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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":7.9832954,"_sort":[1788375398889,7.9832954,4,"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":10.205477,"_sort":[1788375386975,10.205477,0,"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":14.402391,"_sort":[1788375384045,14.402391,0,"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":11.408542,"_sort":[1788375380108,11.408542,0,"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":11.202911,"_sort":[1788375372830,11.202911,0,"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. The performance of public facilities can be examined in an interactive report at bit.ly/SFMunicipalBenchmarking, and annual reports from 2011-present are available there as well.\n \nThis dataset presents the energy performance and basic characteristics for public facilities that is visualized by the SFPUC\u2019s interactive report.  \n \nIn addition, energy performance data for non-municipal buildings (i.e. commercial buildings of 10,000 square feet or larger, and multifamily & mixed-use buildings of 50,000 square feet or larger) is available at: bit.ly/ExistingBuildingsReport\n\n<strong>B. HOW THE DATASET IS CREATED</strong>\nIn compliance with California Energy Benchmarking Regulations (CA Public Resources Code Section 25402.10 and CCR Title 20 Section 1680), and San Francisco Existing Buildings Energy Ordinance (Environment Code Chapter 20), the San Francisco Public Utilities Commission provides energy benchmarking services on behalf of municipal facilities. Details for public facilities are compiled from city records, and energy usage is compiled from utility records; related metrics such as energy use intensity are calculated from the combination of such records. Data is subjected to quality assurance validation prior to publication. For additional information regarding data sources and assumptions, please review the \"Data Sources and Assumptions\" page of the Municipal Facilities Energy Benchmarking dashboard: https://bit.ly/SFMunicipalBenchmarking.\n\n<strong>C. UPDATE PROCESS</strong>\nUpdated Annually.","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/k3fc-45qw/columns.json","describedByType":"application/json","downloadURL":"https://data.sf.gov/api/v3/views/k3fc-45qw/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/k3fc-45qw/columns.xml","describedByType":"application/xml","downloadURL":"https://data.sf.gov/api/v3/views/k3fc-45qw/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/k3fc-45qw/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"}],"identifier":"https://data.sf.gov/api/views/k3fc-45qw","issued":"2024-10-25","keyword":["climate change","energy","environment","sustainability"],"landingPage":"https://data.sf.gov/d/k3fc-45qw","license":"http://opendatacommons.org/licenses/pddl/1.0/","modified":"2026-03-20","publisher":{"@type":"org:Organization","name":"data.sf.gov"},"theme":["Energy and Environment"],"title":"[DEPRECATED] San Francisco Municipal Energy Benchmarking"},"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. The performance of public facilities can be examined in an interactive report at bit.ly/SFMunicipalBenchmarking, and annual reports from 2011-present are available there as well.\n \nThis dataset presents the energy performance and basic characteristics for public facilities that is visualized by the SFPUC\u2019s interactive report.  \n \nIn addition, energy performance data for non-municipal buildings (i.e. commercial buildings of 10,000 square feet or larger, and multifamily & mixed-use buildings of 50,000 square feet or larger) is available at: bit.ly/ExistingBuildingsReport\n\n<strong>B. HOW THE DATASET IS CREATED</strong>\nIn compliance with California Energy Benchmarking Regulations (CA Public Resources Code Section 25402.10 and CCR Title 20 Section 1680), and San Francisco Existing Buildings Energy Ordinance (Environment Code Chapter 20), the San Francisco Public Utilities Commission provides energy benchmarking services on behalf of municipal facilities. Details for public facilities are compiled from city records, and energy usage is compiled from utility records; related metrics such as energy use intensity are calculated from the combination of such records. Data is subjected to quality assurance validation prior to publication. For additional information regarding data sources and assumptions, please review the \"Data Sources and Assumptions\" page of the Municipal Facilities Energy Benchmarking dashboard: https://bit.ly/SFMunicipalBenchmarking.\n\n<strong>C. UPDATE PROCESS</strong>\nUpdated Annually.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/c0fbdaaf-1042-4dcd-9884-7b8a7c021926","harvest_record_raw":"https://catalog.data.gov/harvest_record/c0fbdaaf-1042-4dcd-9884-7b8a7c021926/raw","has_download":true,"has_spatial":false,"identifier":"https://data.sf.gov/api/views/k3fc-45qw","keyword":["climate change","energy","environment","sustainability"],"last_harvested_date":"2026-09-02T18:56:12.830800","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":"deprecated-san-francisco-municipal-energy-benchmarking","spatial_centroid":null,"spatial_shape":null,"theme":["Energy and Environment"],"title":"[DEPRECATED] San Francisco Municipal Energy Benchmarking","type":"dataset"},{"_score":26.516026,"_sort":[1788375350368,26.516026,3,"10888954-a747-4e51-a777-34b34c453c43"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"SFDPH Open Data","hasEmail":"mailto:no-reply@data.sf.gov"},"description":"The San Francisco Department of Public Health Flood Health Vulnerability Index is a composite index that measures the spatial distribution and relative vulnerability of San Francisco communities to the health impacts of flood inundation and extreme storms. The index is constructed using socioeconomic and demographic, exposure, health, and housing indicators and is intended to serve as a planning tool for health and climate adaptation. 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":[{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/cne3-h93g/columns.json","describedByType":"application/json","downloadURL":"https://data.sf.gov/api/v3/views/cne3-h93g/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/cne3-h93g/columns.xml","describedByType":"application/xml","downloadURL":"https://data.sf.gov/api/v3/views/cne3-h93g/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/cne3-h93g/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"}],"identifier":"https://data.sf.gov/api/views/cne3-h93g","issued":"2016-03-25","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"],"landingPage":"https://data.sf.gov/d/cne3-h93g","license":"http://opendatacommons.org/licenses/pddl/1.0/","modified":"2024-03-13","publisher":{"@type":"org:Organization","name":"data.sf.gov"},"theme":["Health and Social Services"],"title":"San Francisco Flood Health Vulnerability"},"description":"The San Francisco Department of Public Health Flood Health Vulnerability Index is a composite index that measures the spatial distribution and relative vulnerability of San Francisco communities to the health impacts of flood inundation and extreme storms. The index is constructed using socioeconomic and demographic, exposure, health, and housing indicators and is intended to serve as a planning tool for health and climate adaptation. 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":27.610256,"_sort":[1788375348420,27.610256,3,"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. 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_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/489ec520-c15c-4376-b979-dde4739fa34f","harvest_record_raw":"https://catalog.data.gov/harvest_record/489ec520-c15c-4376-b979-dde4739fa34f/raw","has_download":true,"has_spatial":false,"identifier":"https://data.sf.gov/api/views/btm4-e4ak","keyword":["carbon emissions","climate","climate change","communitywide","environment","ghg inventory","greenhouse gas emissions","san francisco climate action strategy","sustainability"],"last_harvested_date":"2026-09-02T18:55:48.420868","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-communitywide-greenhouse-gas-inventory","spatial_centroid":null,"spatial_shape":null,"theme":["Energy and Environment"],"title":"San Francisco Communitywide Greenhouse Gas Inventory","type":"dataset"},{"_score":31.463287,"_sort":[1788375346513,31.463287,1,"38967456-5c5d-4296-9b30-78b942d19476"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"SFDPH Open Data","hasEmail":"mailto:no-reply@data.sf.gov"},"description":"The Community Resiliency Indicator System was developed by  San Francisco's Climate and Health Program and is part of San Francisco's Climate and Health Profile. The system includes 40 indicators and an additive index which is a compilation of all of the indicators. See attached methods and project description documents for more details, you can also visit San Francisco's Climate and Health Profile website - www.sfclimatehealth.org (available Feb-2015)","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/banc-xdvr/columns.json","describedByType":"application/json","downloadURL":"https://data.sf.gov/api/v3/views/banc-xdvr/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/banc-xdvr/columns.xml","describedByType":"application/xml","downloadURL":"https://data.sf.gov/api/v3/views/banc-xdvr/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/banc-xdvr/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"}],"identifier":"https://data.sf.gov/api/views/banc-xdvr","issued":"2015-01-23","keyword":["@sfclimatehealth","community resiliency","community resiliency system","department of public health","dph","environmental health","phes","san francisco","san francisco indicator project","san francisco's climate & health program","sfclimatehealth.org","sfdph","sfphes"],"landingPage":"https://data.sf.gov/d/banc-xdvr","license":"http://opendatacommons.org/licenses/pddl/1.0/","modified":"2024-03-13","publisher":{"@type":"org:Organization","name":"data.sf.gov"},"theme":["Health and Social Services"],"title":"Community Resiliency Indicator System"},"description":"The Community Resiliency Indicator System was developed by  San Francisco's Climate and Health Program and is part of San Francisco's Climate and Health Profile. The system includes 40 indicators and an additive index which is a compilation of all of the indicators. See attached methods and project description documents for more details, you can also visit San Francisco's Climate and Health Profile website - www.sfclimatehealth.org (available Feb-2015)","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/a80678d6-13a2-4c68-89f2-1b2a3d9f4644","harvest_record_raw":"https://catalog.data.gov/harvest_record/a80678d6-13a2-4c68-89f2-1b2a3d9f4644/raw","has_download":true,"has_spatial":false,"identifier":"https://data.sf.gov/api/views/banc-xdvr","keyword":["@sfclimatehealth","community resiliency","community resiliency system","department of public health","dph","environmental health","phes","san francisco","san francisco indicator project","san francisco's climate & health program","sfclimatehealth.org","sfdph","sfphes"],"last_harvested_date":"2026-09-02T18:55:46.513061","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":1,"publisher":"data.sf.gov","slug":"community-resiliency-indicator-system","spatial_centroid":null,"spatial_shape":null,"theme":["Health and Social Services"],"title":"Community Resiliency Indicator System","type":"dataset"},{"_score":20.913893,"_sort":[1788375340650,20.913893,5,"49686a36-6f77-444c-aee4-3148f7781caf"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"OpenData","hasEmail":"mailto:no-reply@data.sf.gov"},"description":"Disclaimer: The Sea Level Rise (SLR) map shows the most extreme level of SLR possible. It is a very, very unlikely scenario that would only occur if no efforts to address SLR occur and both a King Tide and 100-year storm occur at the same time. The real purpose of the maps is to provide a broad net to help the City identify projects that that may be vulnerable. The data is based on what was in the ground as of 2010 and doesn\u2019t include piers. \r\n\r\nThe inundation maps and the associated analyses are intended as planning level tools to illustrate the potential for inundation and coastal flooding under a variety of future sea level rise and storm surge scenarios. The maps depict possible future inundation that could occur if nothing is done to adapt or prepare for sea level rise over the next century. The maps do not represent the exact location or depth of flooding. The maps relied on a 1-m digital elevation model created from LiDAR data collected in 2010 and 2011. Although care was taken to capture all relevant topographic features and coastal structures that may impact coastal inundation, it is possible that structures narrower than the 1-m horizontal map scale may not be fully represented. The maps are based on model outputs and do not account for all of the complex and dynamic San Francisco Bay processes or future conditions such as erosion, subsidence, future construction or shoreline protection upgrades, or other changes to San Francisco Bay or the region that may occur in response to sea level rise. 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 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":11.768966,"_sort":[1788310182195,11.768966,1,"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":10.606047,"_sort":[1788310180917,10.606047,1,"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":18.383678,"_sort":[1788310177708,18.383678,1,"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":41.27954,"_sort":[1788310177079,41.27954,1,"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":41.192577,"_sort":[1788310176758,41.192577,2,"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":42.855392,"_sort":[1788310176039,42.855392,0,"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. 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. 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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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This data set is not  meant to be used alone, but with the absolute dynamic topography data. These data were generated to help support the CMIP5 (Coupled Model Intercomparison Project Phase 5) portion of PCMDI (Program for Climate Model Diagnosis and Intercomparison).  The dynamic topograhy are from sea surface height measured by several satellites, Envisat, TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 and referenced to the geoid.  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Version 04 (v04) of the terrestrial water storage data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Additionally, this release 06.3 is an updated version of the Level 3 products in coordination with the release of the analogous Level 2 products used to generate them. It differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; see respective L-2 data descriptions. RL06.3 uses the ACX2-L1B data products. All GRACE-FO RL06.3 Level-3 fields are fully compatible with the GRACE RL06 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/C3193293825-POCLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/gracefo/open/docs/GRACE-FO_L3_Handbook_JPL.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3193293825-POCLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://github.com/podaac/data-subscriber","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://gracefo.jpl.nasa.gov/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://opendap.earthdata.nasa.gov","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://podaac.jpl.nasa.gov/CitingPODAAC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://podaac.jpl.nasa.gov/GRACE-FO","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://podaac.jpl.nasa.gov/Podaac/thumbnails/TELLUS_GRFO_L3_GFZ_RL063_LND_v04.jpg","format":"JPEG","mediaType":"image/jpeg"},{"@type":"dcat:Distribution","downloadURL":"https://podaac.jpl.nasa.gov/gravity/gracefo-documentation","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3193293825-POCLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/GFLND-3G634","keyword":["earth-science-water-budget-terrestrial-hydrosphere-terrestrial-water-storage"],"license":"https://www.usa.gov/government-works","modified":"2026-08-25","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/JPL/GRACE-TELLUS-0001;NASA/JPL/PODAAC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"SouthBoundingCoordinate\": -89.5, \"EastBoundingCoordinate\": 180, \"NorthBoundingCoordinate\": 89.5}]]","theme":["Earth Science"],"title":"GFZ TELLUS GRACE-FO Level-3 Monthly Land Water-Equivalent-Thickness Surface Mass Anomaly Release 6.3 version 04"},"description":"This data set is produced by the German Research Centre for Geosciences (GFZ) as part of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the terrestrial water storage anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. This quantity represents the total terrestrial water storage anomalies from soil moisture, snow, surface water (incl. rivers, lakes, reservoirs etc.), as well as groundwater and aquifers. A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Data grids are provided in ASCII/netCDF/GeoTIFF formats. \n\nGRACE-FO was launched on 22 May 2018, and extends the original GRACE mission (2002 \u2013 2017) and expands its legacy of scientific achievements in tracking earth surface mass changes. Version 04 (v04) of the terrestrial water storage data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Additionally, this release 06.3 is an updated version of the Level 3 products in coordination with the release of the analogous Level 2 products used to generate them. It differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; see respective L-2 data descriptions. RL06.3 uses the ACX2-L1B data products. All GRACE-FO RL06.3 Level-3 fields are fully compatible with the GRACE RL06 data.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/018f005c-34cb-4cd3-924e-fdf8a1d2f0b1","harvest_record_raw":"https://catalog.data.gov/harvest_record/018f005c-34cb-4cd3-924e-fdf8a1d2f0b1/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/GFLND-3G634","keyword":["earth-science-water-budget-terrestrial-hydrosphere-terrestrial-water-storage"],"last_harvested_date":"2026-09-02T00:48:38.985255","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":2,"publisher":"NASA/JPL/GRACE-TELLUS-0001;NASA/JPL/PODAAC","slug":"gfz-tellus-grace-fo-level-3-monthly-land-water-equivalent-thickness-surface-mass-anomal-04","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GFZ TELLUS GRACE-FO Level-3 Monthly Land Water-Equivalent-Thickness Surface Mass Anomaly Release 6.3 version 04","type":"dataset"},{"_score":8.006281,"_sort":[1788310118296,8.006281,2,"01b44601-78bb-4f6f-978b-0e0c7ae8956b"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"The H09-AHI-L2P-ACSPO-v2.90 dataset contains the Subskin Sea Surface Temperature (SST) produced by the NOAA ACSPO system from the Advanced Himawari Imager (AHI; largely identical to GOES-R/ABI) onboard the Himawari-9 (H09) satellite. The H09 is a Japanese weather satellite, the 9th of the Himawari geostationary weather satellite operated by the Japan Meteorological Agency. It was launched on November 2, 2016 into its nominal position at 140.7-deg E, and declared operational on December 13, 2022, replacing the Himawari-8. The AHI is the primary instrument on the Himawari Series for imaging Earth\u2019s weather, oceans, and environment with high temporal and spatial resolutions.  \n\nThe H08/AHI maps SST in a Full Disk (FD) area from 80E-160W and 60S-60N, with spatial resolution 2km at nadir to 15km/VZA (view zenith angle) 67-deg, and 10-min temporal sampling. The 10-min FD data are subsequently collated in time, to produce the 1-hr product, with improved coverage and reduced cloud leakages and image noise. The L2P data is produced in GHRSST compliant netCDF4 GDS2 format, with 24 granules per day, and a total data volume 1.2 GB/day. The near-real time (NRT) data are updated hourly, with several hours latency. The NRT files are replaced with Delayed Mode (DM) files, with a latency of approximately 2-months. File names remain unchanged, and DM vs NRT can be identified by different time stamps and global attributes inside the files (MERRA instead of GFS for atmospheric profiles, and same day CMC L4 analyses in DM instead of one-day delayed in NRT processing).  \n\nPixel earth locations are not reported in the granules, as they remain unchanged from granule to granule. Pixel locations  can be obtained using a flat lat/lon file or a Python script available via Documents tab from the dataset landing page. Climate and Forecast (CF) metadata aware software (e.g., Panoply, xarray) can detect and map the data as is via the granule CF projection attributes and variables. The ACSPO H09 HAI SSTs are validated against quality controlled in situ data from the NOAA iQuam system (Xu and Ignatov, 2014) and continuously monitored in the NOAA SQUAM system (Dash et al, 2010). A 0.02-deg equal-angle gridded L3C product 0.7GB/day) is available at https://podaac.jpl.nasa.gov/dataset/H09-AHI-L3C-ACSPO-v2.90","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/C2744808497-POCLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"http://www.ghrsst.org","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/ghrsst/open/data/GDS2/L2P/H09/STAR/docs/H09_140_7_E.nc","format":"NetCDF","mediaType":"application/netcdf"},{"@type":"dcat:Distribution","downloadURL":"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/ghrsst/open/data/GDS2/L2P/H09/STAR/docs/geo_nav.py","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/ghrsst/open/docs/GDS20r5.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C2744808497-POCLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://ghrsst.jpl.nasa.gov","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://github.com/podaac/data-readers","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://github.com/podaac/data-subscriber","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://opendap.earthdata.nasa.gov","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://podaac.jpl.nasa.gov/CitingPODAAC","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C2744808497-POCLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.star.nesdis.noaa.gov/sod/sst/iquam/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.star.nesdis.noaa.gov/sod/sst/squam/","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/GHH09-2P290","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"license":"https://www.usa.gov/government-works","modified":"2026-08-25","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"DOC/NOAA/NESDIS/STAR;NASA/JPL/PODAAC"},"spatial":"[\"CARTESIAN\", [{\"NorthBoundingCoordinate\": 60.0, \"WestBoundingCoordinate\": 80.0, \"EastBoundingCoordinate\": -160.0, \"SouthBoundingCoordinate\": -60.0}]]","theme":["Earth Science"],"title":"GHRSST L2P NOAA/ACSPO Himawari-09 AHI Pacific Ocean Region Sea Surface Temperature v2.90 dataset"},"description":"The H09-AHI-L2P-ACSPO-v2.90 dataset contains the Subskin Sea Surface Temperature (SST) produced by the NOAA ACSPO system from the Advanced Himawari Imager (AHI; largely identical to GOES-R/ABI) onboard the Himawari-9 (H09) satellite. The H09 is a Japanese weather satellite, the 9th of the Himawari geostationary weather satellite operated by the Japan Meteorological Agency. It was launched on November 2, 2016 into its nominal position at 140.7-deg E, and declared operational on December 13, 2022, replacing the Himawari-8. The AHI is the primary instrument on the Himawari Series for imaging Earth\u2019s weather, oceans, and environment with high temporal and spatial resolutions.  \n\nThe H08/AHI maps SST in a Full Disk (FD) area from 80E-160W and 60S-60N, with spatial resolution 2km at nadir to 15km/VZA (view zenith angle) 67-deg, and 10-min temporal sampling. The 10-min FD data are subsequently collated in time, to produce the 1-hr product, with improved coverage and reduced cloud leakages and image noise. The L2P data is produced in GHRSST compliant netCDF4 GDS2 format, with 24 granules per day, and a total data volume 1.2 GB/day. The near-real time (NRT) data are updated hourly, with several hours latency. The NRT files are replaced with Delayed Mode (DM) files, with a latency of approximately 2-months. File names remain unchanged, and DM vs NRT can be identified by different time stamps and global attributes inside the files (MERRA instead of GFS for atmospheric profiles, and same day CMC L4 analyses in DM instead of one-day delayed in NRT processing).  \n\nPixel earth locations are not reported in the granules, as they remain unchanged from granule to granule. Pixel locations  can be obtained using a flat lat/lon file or a Python script available via Documents tab from the dataset landing page. Climate and Forecast (CF) metadata aware software (e.g., Panoply, xarray) can detect and map the data as is via the granule CF projection attributes and variables. The ACSPO H09 HAI SSTs are validated against quality controlled in situ data from the NOAA iQuam system (Xu and Ignatov, 2014) and continuously monitored in the NOAA SQUAM system (Dash et al, 2010). A 0.02-deg equal-angle gridded L3C product 0.7GB/day) is available at https://podaac.jpl.nasa.gov/dataset/H09-AHI-L3C-ACSPO-v2.90","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/db529086-2f16-4609-95f4-c73ec4347bf3","harvest_record_raw":"https://catalog.data.gov/harvest_record/db529086-2f16-4609-95f4-c73ec4347bf3/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/GHH09-2P290","keyword":["earth-science-ocean-temperature-oceans-sea-surface-temperature"],"last_harvested_date":"2026-09-02T00:48:38.296404","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":2,"publisher":"DOC/NOAA/NESDIS/STAR;NASA/JPL/PODAAC","slug":"ghrsst-l2p-noaa-acspo-himawari-09-ahi-pacific-ocean-region-sea-surface-temperature-v2-90-d","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GHRSST L2P NOAA/ACSPO Himawari-09 AHI Pacific Ocean Region Sea Surface Temperature v2.90 dataset","type":"dataset"},{"_score":6.1290054,"_sort":[1788310117331,6.1290054,1,"65d6b691-a339-44f1-ac0a-66c2eebe7d44"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"Version 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07.\n\nThe Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team.\n\nThe precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2021 version of the Goddard Profiling Algorithm (GPROF2021), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1\u00b0x0.1\u00b0 (roughly 10x10 km) fields. Note that CORRA is adjusted to the monthly Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) product over high-latitude ocean to correct known biases.\n\nThe half-hourly intercalibrated merged PMW estimates are then input to both a Morphing-Kalman Filter (KF) Lagrangian time interpolation scheme based on work by the Climate Prediction Center (CPC) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Dynamic Infrared\u2013Rain Rate (PDIR) re-calibration scheme. In parallel, CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR fields and forwards them to PPS for input to the PERSIANN-CCS algorithm (supported by an asynchronous re-calibration cycle) which are then input to the KF morphing (quasi-Lagrangian time interpolation) scheme.\n\nThe KF morphing (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates. Motion vectors for the morphing are computed by maximizing the pattern correlation of successive hours within each of the precipitation (PRECTOT), total precipitable liquid water (TQL), and vertically integrated vapor (TQV) data fields provided by the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Goddard Earth Observing System model Version 5 (GEOS-5) Forward Processing (FP) for the post-real-time (Final) Run and the near-real-time (Early and Late) Runs, respectively. The vectors from PRECTOT are chosen if available, else from TQL, if available, else from TQV. The KF uses the morphed data as the \u201cforecast\u201d and the IR estimates as the \u201cobservations\u201d, with weighting that depends on the time interval(s) away from the microwave overpass time. The IR becomes important after about \u00b190 minutes away from the overpass time. Variable averaging in the KF is accounted for in a routine (Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood, or SHARPEN) that compares the local histogram of KF morphed precipitation to the local histogram of forward- and backward-morphed microwave data and the IR.\n\nThe IMERG system is run twice in near-real time:\n\n\"Early\" multi-satellite product ~4 hr after observation time using only forward morphing and\n\"Late\" multi-satellite product ~14 hr after observation time, using both forward and backward morphing\nand once after the monthly gauge analysis is received:\n\n\"Final\", satellite-gauge product ~4 months after the observation month, using both forward and backward morphing and including monthly gauge analyses.\n\nIn V07, the near-real-time Early and Late half-hourly estimates have a monthly climatological concluding calibration based on averaging the concluding calibrations computed in the Final, while in the post-real-time Final Run the multi-satellite half-hourly estimates are adjusted so that they sum to the Final Run monthly satellite-gauge combination. In all cases the output contains multiple fields that provide information on the input data, selected intermediate fields, and estimation quality. In general, the complete calibrated precipitation, precipitation, is the data field of choice for most users.\n\nPrecipitation phase is a diagnostic variable computed using analyses of surface temperature, humidity, and pressure.","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/C2723754845-GES_DISC.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/IMERG_TechnicalDocumentation_final.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://arthurhou.pps.eosdis.nasa.gov/Documents/IMERG_V07_ATBD_final.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C2723754845-GES_DISC/temporal","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/GPM_3IMERGHHL_07.html","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERGV06_QI.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/MorphingInV06IMERG.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/browse/GPM_3IMERGHHL_07.png","format":"PNG","mediaType":"image/png"},{"@type":"dcat:Distribution","downloadURL":"https://giovanni.gsfc.nasa.gov/#dataKeyword=IMERGHHL","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://gpm.nasa.gov","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://gpm.nasa.gov/resources/documents/imerg-v07-release-notes","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://gpm1.gesdisc.eosdis.nasa.gov/data/GPM_L3/doc/README.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=C2723754845-GES_DISC","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/GPM/IMERG/3B-HH-L/07","keyword":["earth-science-precipitation-atmosphere","earth-science-precipitation-atmosphere-liquid-precipitation","earth-science-precipitation-atmosphere-precipitation-amount","earth-science-precipitation-atmosphere-precipitation-rate","earth-science-precipitation-atmosphere-solid-precipitation"],"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}]]","theme":["Earth Science"],"title":"GPM IMERG Late Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07 (GPM_3IMERGHHL)"},"description":"Version 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07.\n\nThe Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team.\n\nThe precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2021 version of the Goddard Profiling Algorithm (GPROF2021), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1\u00b0x0.1\u00b0 (roughly 10x10 km) fields. Note that CORRA is adjusted to the monthly Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) product over high-latitude ocean to correct known biases.\n\nThe half-hourly intercalibrated merged PMW estimates are then input to both a Morphing-Kalman Filter (KF) Lagrangian time interpolation scheme based on work by the Climate Prediction Center (CPC) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Dynamic Infrared\u2013Rain Rate (PDIR) re-calibration scheme. In parallel, CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR fields and forwards them to PPS for input to the PERSIANN-CCS algorithm (supported by an asynchronous re-calibration cycle) which are then input to the KF morphing (quasi-Lagrangian time interpolation) scheme.\n\nThe KF morphing (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates. Motion vectors for the morphing are computed by maximizing the pattern correlation of successive hours within each of the precipitation (PRECTOT), total precipitable liquid water (TQL), and vertically integrated vapor (TQV) data fields provided by the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Goddard Earth Observing System model Version 5 (GEOS-5) Forward Processing (FP) for the post-real-time (Final) Run and the near-real-time (Early and Late) Runs, respectively. The vectors from PRECTOT are chosen if available, else from TQL, if available, else from TQV. The KF uses the morphed data as the \u201cforecast\u201d and the IR estimates as the \u201cobservations\u201d, with weighting that depends on the time interval(s) away from the microwave overpass time. The IR becomes important after about \u00b190 minutes away from the overpass time. Variable averaging in the KF is accounted for in a routine (Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood, or SHARPEN) that compares the local histogram of KF morphed precipitation to the local histogram of forward- and backward-morphed microwave data and the IR.\n\nThe IMERG system is run twice in near-real time:\n\n\"Early\" multi-satellite product ~4 hr after observation time using only forward morphing and\n\"Late\" multi-satellite product ~14 hr after observation time, using both forward and backward morphing\nand once after the monthly gauge analysis is received:\n\n\"Final\", satellite-gauge product ~4 months after the observation month, using both forward and backward morphing and including monthly gauge analyses.\n\nIn V07, the near-real-time Early and Late half-hourly estimates have a monthly climatological concluding calibration based on averaging the concluding calibrations computed in the Final, while in the post-real-time Final Run the multi-satellite half-hourly estimates are adjusted so that they sum to the Final Run monthly satellite-gauge combination. In all cases the output contains multiple fields that provide information on the input data, selected intermediate fields, and estimation quality. In general, the complete calibrated precipitation, precipitation, is the data field of choice for most users.\n\nPrecipitation phase is a diagnostic variable computed using analyses of surface temperature, humidity, and pressure.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/88bf7d18-9324-428a-a669-2ea2be17d03c","harvest_record_raw":"https://catalog.data.gov/harvest_record/88bf7d18-9324-428a-a669-2ea2be17d03c/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/GPM/IMERG/3B-HH-L/07","keyword":["earth-science-precipitation-atmosphere","earth-science-precipitation-atmosphere-liquid-precipitation","earth-science-precipitation-atmosphere-precipitation-amount","earth-science-precipitation-atmosphere-precipitation-rate","earth-science-precipitation-atmosphere-solid-precipitation"],"last_harvested_date":"2026-09-02T00:48:37.331309","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-imerg-late-precipitation-l3-half-hourly-0-1-degree-x-0-1-degree-v07-gpm_3imerghhl-at-g","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"GPM IMERG Late Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07 (GPM_3IMERGHHL)","type":"dataset"},{"_score":41.5015,"_sort":[1788310116568,41.5015,2,"cf06ebcd-1e09-474a-9a33-95375463e2f7"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07. \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. 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Older versions are no longer available and have been superseded by Version 07. \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\n3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. 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