirpation risk in RGCT populations from northern New Mexico, U.S.A. We found the rate at which RGCT reached maturity was highest at warm to intermediate stream temperatures, which was the demographic trait most strongly linked to RGCT persistence. Interestingly, older life-stages contributed more to population growth as temperatures decreased, providing further evidence of strong temperature effects controlling life-history trait expression in RGCT. 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The ERC is a number related to the available energy (BTU) per unit area (square foot) within the flaming front at the head of a fire. The ERC is considered a composite fuel moisture index as it reflects the contribution of all live and dead fuels to potential fire intensity. As live fuels cure and dead fuels dry, the ERC will increase and can be described as a build-up index. The ERC has memory. Each daily calculation considers the past 7 days in calculating the new number. 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A function is provided for users to classify new mCP samples with the model.\n\nThe datasets consist of measurements of the UV-visible absorbance spectra (350 nm to 750 nm) of purified and unpurified m-cresol purple samples in sodium hydroxide and sodium chloride solutions at pH\n12 and an ionic strength of 0.7 mol/kg soln. The UV-visible absorbance measurements were collected on an Agilent Cary 100 spectrophotometer at NIST.  A second dataset is included consisting of \nsimilar measurements of various purified m-cresol purple samples collected on an Agilent 8453 spectrophotometer at MBARI. This dataset was used to test the performance of the SIMCA model on samples measured on a different spectrophotometer. 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Impurities in the indicator are known to absorb strongly at one of the wavelengths used in spectrophotometric pH determination and lead to large biases in the pH measurements.  This repository contains data and Matlab scripts to facilitate the implementation of a DD-SIMCA model for detecting residual impurities in purified m-cresol purple (mCP) relevant to climate quality seawater pH measurements. The model was trained on measurements of UV-visible absorbance spectra of purified mCP and tested on independent datasets consisting of purified and unpurified mCP samples. The repository contains demo scripts that will demonstrate the training and optimization of the DD-SIMCA model and reproduce the figures in the associated publication. A function is provided for users to classify new mCP samples with the model.\n\nThe datasets consist of measurements of the UV-visible absorbance spectra (350 nm to 750 nm) of purified and unpurified m-cresol purple samples in sodium hydroxide and sodium chloride solutions at pH\n12 and an ionic strength of 0.7 mol/kg soln. The UV-visible absorbance measurements were collected on an Agilent Cary 100 spectrophotometer at NIST.  A second dataset is included consisting of \nsimilar measurements of various purified m-cresol purple samples collected on an Agilent 8453 spectrophotometer at MBARI. This dataset was used to test the performance of the SIMCA model on samples measured on a different spectrophotometer. 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The Plant Finder recommends appropriate habitat-building plants for sidewalks, gardens and roofs that are adapted to San Francisco's unique environment and climate.\n\nThe plants in the database include California natives and Mediterranean climate exotics. A large subset of the California natives are actually local San Francisco natives. We strongly recommend local natives since they provide the best habitat for local pollinators and other wildlife with whom they have co-evolved. San Francisco natives are the most closely adapted to the climate and environment of the San Francisco peninsula of course, and so they are the best in terms of water and soil conservation, ecosystem health, and overall sustainability.\n\nThe geographic boundaries for plant communities used in SF Plant Finder are here: https://data.sfgov.org/d/27u4-a5b3","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/vmnk-skih/columns.json","describedByType":"application/json","downloadURL":"https://data.sf.gov/api/v3/views/vmnk-skih/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/vmnk-skih/columns.xml","describedByType":"application/xml","downloadURL":"https://data.sf.gov/api/v3/views/vmnk-skih/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/vmnk-skih/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"}],"identifier":"https://data.sf.gov/api/views/vmnk-skih","issued":"2025-07-10","keyword":["environment","green connections","planning","plant","plantfinder","plants","sf plant finder"],"landingPage":"https://data.sf.gov/d/vmnk-skih","license":"http://opendatacommons.org/licenses/pddl/1.0/","modified":"2025-07-10","publisher":{"@type":"org:Organization","name":"data.sf.gov"},"theme":["Energy and Environment"],"title":"San Francisco Plant Finder Data"},"description":"This is the plant list used by the SF Plant Finder (https://sfplanninggis.org/plantsf/).  \n\nThe San Francisco Plant Finder is a resource for gardeners, designers, ecologists and anyone who is interested in greening neighborhoods, enhancing our urban ecology and surviving the drought. The Plant Finder recommends appropriate habitat-building plants for sidewalks, gardens and roofs that are adapted to San Francisco's unique environment and climate.\n\nThe plants in the database include California natives and Mediterranean climate exotics. A large subset of the California natives are actually local San Francisco natives. We strongly recommend local natives since they provide the best habitat for local pollinators and other wildlife with whom they have co-evolved. San Francisco natives are the most closely adapted to the climate and environment of the San Francisco peninsula of course, and so they are the best in terms of water and soil conservation, ecosystem health, and overall sustainability.\n\nThe geographic boundaries for plant communities used in SF Plant Finder are here: https://data.sfgov.org/d/27u4-a5b3","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/1aa0febb-120d-4928-bf02-f70ef3e232b3","harvest_record_raw":"https://catalog.data.gov/harvest_record/1aa0febb-120d-4928-bf02-f70ef3e232b3/raw","has_download":true,"has_spatial":false,"identifier":"https://data.sf.gov/api/views/vmnk-skih","keyword":["environment","green connections","planning","plant","plantfinder","plants","sf plant finder"],"last_harvested_date":"2026-09-02T18:56:39.586390","organization":{"aliases":["sf","california"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"7f3c6c7a-ef5f-4ee0-87ce-a0300ba767d5","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/city_san_francisco_ca.png","name":"City of San Francisco","organization_type":"City Government","slug":"san-francisco-ca"},"parent_identifier":null,"popularity":0,"publisher":"data.sf.gov","slug":"san-francisco-plant-finder-data","spatial_centroid":null,"spatial_shape":null,"theme":["Energy and Environment"],"title":"San Francisco Plant Finder Data","type":"dataset"},{"_score":7.975216,"_sort":[1788375398889,7.975216,4,"374f22e6-71ff-41c8-ad59-226d7c041e6e"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"OpenData","hasEmail":"mailto:no-reply@data.sf.gov"},"description":"<strong>A. SUMMARY</strong>\nThe Municipal Natural Gas Equipment Inventory serves to catalog natural gas-fueled equipment used in municipally owned buildings. \nThis inventory, implemented by the SF Environment Department, aims to establish an understanding of the scope of work needed to electrify municipal buildings and inform an effective and collaborative planning process.\nThis effort was identified as an action in Section BO-2.4 of the  <u><a href=\"https://www.sfenvironment.org/files/events/2021_climate_action_plan.pdf\">2021 Climate Action Plan</a></u> and is included in the <u><a href=\"https://codelibrary.amlegal.com/codes/san_francisco/latest/sf_environment/0-0-0-577\">Environment Code Chapter 7</a></u> (Municipal Green Building Requirements). \n\n<strong>B. HOW THE DATASET IS CREATED</strong>\nThe list of buildings required to report data for the Municipal Natural Gas Equipment Inventory was compiled by cross-referencing the City\u2019s  <u><a href=\"https://data.sfgov.org/City-Infrastructure/City-Facilities/nc68-ngbr/about_datax\">Facility Systems of Record</a></u> and the  <u><a href=\"https://sfpuc.org/about-us/reports/municipal-buildings-energy-benchmarking\">2020 municipal benchmarking report</a></u> to identify all city-owned buildings with non-zero carbon emissions. Numerous municipal buildings are exempt from these reporting requirements, including facilities of the Port of San Francisco and buildings with a primary purpose of providing collection, storage, treatment, delivery, distribution, and/or transmission of water, wastewater, and/or power utilities. \nEach department received an inventory template, provided by the Environment Department, to submit high level building data and detailed information on each piece of natural gas equipment in use in these buildings. Departments were asked to self-report the required building and equipment data over the course of a 6-month data collection period in 2023 and are asked to keep this inventory up to date in the following years as equipment is replaced. \n\n<strong>C. UPDATE PROCESS</strong>\nThe inventory will be regularly updated by department representatives via the inventory PowerApp. When a gas-powered equipment item is retired or replaced, departments are asked to mark it as no longer in use and provide information on any electric replacement equipment, if applicable. While departments have the flexibility to update the inventory at any time, they are encouraged to do so at 6 month intervals at the minimum. \n\nUpdated inventory data will be automatically reflected in this dataset. \n\n<strong>D. HOW TO USE THIS DATASET</strong>\nIt is important to note that this dataset does not include facilities of the Port of San Francisco and buildings with a primary purpose of providing collection, storage, treatment, delivery, distribution, and/or transmission of water, wastewater, and/or power utilities, in accordance with Environment Code Chapter 7 exemptions.","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/vc6r-v7av/columns.json","describedByType":"application/json","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/vc6r-v7av/columns.xml","describedByType":"application/xml","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/export.kml?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kml+xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/export.kmz?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kmz"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/query.geojson?accessType=DOWNLOAD","mediaType":"application/geo+json"}],"identifier":"https://data.sf.gov/api/views/vc6r-v7av","issued":"2024-03-28","keyword":["environment","environmental health","greenhouse gas emissions","natural gas"],"landingPage":"https://data.sf.gov/d/vc6r-v7av","modified":"2026-08-28","publisher":{"@type":"org:Organization","name":"data.sf.gov"},"theme":["Energy and Environment"],"title":"San Francisco Municipal Natural Gas Equipment Inventory"},"description":"<strong>A. SUMMARY</strong>\nThe Municipal Natural Gas Equipment Inventory serves to catalog natural gas-fueled equipment used in municipally owned buildings. \nThis inventory, implemented by the SF Environment Department, aims to establish an understanding of the scope of work needed to electrify municipal buildings and inform an effective and collaborative planning process.\nThis effort was identified as an action in Section BO-2.4 of the  <u><a href=\"https://www.sfenvironment.org/files/events/2021_climate_action_plan.pdf\">2021 Climate Action Plan</a></u> and is included in the <u><a href=\"https://codelibrary.amlegal.com/codes/san_francisco/latest/sf_environment/0-0-0-577\">Environment Code Chapter 7</a></u> (Municipal Green Building Requirements). \n\n<strong>B. HOW THE DATASET IS CREATED</strong>\nThe list of buildings required to report data for the Municipal Natural Gas Equipment Inventory was compiled by cross-referencing the City\u2019s  <u><a href=\"https://data.sfgov.org/City-Infrastructure/City-Facilities/nc68-ngbr/about_datax\">Facility Systems of Record</a></u> and the  <u><a href=\"https://sfpuc.org/about-us/reports/municipal-buildings-energy-benchmarking\">2020 municipal benchmarking report</a></u> to identify all city-owned buildings with non-zero carbon emissions. Numerous municipal buildings are exempt from these reporting requirements, including facilities of the Port of San Francisco and buildings with a primary purpose of providing collection, storage, treatment, delivery, distribution, and/or transmission of water, wastewater, and/or power utilities. \nEach department received an inventory template, provided by the Environment Department, to submit high level building data and detailed information on each piece of natural gas equipment in use in these buildings. Departments were asked to self-report the required building and equipment data over the course of a 6-month data collection period in 2023 and are asked to keep this inventory up to date in the following years as equipment is replaced. \n\n<strong>C. UPDATE PROCESS</strong>\nThe inventory will be regularly updated by department representatives via the inventory PowerApp. When a gas-powered equipment item is retired or replaced, departments are asked to mark it as no longer in use and provide information on any electric replacement equipment, if applicable. While departments have the flexibility to update the inventory at any time, they are encouraged to do so at 6 month intervals at the minimum. \n\nUpdated inventory data will be automatically reflected in this dataset. \n\n<strong>D. HOW TO USE THIS DATASET</strong>\nIt is important to note that this dataset does not include facilities of the Port of San Francisco and buildings with a primary purpose of providing collection, storage, treatment, delivery, distribution, and/or transmission of water, wastewater, and/or power utilities, in accordance with Environment Code Chapter 7 exemptions.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/6bc8420b-1643-4be1-b0ee-0dd225c00c2f","harvest_record_raw":"https://catalog.data.gov/harvest_record/6bc8420b-1643-4be1-b0ee-0dd225c00c2f/raw","has_download":true,"has_spatial":false,"identifier":"https://data.sf.gov/api/views/vc6r-v7av","keyword":["environment","environmental health","greenhouse gas emissions","natural gas"],"last_harvested_date":"2026-09-02T18:56:38.889822","organization":{"aliases":["sf","california"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"7f3c6c7a-ef5f-4ee0-87ce-a0300ba767d5","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/city_san_francisco_ca.png","name":"City of San Francisco","organization_type":"City Government","slug":"san-francisco-ca"},"parent_identifier":null,"popularity":4,"publisher":"data.sf.gov","slug":"san-francisco-municipal-natural-gas-equipment-inventory","spatial_centroid":null,"spatial_shape":null,"theme":["Energy and Environment"],"title":"San Francisco Municipal Natural Gas Equipment Inventory","type":"dataset"},{"_score":10.199381,"_sort":[1788375386975,10.199381,0,"f38ec6b1-0e62-4fbc-a758-65f0796265bd"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"OpenData","hasEmail":"mailto:no-reply@data.sf.gov"},"description":"<strong>A. SUMMARY</strong>\nSan Francisco International Airport (SFO) keeps track of aircraft noise levels in communities around the airport 24/7. Measured aircraft noise events are used in calculations to determine Aircraft Community Noise Equivalent Level (CNEL). This noise metric is used to assess and regulate aircraft noise exposure in residential communities surrounding the airport. The annual Aircraft CNEL helps validate the 65\u2010decibel noise impact contour, an output of computer noise modeling.\n\n<strong>B. HOW THE DATASET IS CREATED</strong>\nThis dataset consists of measured aircraft noise events at each of SFO\u2019s 29 community noise monitoring sites. Also provided as part of this dataset is ANEEM Aircraft CNEL. This aircraft climate is derived using ANEEM algorithms that can measure quieter aircraft noise levels below that of conventional threshold correlation methodology resulting in improved noise to aircraft correlations.\n\n<strong>C. UPDATE PROCESS</strong>\nData is available starting in March 2017. Aircraft climates derived using ANEEM algorithms are available starting January 2023. This dataset will be updated on a monthly basis.\n\n<strong>D. HOW TO USE THIS DATASET</strong>\nThis information is used to produce the monthly Aircraft Noise Levels section on page 1 of the Airport Director\u2019s Report. These reports are presented at the SFO Airport Community Roundtable Meetings and available online at https://www.flysfo.com/about/community-noise/noise-office/reports/airport-directors-report\n\nPlease contact the Noise Abatement Office at NoiseAbatementOffice@flysfo.com for any questions regarding this data.\n\nDate created: June 27, 2023","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/qxw2-ncq3/columns.json","describedByType":"application/json","downloadURL":"https://data.sf.gov/api/v3/views/qxw2-ncq3/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/qxw2-ncq3/columns.xml","describedByType":"application/xml","downloadURL":"https://data.sf.gov/api/v3/views/qxw2-ncq3/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/qxw2-ncq3/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/qxw2-ncq3/export.kml?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kml+xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/qxw2-ncq3/export.kmz?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kmz"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/qxw2-ncq3/query.geojson?accessType=DOWNLOAD","mediaType":"application/geo+json"}],"identifier":"https://data.sf.gov/api/views/qxw2-ncq3","issued":"2023-11-20","keyword":["untagged"],"landingPage":"https://data.sf.gov/d/qxw2-ncq3","license":"http://opendatacommons.org/licenses/pddl/1.0/","modified":"2026-07-28","publisher":{"@type":"org:Organization","name":"data.sf.gov"},"theme":["Transportation"],"title":"Aircraft Noise Climates"},"description":"<strong>A. SUMMARY</strong>\nSan Francisco International Airport (SFO) keeps track of aircraft noise levels in communities around the airport 24/7. Measured aircraft noise events are used in calculations to determine Aircraft Community Noise Equivalent Level (CNEL). This noise metric is used to assess and regulate aircraft noise exposure in residential communities surrounding the airport. The annual Aircraft CNEL helps validate the 65\u2010decibel noise impact contour, an output of computer noise modeling.\n\n<strong>B. HOW THE DATASET IS CREATED</strong>\nThis dataset consists of measured aircraft noise events at each of SFO\u2019s 29 community noise monitoring sites. Also provided as part of this dataset is ANEEM Aircraft CNEL. This aircraft climate is derived using ANEEM algorithms that can measure quieter aircraft noise levels below that of conventional threshold correlation methodology resulting in improved noise to aircraft correlations.\n\n<strong>C. UPDATE PROCESS</strong>\nData is available starting in March 2017. Aircraft climates derived using ANEEM algorithms are available starting January 2023. This dataset will be updated on a monthly basis.\n\n<strong>D. HOW TO USE THIS DATASET</strong>\nThis information is used to produce the monthly Aircraft Noise Levels section on page 1 of the Airport Director\u2019s Report. These reports are presented at the SFO Airport Community Roundtable Meetings and available online at https://www.flysfo.com/about/community-noise/noise-office/reports/airport-directors-report\n\nPlease contact the Noise Abatement Office at NoiseAbatementOffice@flysfo.com for any questions regarding this data.\n\nDate created: June 27, 2023","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/0b548d66-940d-4bba-99f1-2bcef74ea3af","harvest_record_raw":"https://catalog.data.gov/harvest_record/0b548d66-940d-4bba-99f1-2bcef74ea3af/raw","has_download":true,"has_spatial":false,"identifier":"https://data.sf.gov/api/views/qxw2-ncq3","keyword":["untagged"],"last_harvested_date":"2026-09-02T18:56:26.975410","organization":{"aliases":["sf","california"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"7f3c6c7a-ef5f-4ee0-87ce-a0300ba767d5","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/city_san_francisco_ca.png","name":"City of San Francisco","organization_type":"City Government","slug":"san-francisco-ca"},"parent_identifier":null,"popularity":0,"publisher":"data.sf.gov","slug":"aircraft-noise-climates","spatial_centroid":null,"spatial_shape":null,"theme":["Transportation"],"title":"Aircraft Noise Climates","type":"dataset"},{"_score":14.39775,"_sort":[1788375384045,14.39775,0,"e263fdc3-2713-4c14-a744-42bfd7933ea5"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"OpenData","hasEmail":"mailto:no-reply@data.sf.gov"},"description":"The purpose of the San Francisco Municipal Greenhouse Gas Inventory is to measure and track departmental greenhouse gas emissions as part of the City's climate action strategy. Per Environment Code Chapter 9, this data is collected and calculated by the Department of the Environment.\n\n\nNote: Data as of 10/20/18. San Francisco municipal greenhouse gas inventory for Fiscal Years 2012 per the California Air Resources Board's Local Government Operations Protocol Version 1.1 (May 2010). Third-party verification of Fiscal Year 2012 which was completed in March 2015 is available at http://sfenvironment.org/download/fiscal-year-2012-municipal-ghg-inventory-memo","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/pxac-sadh/columns.json","describedByType":"application/json","downloadURL":"https://data.sf.gov/api/v3/views/pxac-sadh/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/pxac-sadh/columns.xml","describedByType":"application/xml","downloadURL":"https://data.sf.gov/api/v3/views/pxac-sadh/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/pxac-sadh/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"}],"identifier":"https://data.sf.gov/api/views/pxac-sadh","issued":"2016-01-21","keyword":["untagged"],"landingPage":"https://data.sf.gov/d/pxac-sadh","license":"http://opendatacommons.org/licenses/pddl/1.0/","modified":"2024-06-20","publisher":{"@type":"org:Organization","name":"data.sf.gov"},"theme":["Energy and Environment"],"title":"San Francisco Municipal Greenhouse Gas Inventory"},"description":"The purpose of the San Francisco Municipal Greenhouse Gas Inventory is to measure and track departmental greenhouse gas emissions as part of the City's climate action strategy. Per Environment Code Chapter 9, this data is collected and calculated by the Department of the Environment.\n\n\nNote: Data as of 10/20/18. San Francisco municipal greenhouse gas inventory for Fiscal Years 2012 per the California Air Resources Board's Local Government Operations Protocol Version 1.1 (May 2010). Third-party verification of Fiscal Year 2012 which was completed in March 2015 is available at http://sfenvironment.org/download/fiscal-year-2012-municipal-ghg-inventory-memo","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/a9776654-4081-44cb-9f6c-c9ca70b207aa","harvest_record_raw":"https://catalog.data.gov/harvest_record/a9776654-4081-44cb-9f6c-c9ca70b207aa/raw","has_download":true,"has_spatial":false,"identifier":"https://data.sf.gov/api/views/pxac-sadh","keyword":["untagged"],"last_harvested_date":"2026-09-02T18:56:24.045675","organization":{"aliases":["sf","california"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"7f3c6c7a-ef5f-4ee0-87ce-a0300ba767d5","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/city_san_francisco_ca.png","name":"City of San Francisco","organization_type":"City Government","slug":"san-francisco-ca"},"parent_identifier":null,"popularity":0,"publisher":"data.sf.gov","slug":"san-francisco-municipal-greenhouse-gas-inventory","spatial_centroid":null,"spatial_shape":null,"theme":["Energy and Environment"],"title":"San Francisco Municipal Greenhouse Gas Inventory","type":"dataset"},{"_score":11.401099,"_sort":[1788375380108,11.401099,0,"728f25d0-74a8-420f-aae0-5c267fe1aec8"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"Alex Morrison","hasEmail":"mailto:no-reply@data.sf.gov"},"description":"Heat and air quality issues caused by climate change and gas-powered vehicles affect San Francisco communities differently. Tree canopy that would buffer the effects is not equally distributed due to historic racial inequities in infrastructure investment.\n\nTo positively affect public health and leverage new federal funding sources, the City and other stakeholders are planning for green infrastructure investments, such as tree planting, sidewalk landscape zones, cool pavement, structural shading,\ngreen schoolyards, and increased areas of stormwater management. This dataset identifies locations where these strategies could have the highest benefit\nto community health and make the most effective use of City investment.\n\nSF Public Works mapped a combination of environmental and health data to identify the priority zones. The study layers exposure to fine particulate matter (PM2.5), satellite temperature readings from a recent heat wave, and tree canopy data to identify where exposure is the highest. To further refine the prioritization zone, data was added for residents experiencing asthma or diabetes hospitalizations\nwhich are both exacerbated by heat and air quality issues. \n\nThis created two final maps focused on heat and air quality that combine environmental data and human health. These maps were combined to produce the final priority zones.","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/nn26-kuy2/columns.json","describedByType":"application/json","downloadURL":"https://data.sf.gov/api/v3/views/nn26-kuy2/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/nn26-kuy2/columns.xml","describedByType":"application/xml","downloadURL":"https://data.sf.gov/api/v3/views/nn26-kuy2/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/nn26-kuy2/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/nn26-kuy2/export.kml?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kml+xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/nn26-kuy2/export.kmz?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kmz"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/nn26-kuy2/query.geojson?accessType=DOWNLOAD","mediaType":"application/geo+json"}],"identifier":"https://data.sf.gov/api/views/nn26-kuy2","issued":"2024-06-20","keyword":["untagged"],"landingPage":"https://data.sf.gov/d/nn26-kuy2","modified":"2024-06-21","publisher":{"@type":"org:Organization","name":"data.sf.gov"},"theme":["Energy and Environment"],"title":"HAQR Priority Green Infrastructure Zones"},"description":"Heat and air quality issues caused by climate change and gas-powered vehicles affect San Francisco communities differently. Tree canopy that would buffer the effects is not equally distributed due to historic racial inequities in infrastructure investment.\n\nTo positively affect public health and leverage new federal funding sources, the City and other stakeholders are planning for green infrastructure investments, such as tree planting, sidewalk landscape zones, cool pavement, structural shading,\ngreen schoolyards, and increased areas of stormwater management. This dataset identifies locations where these strategies could have the highest benefit\nto community health and make the most effective use of City investment.\n\nSF Public Works mapped a combination of environmental and health data to identify the priority zones. The study layers exposure to fine particulate matter (PM2.5), satellite temperature readings from a recent heat wave, and tree canopy data to identify where exposure is the highest. To further refine the prioritization zone, data was added for residents experiencing asthma or diabetes hospitalizations\nwhich are both exacerbated by heat and air quality issues. \n\nThis created two final maps focused on heat and air quality that combine environmental data and human health. These maps were combined to produce the final priority zones.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/6d254ee6-5c2a-4d45-aab6-d337b3ccbd8a","harvest_record_raw":"https://catalog.data.gov/harvest_record/6d254ee6-5c2a-4d45-aab6-d337b3ccbd8a/raw","has_download":true,"has_spatial":false,"identifier":"https://data.sf.gov/api/views/nn26-kuy2","keyword":["untagged"],"last_harvested_date":"2026-09-02T18:56:20.108177","organization":{"aliases":["sf","california"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"7f3c6c7a-ef5f-4ee0-87ce-a0300ba767d5","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/city_san_francisco_ca.png","name":"City of San Francisco","organization_type":"City Government","slug":"san-francisco-ca"},"parent_identifier":null,"popularity":0,"publisher":"data.sf.gov","slug":"haqr-priority-green-infrastructure-zones","spatial_centroid":null,"spatial_shape":null,"theme":["Energy and Environment"],"title":"HAQR Priority Green Infrastructure Zones","type":"dataset"},{"_score":11.209602,"_sort":[1788375372830,11.209602,0,"857be423-36b5-410e-9739-045558e27105"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"SFEBO Help Desk","hasEmail":"mailto:no-reply@data.sf.gov"},"description":"<strong>As of March 20, 2026, this dataset will no longer update. To access new and historical data going forward, navigate to the <u><a href=\"https://data.sfgov.org/d/bfhx-j6n5/\">dataset here</a></u>.</strong>\n\n<strong>A. SUMMARY</strong>\nSan Francisco\u2019s Existing Buildings Energy Performance Ordinance requires owners of non-residential buildings over 10,000 square feet to annually benchmark and disclose energy performance. On behalf of City agencies, the San Francisco Public Utilities Commission (SFPUC) benchmarks and reports energy use for a portfolio of approximately 500 public facilities buildings. 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Steps for calculating the index can be found in in the \"An Assessment of San Francisco\u2019s Vulnerability to Flooding & Extreme Storms\" located at https://sfclimatehealth.org/wp-content/uploads/2018/12/FloodVulnerabilityReport_v5.pdf.pdf\n\nData dictionary can be found in the attachments section of the metadata.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/7705d5b6-a21a-4f45-a424-d311db779f24","harvest_record_raw":"https://catalog.data.gov/harvest_record/7705d5b6-a21a-4f45-a424-d311db779f24/raw","has_download":true,"has_spatial":false,"identifier":"https://data.sf.gov/api/views/cne3-h93g","keyword":["@sfclimatehealth.org","climate change","community resiliency","dph","flood","health assessment","health impacts","public health","san francisco climate and health program","sea level rise","sfclimatehealth.org"],"last_harvested_date":"2026-09-02T18:55:50.368499","organization":{"aliases":["sf","california"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"7f3c6c7a-ef5f-4ee0-87ce-a0300ba767d5","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/city_san_francisco_ca.png","name":"City of San Francisco","organization_type":"City Government","slug":"san-francisco-ca"},"parent_identifier":null,"popularity":3,"publisher":"data.sf.gov","slug":"san-francisco-flood-health-vulnerability","spatial_centroid":null,"spatial_shape":null,"theme":["Health and Social Services"],"title":"San Francisco Flood Health Vulnerability","type":"dataset"},{"_score":27.607811,"_sort":[1788375348420,27.607811,3,"4b00ede5-99cb-4808-987b-f1277b8edf48"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"OpenData","hasEmail":"mailto:no-reply@data.sf.gov"},"description":"The purpose of the San Francisco Communitywide Greenhouse Gas Inventory is to measure and track greenhouse gas emissions to determine progress towards meeting the City's climate action goals. The Department of the Environment collects this data from various sources and calculates the emissions per current greenhouse gas protocols. This data supports San Francisco's climate change planning and mitigation strategies.\n\nNote: Greenhouse gas emissions were calculated based on the ICLEI 2012 U.S. Community Protocol Version 1.0. San Francisco inventories are completed in accordance with the ICLEI U.S. Community Protocol (USCP) for Accounting and Reporting of Greenhouse Gas Emissions. The methodology and sectors tracked were third party verified in inventory year 2012. The subsequent inventories are completed according to the guidance of the verifiers. The third-party verification memo for 2010 is available at http://sfenvironment.org/download/2010-community-greenhouse-gas-inventory-3rd-party-verification-memo-march-2013 and for 2012 at http://sfenvironment.org/download/2012-community-greenhouse-gas-inventory-3rd-party-verification-memo-january-2015. In 2015, the City began reporting its emissions to C40 to improve its GHG emissions inventory by using a newer protocol to estimate emissions referred to as the Global Protocol for Community-Scale Greenhouse Gas Emissions Inventories (GPC). GPC is a framework unifying emissions inventories globally while incorporating new categories to track. San Francisco has been tracking its emissions since 1990; hence, it continues to use the ICLEI USCP. Today, San Francisco continues to disclose emissions under the GPC framework for reporting purposes to and compliance with the Global Covenant of Mayors (GCOM).","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/btm4-e4ak/columns.json","describedByType":"application/json","downloadURL":"https://data.sf.gov/api/v3/views/btm4-e4ak/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/btm4-e4ak/columns.xml","describedByType":"application/xml","downloadURL":"https://data.sf.gov/api/v3/views/btm4-e4ak/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/btm4-e4ak/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"}],"identifier":"https://data.sf.gov/api/views/btm4-e4ak","issued":"2018-10-31","keyword":["carbon emissions","climate","climate change","communitywide","environment","ghg inventory","greenhouse gas emissions","san francisco climate action strategy","sustainability"],"landingPage":"https://data.sf.gov/d/btm4-e4ak","license":"http://opendatacommons.org/licenses/pddl/1.0/","modified":"2024-06-20","publisher":{"@type":"org:Organization","name":"data.sf.gov"},"theme":["Energy and Environment"],"title":"San Francisco Communitywide Greenhouse Gas Inventory"},"description":"The purpose of the San Francisco Communitywide Greenhouse Gas Inventory is to measure and track greenhouse gas emissions to determine progress towards meeting the City's climate action goals. The Department of the Environment collects this data from various sources and calculates the emissions per current greenhouse gas protocols. This data supports San Francisco's climate change planning and mitigation strategies.\n\nNote: Greenhouse gas emissions were calculated based on the ICLEI 2012 U.S. Community Protocol Version 1.0. San Francisco inventories are completed in accordance with the ICLEI U.S. Community Protocol (USCP) for Accounting and Reporting of Greenhouse Gas Emissions. The methodology and sectors tracked were third party verified in inventory year 2012. The subsequent inventories are completed according to the guidance of the verifiers. The third-party verification memo for 2010 is available at http://sfenvironment.org/download/2010-community-greenhouse-gas-inventory-3rd-party-verification-memo-march-2013 and for 2012 at http://sfenvironment.org/download/2012-community-greenhouse-gas-inventory-3rd-party-verification-memo-january-2015. 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For more context about the maps and analyses, including a description of the data and methods used, please see the Climate Stressors and Impacts Report: Bayside Sea Level Rise Inundation Mapping Technical Memorandum, July 2014.\r\n\r\nMore information at http://onesanfrancisco.org/node/148","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/92e4-7ptg/columns.json","describedByType":"application/json","downloadURL":"https://data.sf.gov/api/v3/views/92e4-7ptg/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/92e4-7ptg/columns.xml","describedByType":"application/xml","downloadURL":"https://data.sf.gov/api/v3/views/92e4-7ptg/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/92e4-7ptg/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/92e4-7ptg/export.kml?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kml+xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/92e4-7ptg/export.kmz?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kmz"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/92e4-7ptg/query.geojson?accessType=DOWNLOAD","mediaType":"application/geo+json"}],"identifier":"https://data.sf.gov/api/views/92e4-7ptg","issued":"2017-03-20","keyword":["climate change","planning","preparedness","resiliency"],"landingPage":"https://data.sf.gov/d/92e4-7ptg","license":"http://opendatacommons.org/licenses/pddl/1.0/","modified":"2024-06-26","publisher":{"@type":"org:Organization","name":"data.sf.gov"},"theme":["Geographic Locations and Boundaries"],"title":"108\" Inundation Vulnerability Zone Line (Sea Level Rise + 100YR Flood Event)"},"description":"Disclaimer: The Sea Level Rise (SLR) map shows the most extreme level of SLR possible. 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Since the GRACE data have a 2-6 month latency, the GLDAS-2.2 data are first created without GRACE-DA, and are designated as the Early Product (EP), with about 1 month latency.  Once the GRACE data become available, the GLDAS-2.2 data are processed with GRACE-DA in the main production stream and are removed from the Early Product archive.  \n\nThe GLDAS-2.2 GRACE-DA product was simulated with Catchment-F2.5 in Land Information System (LIS) Version 7. The data product contains 24 land surface fields from February 1, 2003 to present.\n\nThe simulation started on February 1, 2003 using the conditions from the GLDAS-2.0 Daily Catchment model simulation, forced with the meteorological analysis fields from the operational European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System.  The total terrestrial water anomaly observation from GRACE satellite was assimilated (Li et al, 2019). 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The entire GLDAS-001 collection from January 1979 through March 2020 was decommissioned on June 30, 2020 and removed from the GES DISC system. For more information, please see the IMPORTANT NOTICE document.\n\nThis data set contains a series of land surface parameters simulated from the Common Land Model (CLM) V2.0 model in the Global Land Data Assimilation System (GLDAS). The data are in 1.0 degree resolution and range from January 1979 to present. The temporal resolution is 3-hourly. \n\nThis simulation was forced by a combination of NOAA/GDAS atmospheric analysis fields, spatially and temporally disaggregated NOAA Climate Prediction Center Merged Analysis of Precipitation (CMAP) fields, and observation based downward shortwave and longwave radiation fields derived using the method of the Air Force Weather Agency's AGRicultural METeorological modeling system (AGRMET). 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The GLDAS-2.0 data are archived and distributed in NetCDF format.\n\nThe GLDAS-2.0 model simulations were initialized on January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulations were forced by the global meteorological forcing data set from Princeton University (Sheffield et al., 2006). Each simulation uses the common GLDAS data sets for land water mask (MOD44W: Carroll et al., 2009) and elevation (GTOPO30) along with the model default land cover and soils datasets. Catchment model uses the Mosaic land cover classification and soils, topographic, and other model-specific parameters were derived in a consistent manner as in the NASA/GMAO\u2019s GEOS-5 climate modeling system. The MODIS based land surface parameters are used in the current GLDAS-2.0 and GLDAS-2.1 products.\n\nIn October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask.  Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values.  The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README.  The MOD44W MODIS land mask is available on the GLDAS Project site.\n\nIf you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C1933574565-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C1933574565-GES_DISC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/GLDAS_VIC10_M_2.0.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/information/documents?title=Hydrology%2520Documentation","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/information/howto?tags=hydrology","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/GLDAS_VIC10_M_2.0.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/hydrology/README_GLDAS2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/GLDAS/GLDAS_CLM10SUBP_3H_Status_and_Related_Data_Collections.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/GLDAS/GLDAS_LSM_Description.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://hydro1.gesdisc.eosdis.nasa.gov/data/GLDAS/GLDAS_VIC10_M.2.0/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ldas.gsfc.nasa.gov/gldas/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C1933574565-GES_DISC","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/ZRIHVF29X43C","keyword":["earth-science-atmospheric-pressure-atmosphere-surface-pressure","earth-science-atmospheric-radiation-atmosphere-heat-flux","earth-science-atmospheric-radiation-atmosphere-longwave-radiation","earth-science-atmospheric-radiation-atmosphere-shortwave-radiation","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-processes","earth-science-atmospheric-winds-atmosphere-surface-winds","earth-science-precipitation-atmosphere-liquid-precipitation","earth-science-precipitation-atmosphere-precipitation-rate","earth-science-precipitation-atmosphere-solid-precipitation","earth-science-snow-ice-terrestrial-hydrosphere-snow-water-equivalent","earth-science-soils-land-surface-soil-moisture-water-content","earth-science-soils-land-surface-soil-temperature","earth-science-surface-thermal-properties-land-surface-land-surface-temperature","earth-science-surface-water-terrestrial-hydrosphere-surface-water-processes-measurements"],"license":"https://www.usa.gov/government-works","modified":"2026-08-25","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -60}]]","temporal":"1948-01-01/2014-12-31","theme":["Earth Science"],"title":"GLDAS VIC Land Surface Model L4 monthly 1.0 x 1.0 degree V2.0 (GLDAS_VIC10_M)"},"description":"NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2.  GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014.  GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present.  GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are \"open-loop\" (i.e., no data assimilation).  The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.\n\nThis data set,  GLDAS-2.0 VIC monthly 1.0 degree, contains a series of land surface variables generated through temporal averaging of GLDAS-2.0 3-hourly data simulated with the VIC 4.1.2 Land Surface Model in Land Information System (LIS) Version 7. The data set currently cover from January 1948 to December 2014, but will be extended as the forcing data becomes available. The GLDAS-2.0 data are archived and distributed in NetCDF format.\n\nThe GLDAS-2.0 model simulations were initialized on January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulations were forced by the global meteorological forcing data set from Princeton University (Sheffield et al., 2006). Each simulation uses the common GLDAS data sets for land water mask (MOD44W: Carroll et al., 2009) and elevation (GTOPO30) along with the model default land cover and soils datasets. Catchment model uses the Mosaic land cover classification and soils, topographic, and other model-specific parameters were derived in a consistent manner as in the NASA/GMAO\u2019s GEOS-5 climate modeling system. The MODIS based land surface parameters are used in the current GLDAS-2.0 and GLDAS-2.1 products.\n\nIn October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask.  Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values.  The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README.  The MOD44W MODIS land mask is available on the GLDAS Project site.\n\nIf you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/a9c55de3-c4dd-4a96-9f87-3fc486049f1a","harvest_record_raw":"https://catalog.data.gov/harvest_record/a9c55de3-c4dd-4a96-9f87-3fc486049f1a/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/ZRIHVF29X43C","keyword":["earth-science-atmospheric-pressure-atmosphere-surface-pressure","earth-science-atmospheric-radiation-atmosphere-heat-flux","earth-science-atmospheric-radiation-atmosphere-longwave-radiation","earth-science-atmospheric-radiation-atmosphere-shortwave-radiation","earth-science-atmospheric-temperature-atmosphere-surface-temperature","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators","earth-science-atmospheric-water-vapor-atmosphere-water-vapor-processes","earth-science-atmospheric-winds-atmosphere-surface-winds","earth-science-precipitation-atmosphere-liquid-precipitation","earth-science-precipitation-atmosphere-precipitation-rate","earth-science-precipitation-atmosphere-solid-precipitation","earth-science-snow-ice-terrestrial-hydrosphere-snow-water-equivalent","earth-science-soils-land-surface-soil-moisture-water-content","earth-science-soils-land-surface-soil-temperature","earth-science-surface-thermal-properties-land-surface-land-surface-temperature","earth-science-surface-water-terrestrial-hydrosphere-surface-water-processes-measurements"],"last_harvested_date":"2026-09-02T00:49:44.229830","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"gldas-vic-land-surface-model-l4-monthly-1-0-x-1-0-degree-v2-0-gldas_vic10_m-at-ges-disc","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GLDAS VIC Land Surface Model L4 monthly 1.0 x 1.0 degree V2.0 (GLDAS_VIC10_M)","type":"dataset"},{"_score":11.762766,"_sort":[1788310182195,11.762766,1,"69d65c0a-8578-4140-a725-6fdd0d388e67"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"As part of the NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, this project entitled \u201cMulti-Decadal Nitrogen Dioxide and Derived Products from Satellites (MINDS)\u201d will develop consistent long-term global trend-quality data records spanning the last two decades, over which remarkable changes in nitrogen oxides (NOx) emissions have occurred. The objective of the project Is to adapt Ozone Monitoring Instrument (OMI) operational algorithms to other satellite instruments and create consistent multi-satellite L2 and L3 nitrogen dioxide (NO2) columns and value-added L4 surface NO2 concentrations and NOx emissions data products, systematically accounting for instrumental differences. The instruments include Global Ozone Monitoring Experiment (GOME, 1996-2003), SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY, 2002-2012), OMI (2004-present), GOME-2 (2007-present), and TROPOspheric Monitoring Instrument (TROPOMI, 2018-present). The quality assured L2-L4 products will be made available to the scientific community via the NASA GES DISC website in Climate and Forecast (CF)-compliant Hierarchical Data Format (HDF5) and netCDF formats.","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C2539362687-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/GOME_MINDS_NO2_1.1.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/GOME_MINDS_NO2_1.1.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://earthdata.nasa.gov/esds/competitive-programs/measures/minds","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://measures.gesdisc.eosdis.nasa.gov/data/MINDS/GOME_MINDS_NO2.1.1/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://measures.gesdisc.eosdis.nasa.gov/data/MINDS/GOME_MINDS_NO2.1.1/doc/README.MEaSUREs_MINDS_NO2.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://measures.gesdisc.eosdis.nasa.gov/opendap/hyrax/MINDS/GOME_MINDS_NO2.1.1/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C2539362687-GES_DISC","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/MEASURES/MINDS/DATA202","keyword":["earth-science-atmospheric-chemistry-atmosphere-nitrogen-compounds"],"license":"https://www.usa.gov/government-works","modified":"2026-08-25","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"1995-06-30/2003-06-22","theme":["Earth Science"],"title":"GOME/ERS-2 NO2 Tropospheric, Stratospheric and Total Columns MINDS 1-Orbit L2 Swath 40 km x 320 km V1.1 (GOME_MINDS_NO2)"},"description":"As part of the NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, this project entitled \u201cMulti-Decadal Nitrogen Dioxide and Derived Products from Satellites (MINDS)\u201d will develop consistent long-term global trend-quality data records spanning the last two decades, over which remarkable changes in nitrogen oxides (NOx) emissions have occurred. The objective of the project Is to adapt Ozone Monitoring Instrument (OMI) operational algorithms to other satellite instruments and create consistent multi-satellite L2 and L3 nitrogen dioxide (NO2) columns and value-added L4 surface NO2 concentrations and NOx emissions data products, systematically accounting for instrumental differences. The instruments include Global Ozone Monitoring Experiment (GOME, 1996-2003), SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY, 2002-2012), OMI (2004-present), GOME-2 (2007-present), and TROPOspheric Monitoring Instrument (TROPOMI, 2018-present). The quality assured L2-L4 products will be made available to the scientific community via the NASA GES DISC website in Climate and Forecast (CF)-compliant Hierarchical Data Format (HDF5) and netCDF formats.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/150db527-d518-4cf1-9371-5279fdab9a92","harvest_record_raw":"https://catalog.data.gov/harvest_record/150db527-d518-4cf1-9371-5279fdab9a92/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/MEASURES/MINDS/DATA202","keyword":["earth-science-atmospheric-chemistry-atmosphere-nitrogen-compounds"],"last_harvested_date":"2026-09-02T00:49:42.195391","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"gome-ers-2-no2-tropospheric-stratospheric-and-total-columns-minds-1-orbit-l2-swath-40-km-x","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GOME/ERS-2 NO2 Tropospheric, Stratospheric and Total Columns MINDS 1-Orbit L2 Swath 40 km x 320 km V1.1 (GOME_MINDS_NO2)","type":"dataset"},{"_score":10.599098,"_sort":[1788310180917,10.599098,1,"1e70fac7-417d-442e-97b9-6eca9c3b6826"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"Version 3.3 is the current version. Older versions have been superseded by Version 3.3.\n\nProduct latency/update: The products are currently paused at September 2024 because the IR input dataset from NCEI requires a new calibration scheme to extend past that point. Once NCEI irons out the calibration, we expect to return to quarterly updates.\n\nThe Global Precipitation Climatology Project (GPCP) is the precipitation component of an internationally coordinated set of (mainly) satellite-based global products dealing with the Earth's water and energy cycles, under the auspices of the Global Water and Energy Exchange (GEWEX) Data and Assessment Panel (GDAP) of the World Climate Research Program.  As the follow on to the GPCP Version 1.3 One Degree Daily product, GPCP Version 3 (GPCP V3.3) seeks to continue the long, homogeneous precipitation record using modern merging techniques and input data sets.  The GPCPV3 suite currently consists of the 0.5-degree Monthly and 0.5-degree Daily. Additional products may be added, which consist of (1) 0.5-degree pentad and (2) 0.1-degree 3-hourly.  All GPCPV3 products will be internally consistent.  Inputs consist of GPM IMERG in the span 55\u00b0N-S, and TOVS/AIRS estimates, adjusted climatologically to IMERG, outside 55\u00b0N-S.  The Daily estimates are scaled to approximately sum to the Monthly value at each 0.5\u00b0 grid box.  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Older versions have been superseded by Version 3.3.\n\nProduct latency/update: The products are currently paused at September 2024 because the IR input dataset from NCEI requires a new calibration scheme to extend past that point. Once NCEI irons out the calibration, we expect to return to quarterly updates.\n\nThe Global Precipitation Climatology Project (GPCP) is the precipitation component of an internationally coordinated set of (mainly) satellite-based global products dealing with the Earth's water and energy cycles, under the auspices of the Global Water and Energy Exchange (GEWEX) Data and Assessment Panel (GDAP) of the World Climate Research Program.  As the follow on to the GPCP Version 1.3 One Degree Daily product, GPCP Version 3 (GPCP V3.3) seeks to continue the long, homogeneous precipitation record using modern merging techniques and input data sets.  The GPCPV3 suite currently consists of the 0.5-degree Monthly and 0.5-degree Daily. Additional products may be added, which consist of (1) 0.5-degree pentad and (2) 0.1-degree 3-hourly.  All GPCPV3 products will be internally consistent.  Inputs consist of GPM IMERG in the span 55\u00b0N-S, and TOVS/AIRS estimates, adjusted climatologically to IMERG, outside 55\u00b0N-S.  The Daily estimates are scaled to approximately sum to the Monthly value at each 0.5\u00b0 grid box.  In addition to the final precipitation field, probability of liquid phase estimates are provided globally.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/b9be0f8d-b288-46be-a8ae-8adcc985c46f","harvest_record_raw":"https://catalog.data.gov/harvest_record/b9be0f8d-b288-46be-a8ae-8adcc985c46f/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/MEASURES/GPCP/DATA307","keyword":["earth-science-precipitation-atmosphere","earth-science-precipitation-atmosphere-precipitation-rate"],"last_harvested_date":"2026-09-02T00:49:40.917973","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"gpcp-precipitation-level-3-daily-0-5-degree-v3-3-gpcpday-at-ges-disc","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GPCP Precipitation Level 3 Daily 0.5-Degree V3.3 (GPCPDAY)","type":"dataset"},{"_score":18.376663,"_sort":[1788310177708,18.376663,1,"7dd71175-8de8-4724-9ebb-e10749d8fde2"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"Version 08 is the current version of the data set. Older versions are no longer available and have been superseded by the current version.\n\nThe \"CLIM\"  products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series.  Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\nThe 2AGPROF (also known as, GPM GPROF (Level 2)) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: GMI, SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18) AMSR2 (GCOM-W1), TMI MHS (NOAA 18&19, METOP A&B), ATMS (NPP), SAPHIR (MT1) This provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are near-realtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. The GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an a-priori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n GPM Project generated these data at spatial sampling of 16 x 18 km  (cross-track x along-track nominal at nadir).","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C4054954931-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/ATBD_GPM_V7_GPROF.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/GPROFV08A_releasenotes.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954931-GES_DISC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954931-GES_DISC/temporal","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/GPM_2AGPROFNOAA18MHS_CLIM_08.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/README.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/browse/GPM_2AGPROFNOAA18MHS_CLIM_07.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://gpm.nasa.gov","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/pub/GPMfilespec/filespec.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/tsdis/AB/docs/gpm_anomalous.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C4054954931-GES_DISC&q=GPM_2AGPROFNOAA18MHS_CLIM_08","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.star.nesdis.noaa.gov/mirs/mhs.php","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/GPM/MHS/NOAA18/GPROFCLIM/2A/08","keyword":["earth-science-atmospheric-water-vapor-atmosphere","earth-science-precipitation-atmosphere"],"license":"https://www.usa.gov/government-works","modified":"2026-08-25","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"GEODETIC\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"2005-05-25/2018-10-20","theme":["Earth Science"],"title":"GPM MHS on NOAA-18 (GPROF) Radiometer Precipitation Profiling L2A 1.5 hours 17 km V08 (GPM_2AGPROFNOAA18MHS_CLIM)"},"description":"Version 08 is the current version of the data set. Older versions are no longer available and have been superseded by the current version.\n\nThe \"CLIM\"  products differ from their \"regular\" counterparts (without the \"CLIM\" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series.  Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the \"CLIM\" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\nThe 2AGPROF (also known as, GPM GPROF (Level 2)) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: GMI, SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18) AMSR2 (GCOM-W1), TMI MHS (NOAA 18&19, METOP A&B), ATMS (NPP), SAPHIR (MT1) This provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are near-realtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. The GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an a-priori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n GPM Project generated these data at spatial sampling of 16 x 18 km  (cross-track x along-track nominal at nadir).","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/292b0930-97a4-4e94-85e2-718f671f5170","harvest_record_raw":"https://catalog.data.gov/harvest_record/292b0930-97a4-4e94-85e2-718f671f5170/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/GPM/MHS/NOAA18/GPROFCLIM/2A/08","keyword":["earth-science-atmospheric-water-vapor-atmosphere","earth-science-precipitation-atmosphere"],"last_harvested_date":"2026-09-02T00:49:37.708322","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"gpm-mhs-on-noaa-18-gprof-radiometer-precipitation-profiling-l2a-1-5-hours-17-km-v08-gpm_2a","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GPM MHS on NOAA-18 (GPROF) Radiometer Precipitation Profiling L2A 1.5 hours 17 km V08 (GPM_2AGPROFNOAA18MHS_CLIM)","type":"dataset"},{"_score":41.270966,"_sort":[1788310177079,41.270966,1,"a1cd5cb5-a1c2-4c4a-bb91-09970d5b89ca"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"Version 08 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version.\n\nThe 'CLIM'  products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series.  Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F11, F13, F14, F15); SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n GPM Project generated these data at spatial sampling of 13 x 13 km (nominal at nadir).","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C4054954520-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/ATBD_GPM_V7_GPROF.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/GPROFV08A_releasenotes.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954520-GES_DISC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954520-GES_DISC/temporal","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/GPM_2AGPROFF08SSMI_CLIM_08.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/README.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/browse/GPM_2AGPROFF08SSMI_CLIM_07.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://gpm.nasa.gov","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/pub/GPMfilespec/filespec.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/tsdis/AB/docs/gpm_anomalous.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C4054954520-GES_DISC&q=GPM_2AGPROFF08SSMI_CLIM_08","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.wmo-sat.info/oscar/instruments/view/533","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/GPM/SSMI/F08/GPROFCLIM/2A/08","keyword":["earth-science-atmospheric-water-vapor-atmosphere","earth-science-precipitation-atmosphere"],"license":"https://www.usa.gov/government-works","modified":"2026-08-25","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"GEODETIC\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"1987-07-09/1991-12-31","theme":["Earth Science"],"title":"GPM SSM/I on F08 (GPROF) Climate-based Radiometer Precipitation Profiling L2 1.5 hours 12 km V08 (GPM_2AGPROFF08SSMI_CLIM)"},"description":"Version 08 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version.\n\nThe 'CLIM'  products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series.  Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F11, F13, F14, F15); SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n GPM Project generated these data at spatial sampling of 13 x 13 km (nominal at nadir).","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/40071308-b571-4511-aa83-733260e7edc1","harvest_record_raw":"https://catalog.data.gov/harvest_record/40071308-b571-4511-aa83-733260e7edc1/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/GPM/SSMI/F08/GPROFCLIM/2A/08","keyword":["earth-science-atmospheric-water-vapor-atmosphere","earth-science-precipitation-atmosphere"],"last_harvested_date":"2026-09-02T00:49:37.079736","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"gpm-ssm-i-on-f08-gprof-climate-based-radiometer-precipitation-profiling-l2-1-5-hours-12-km-d01d2","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GPM SSM/I on F08 (GPROF) Climate-based Radiometer Precipitation Profiling L2 1.5 hours 12 km V08 (GPM_2AGPROFF08SSMI_CLIM)","type":"dataset"},{"_score":41.182274,"_sort":[1788310176758,41.182274,2,"752af18c-0c69-4034-8bb6-a0e7e923efa8"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"Version 08 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version.\n\nThe 'CLIM'  products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series.  Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F11, F13, F14, F15); SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. 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The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F11, F13, F14, F15); SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. 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Older versions will no longer be available and have been superseded by the current version.\n\nThe 'CLIM'  products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series.  Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F11, F13, F14, F15); SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n GPM Project generated these data at spatial sampling of 13 x 13 km (nominal at nadir).","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C4054954932-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/ATBD_GPM_V7_GPROF.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/GPROFV08A_releasenotes.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954932-GES_DISC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954932-GES_DISC/temporal","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/GPM_2AGPROFF11SSMI_CLIM_08.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/README.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/browse/GPM_2AGPROFF11SSMI_CLIM_07.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://gpm.nasa.gov","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/pub/GPMfilespec/filespec.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/tsdis/AB/docs/gpm_anomalous.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C4054954932-GES_DISC&q=GPM_2AGPROFF11SSMI_CLIM_08","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.wmo-sat.info/oscar/instruments/view/533","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/GPM/SSMI/F11/GPROFCLIM/2A/08","keyword":["earth-science-atmospheric-water-vapor-atmosphere","earth-science-precipitation-atmosphere"],"license":"https://www.usa.gov/government-works","modified":"2026-08-25","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"GEODETIC\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"1991-12-03/2000-05-16","theme":["Earth Science"],"title":"GPM SSM/I on F11 (GPROF) Climate-based Radiometer Precipitation Profiling L2 1.5 hours 13 km V08 (GPM_2AGPROFF11SSMI_CLIM)"},"description":"Version 08 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version.\n\nThe 'CLIM'  products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series.  Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F11, F13, F14, F15); SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n GPM Project generated these data at spatial sampling of 13 x 13 km (nominal at nadir).","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/29b48d96-1d1d-4053-aee7-1bbae43b3c81","harvest_record_raw":"https://catalog.data.gov/harvest_record/29b48d96-1d1d-4053-aee7-1bbae43b3c81/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/GPM/SSMI/F11/GPROFCLIM/2A/08","keyword":["earth-science-atmospheric-water-vapor-atmosphere","earth-science-precipitation-atmosphere"],"last_harvested_date":"2026-09-02T00:49:36.421211","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/GSFC/SED/ESD/TISL/GESDISC","slug":"gpm-ssm-i-on-f11-gprof-climate-based-radiometer-precipitation-profiling-l2-1-5-hours-13-km-29e91","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GPM SSM/I on F11 (GPROF) Climate-based Radiometer Precipitation Profiling L2 1.5 hours 13 km V08 (GPM_2AGPROFF11SSMI_CLIM)","type":"dataset"},{"_score":42.848778,"_sort":[1788310176039,42.848778,0,"6527116e-1204-47df-a848-94c4ab9671f7"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"Version 08 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version.\n\nThe 'CLIM'  products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series.  Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F11, F13, F14, F15); SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n GPM Project generated these data at spatial sampling of 13 x 13 km (nominal at nadir).","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C4054954885-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/ATBD_GPM_V7_GPROF.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/GPROFV08A_releasenotes.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954885-GES_DISC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954885-GES_DISC/temporal","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/GPM_2AGPROFF15SSMI_CLIM_08.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/README.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/browse/GPM_2AGPROFF15SSMI_CLIM_07.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://gpm.nasa.gov","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/pub/GPMfilespec/filespec.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/tsdis/AB/docs/gpm_anomalous.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C4054954885-GES_DISC&q=GPM_2AGPROFF15SSMI_CLIM_08","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.wmo-sat.info/oscar/instruments/view/533","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/GPM/SSMI/F15/GPROFCLIM/2A/08","keyword":["earth-science-atmospheric-water-vapor-atmosphere","earth-science-precipitation-atmosphere"],"license":"https://www.usa.gov/government-works","modified":"2026-08-25","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"GEODETIC\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"2000-02-23/2006-08-14","theme":["Earth Science"],"title":"GPM SSM/I on F15 (GPROF) Climate-based Radiometer Precipitation Profiling L2 1.5 hours 12 km V08 (GPM_2AGPROFF15SSMI_CLIM)"},"description":"Version 08 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version.\n\nThe 'CLIM'  products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series.  Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F11, F13, F14, F15); SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. 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Older versions will no longer be available and have been superseded by the current version.\n\nThe 'CLIM'  products differ from their 'regular' counterparts (without the 'CLIM' in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series.  Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the 'CLIM' output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F11, F13, F14, F15); SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.\n\nThe GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.\n\n GPM Project generated these data at spatial sampling of 13 x 13 km (nominal at nadir).","distribution":[{"@type":"dcat:Distribution","conformsTo":"http://www.isotc211.org/2005/gmi","description":"The metadata's original source.","downloadURL":"https://cmr.earthdata.nasa.gov/search/concepts/C4054954554-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/ATBD_GPM_V7_GPROF.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/GPROFV08A_releasenotes.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954554-GES_DISC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C4054954554-GES_DISC/temporal","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/GPM_2AGPROFF13SSMI_CLIM_08.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/README.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/browse/GPM_2AGPROFF13SSMI_CLIM_07.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://gpm.nasa.gov","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/pub/GPMfilespec/filespec.GPM.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://gpmweb2https.pps.eosdis.nasa.gov/tsdis/AB/docs/gpm_anomalous.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C4054954554-GES_DISC&q=GPM_2AGPROFF13SSMI_CLIM_08","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.wmo-sat.info/oscar/instruments/view/533","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/GPM/SSMI/F13/GPROFCLIM/2A/08","keyword":["earth-science-atmospheric-water-vapor-atmosphere","earth-science-precipitation-atmosphere"],"license":"https://www.usa.gov/government-works","modified":"2026-08-25","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/GSFC/SED/ESD/TISL/GESDISC"},"spatial":"[\"GEODETIC\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]","temporal":"1995-05-03/2009-11-20","theme":["Earth Science"],"title":"GPM SSM/I on F13 (GPROF) Climate-based Radiometer Precipitation Profiling L2 1.5 hours 12 km V08 (GPM_2AGPROFF13SSMI_CLIM)"},"description":"Version 08 is the current version of the data set. 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The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.\n\n\nThe 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:\n+ TMI (TRMM)\n+ GMI, (GPM)\n+ SSMI (DMSP F11, F13, F14, F15); SSMIS (DMSP F16, F17, F18, F19)\n+ AMSR2 (GCOM-W1)\n+ MHS (NOAA 18,19) \n+ MHS (METOP A,B)\n+ ATMS (NPP)\n+ SAPHIR (MT1)\n\nThis provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. 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These data are typically available within three hours of measurement as required by the Land Atmosphere NRT Capability Earth Observing System (LANCE). These data are intended for a rapid turnaround assessment and are only archived for up to ten days. Users who require a longer data record, or wish to conduct rigorous analysis should use the offline version of this product S5P_L2__NO2____HiR.\n\nThe Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). 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The CrIS/ATMS instruments used for this product are on board the Suomi National Polar-orbiting Partnership (SNPP) platform and use the Normal Spectral Resolution (NSR) data. The CrIS instrument is a Fourier transform spectrometer with a total of 1305 NSR infrared sounding channels covering the longwave (655-1095 cm-1), midwave (1210-1750 cm-1), and shortwave (2155-2550 cm-1) spectral regions. The ATMS instrument  is a cross-track scanner with 22 channels in spectral bands from 23 GHz through 183 GHz.\n \nThe CHART algorithm is uses the  basic cloud clearing and retrieval methodologies used including the definition and derivation of Jacobians, the channel noise covariance matrix, and the use of constraints including the background term, are essentially identical to those of AIRS Version-6.6 and previous AIRS Science Team retrieval algorithms.  As with the Version-6.6 AIRS system, the CHART algorithm uses a Neural Network system as an initial guess. The sounding retrieval methodology characterizes the full atmospheric state and the retrievals contains a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; outgoing longwave radiation (OLR); and an infrared-based precipitation estimate.\n\n\nThe monthly one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products applying the specific quality control (QC) methodology to form a level-2 daily gridded product. Specific QC is defined per retrieved geophysical parameter at a given level within a profile. It accepts profile level data from the top of the atmosphere down to the level where the QC algorithm determines that the retrieval is good. Below this level, the data is rejected. This is the same methodology used by the AIRS Version 6 processing system. The daily level-3 gridded products are averaged to create the monthly average. \n\n\nThe CHART system was designed to serve as a seamless follow on to the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS/AMSU) instrument processing system. 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This product also provides standard deviations, quality assurance weighted means and other statistically derived quantities for each parameter. \n\nThe MYD08_D3 contains roughly 600 statistical datasets that are derived from approximately 80 scientific parameters from four Level-2 MODIS Atmosphere Products: MOD04_L2, MOD05_L2, MOD06_L2, and MOD07_L2. Statistics are computed over a 1 degree equal-angle lat-lon grid that spans a 24-hour (0000 to 2400 Greenwich Mean Time) interval. Since the grid cells are 1 degree by 1 degree, the output grid is always 360 pixels in width and 180 pixels in length.\n\nMYD08_D3 product files are stored in Hierarchical Data Format (HDF-EOS). Each gridded global parameter is stored as Scientific Data Sets (SDS) within the file. \n\nThe MODIS Daily Product will be used in the simultaneously study of clouds, water vapor, aerosol , trace gases, land surface and oceanic properties, as well as the interaction between them and their effect on the Earth's energy budget and climate. 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