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Updated datasets 2025-01-07 UTC
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2 changes: 1 addition & 1 deletion datasets/COPERNICUS_S1_GRD.json
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"interval": [
[
"2014-10-03T00:00:00Z",
"2025-01-05T18:21:57Z"
"2025-01-06T18:36:34Z"
]
]
}
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2 changes: 1 addition & 1 deletion datasets/COPERNICUS_S2.json
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"interval": [
[
"2015-06-27T00:00:00Z",
"2025-01-05T19:58:03Z"
"2025-01-06T20:00:33Z"
]
]
}
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2 changes: 1 addition & 1 deletion datasets/COPERNICUS_S2_CLOUD_PROBABILITY.json
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"interval": [
[
"2015-06-27T00:00:00Z",
"2025-01-05T19:10:41Z"
"2025-01-06T19:58:11Z"
]
]
}
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2 changes: 1 addition & 1 deletion datasets/COPERNICUS_S2_HARMONIZED.json
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"interval": [
[
"2015-06-27T00:00:00Z",
"2025-01-05T17:18:15Z"
"2025-01-06T20:00:33Z"
]
]
}
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2 changes: 1 addition & 1 deletion datasets/COPERNICUS_S2_SR.json
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"interval": [
[
"2017-03-28T00:00:00Z",
"2025-01-05T19:57:51Z"
"2025-01-06T18:17:46Z"
]
]
}
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2 changes: 1 addition & 1 deletion datasets/COPERNICUS_S2_SR_HARMONIZED.json
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"interval": [
[
"2017-03-28T00:00:00Z",
"2025-01-05T16:23:12Z"
"2025-01-06T18:17:46Z"
]
]
}
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2 changes: 1 addition & 1 deletion datasets/COPERNICUS_S3_OLCI.json
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"interval": [
[
"2016-10-18T19:25:42Z",
"2025-01-04T09:24:44Z"
"2025-01-05T08:34:33Z"
]
]
}
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2 changes: 1 addition & 1 deletion datasets/COPERNICUS_S5P_NRTI_L3_AER_AI.json
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"interval": [
[
"2018-07-10T11:17:44Z",
"2025-01-05T09:14:08Z"
"2025-01-06T09:14:08Z"
]
]
}
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2 changes: 1 addition & 1 deletion datasets/COPERNICUS_S5P_NRTI_L3_AER_LH.json
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"interval": [
[
"2018-07-10T11:17:44Z",
"2025-01-05T08:19:08Z"
"2025-01-06T09:04:09Z"
]
]
}
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2 changes: 1 addition & 1 deletion datasets/COPERNICUS_S5P_NRTI_L3_CLOUD.json
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"interval": [
[
"2018-07-05T23:24:16Z",
"2025-01-05T09:14:08Z"
"2025-01-06T09:29:09Z"
]
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}
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2 changes: 1 addition & 1 deletion datasets/COPERNICUS_S5P_NRTI_L3_CO.json
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"interval": [
[
"2018-11-22T12:00:13Z",
"2025-01-05T09:14:08Z"
"2025-01-06T08:59:09Z"
]
]
}
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2 changes: 1 addition & 1 deletion datasets/COPERNICUS_S5P_NRTI_L3_HCHO.json
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"interval": [
[
"2018-10-02T07:58:03Z",
"2025-01-05T08:19:08Z"
"2025-01-06T09:19:08Z"
]
]
}
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2 changes: 1 addition & 1 deletion datasets/COPERNICUS_S5P_NRTI_L3_NO2.json
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"interval": [
[
"2018-07-10T10:05:44Z",
"2025-01-05T08:19:08Z"
"2025-01-06T09:19:08Z"
]
]
}
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2 changes: 1 addition & 1 deletion datasets/COPERNICUS_S5P_NRTI_L3_O3.json
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"interval": [
[
"2018-07-10T11:02:44Z",
"2025-01-05T09:10:36Z"
"2025-01-06T09:24:08Z"
]
]
}
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2 changes: 1 addition & 1 deletion datasets/COPERNICUS_S5P_NRTI_L3_SO2.json
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"interval": [
[
"2018-07-10T11:17:44Z",
"2025-01-05T08:14:08Z"
"2025-01-06T09:19:08Z"
]
]
}
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2 changes: 1 addition & 1 deletion datasets/COPERNICUS_S5P_OFFL_L3_AER_AI.json
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"interval": [
[
"2018-07-04T13:34:21Z",
"2025-01-03T19:57:34Z"
"2025-01-04T16:15:34Z"
]
]
}
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2 changes: 1 addition & 1 deletion datasets/COPERNICUS_S5P_OFFL_L3_AER_LH.json
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"interval": [
[
"2018-07-04T13:34:21Z",
"2025-01-03T13:11:34Z"
"2025-01-04T12:52:36Z"
]
]
}
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2 changes: 1 addition & 1 deletion datasets/COPERNICUS_S5P_OFFL_L3_CH4.json
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"interval": [
[
"2019-02-08T08:13:16Z",
"2025-01-03T06:25:34Z"
"2025-01-04T04:25:05Z"
]
]
}
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2 changes: 1 addition & 1 deletion datasets/COPERNICUS_S5P_OFFL_L3_CLOUD.json
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"interval": [
[
"2018-07-04T11:34:21Z",
"2025-01-03T18:16:05Z"
"2025-01-04T17:57:05Z"
]
]
}
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2 changes: 1 addition & 1 deletion datasets/COPERNICUS_S5P_OFFL_L3_CO.json
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"interval": [
[
"2018-06-28T10:24:07Z",
"2025-01-03T18:16:05Z"
"2025-01-04T12:52:36Z"
]
]
}
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2 changes: 1 addition & 1 deletion datasets/COPERNICUS_S5P_OFFL_L3_HCHO.json
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"interval": [
[
"2018-12-05T12:14:36Z",
"2025-01-03T13:11:34Z"
"2025-01-04T17:57:05Z"
]
]
}
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2 changes: 1 addition & 1 deletion datasets/COPERNICUS_S5P_OFFL_L3_NO2.json
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"interval": [
[
"2018-06-28T10:24:07Z",
"2024-12-27T22:10:33Z"
"2024-12-28T20:10:03Z"
]
]
}
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2 changes: 1 addition & 1 deletion datasets/COPERNICUS_S5P_OFFL_L3_O3.json
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"interval": [
[
"2018-09-08T21:19:29Z",
"2025-01-03T09:48:35Z"
"2025-01-04T01:02:04Z"
]
]
}
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2 changes: 1 addition & 1 deletion datasets/COPERNICUS_S5P_OFFL_L3_SO2.json
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"interval": [
[
"2018-12-05T11:53:01Z",
"2025-01-03T09:48:35Z"
"2025-01-04T11:11:05Z"
]
]
}
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4 changes: 2 additions & 2 deletions datasets/ECMWF_ERA5_LAND_DAILY_AGGR.json
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"type": "Collection",
"id": "ECMWF/ERA5_LAND/DAILY_AGGR",
"stac_version": "1.0.0",
"description": "ERA5-Land is a reanalysis dataset providing a consistent view of the\nevolution of land variables over several decades at an enhanced resolution\ncompared to ERA5. ERA5-Land has been produced by replaying the land\ncomponent of the ECMWF ERA5 climate reanalysis. Reanalysis combines model\ndata with observations from across the world into a globally complete and\nconsistent dataset using the laws of physics. Reanalysis produces data that\ngoes several decades back in time, providing an accurate description of the\nclimate of the past. This dataset includes all 50 variables as available on\nCDS.\n\nThe asset is a daily aggregate of ECMWF ERA5 Land hourly assets which\nincludes both flow and non-flow bands. Flow bands are formed by collecting\nthe first hour's data of the following day which holds aggregated sum of\nprevious day and while the non-flow bands are created by averaging all\nhourly data of the day. The flow bands are labeled with the \"_sum\"\nidentifier, which approach is different from the daily data produced by\nCopernicus Climate Data Store, where flow bands are averaged\ntoo.\n\nDaily aggregates have been pre-calculated to facilitate many applications\nrequiring easy and fast access to the data.\n\nERA5-Land daily aggregated data is available from 1950 to three months\nfrom real-time. More information can be found at the\n[Copernicus Climate Data Store](https://cds.climate.copernicus.eu).\n\nPrecipitation and other flow (accumulated) bands might occasionally have\nnegative values, which doesn't make physical sense. At other times their values\nmight be excessively high.\n\nThis problem is due to how the GRIB format saves data: it simplifies or \"packs\"\nthe data into smaller, less precise numbers, which can introduce errors. These\nerrors get worse when the data varies a lot.\n\nBecause of this, when we look at the data for a whole day to compute daily\ntotals, sometimes the highest amount of rainfall recorded at one time can seem\nlarger than the total rainfall measured for the entire day.\n\nTo learn more, Please see:\n[\"Why are there sometimes small negative precipitation accumulations\"](https://confluence.ecmwf.int/display/UDOC/Why+are+there+sometimes+small+negative+precipitation+accumulations+-+ecCodes+GRIB+FAQ)",
"description": "ERA5-Land is a reanalysis dataset providing a consistent view of the\nevolution of land variables over several decades at an enhanced resolution\ncompared to ERA5. ERA5-Land has been produced by replaying the land\ncomponent of the ECMWF ERA5 climate reanalysis. Reanalysis combines model\ndata with observations from across the world into a globally complete and\nconsistent dataset using the laws of physics. Reanalysis produces data that\ngoes several decades back in time, providing an accurate description of the\nclimate of the past. This dataset includes all 50 variables as available on\nCDS.\n\nERA5-Land data is available from 1950 to three months from real-time.\n\nPlease consult [the ERA5-Land \"Known Issues\" section](https://confluence.ecmwf.int/display/CKB/ERA5-Land%3A+data+documentation). In particular, note that three components of the total evapotranspiration have values swapped as follows:\n\n* variable \"Evaporation from bare soil\" (mars parameter code 228101 (evabs)) has the values corresponding to the \"Evaporation from vegetation transpiration\" (mars parameter 228103 (evavt)),\n* variable \"Evaporation from open water surfaces excluding oceans (mars parameter code 228102 (evaow)) has the values corresponding to the \"Evaporation from bare soil\" (mars parameter code 228101 (evabs)),\n* variable \"Evaporation from vegetation transpiration\" (mars parameter code 228103 (evavt)) has the values corresponding to the \"Evaporation from open water surfaces excluding oceans\" (mars parameter code 228102 (evaow)).\n\n\nThe asset is a daily aggregate of ECMWF ERA5 Land hourly assets which\nincludes both flow and non-flow bands. Flow bands are formed by collecting\nthe first hour's data of the following day which holds aggregated sum of\nprevious day and while the non-flow bands are created by averaging all\nhourly data of the day. The flow bands are labeled with the \"_sum\"\nidentifier, which approach is different from the daily data produced by\nCopernicus Climate Data Store, where flow bands are averaged\ntoo.\n\nDaily aggregates have been pre-calculated to facilitate many applications\nrequiring easy and fast access to the data.\n\nPrecipitation and other flow (accumulated) bands might occasionally have\nnegative values, which doesn't make physical sense. At other times their values\nmight be excessively high.\n\nThis problem is due to how the GRIB format saves data: it simplifies or \"packs\"\nthe data into smaller, less precise numbers, which can introduce errors. These\nerrors get worse when the data varies a lot.\n\nBecause of this, when we look at the data for a whole day to compute daily\ntotals, sometimes the highest amount of rainfall recorded at one time can seem\nlarger than the total rainfall measured for the entire day.\n\nTo learn more, Please see:\n[\"Why are there sometimes small negative precipitation accumulations\"](https://confluence.ecmwf.int/display/UDOC/Why+are+there+sometimes+small+negative+precipitation+accumulations+-+ecCodes+GRIB+FAQ)",
"links": [
{
"rel": "root",
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"interval": [
[
"1950-01-02T00:00:00Z",
"2024-12-29T00:00:00Z"
"2024-12-30T00:00:00Z"
]
]
}
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4 changes: 2 additions & 2 deletions datasets/ECMWF_ERA5_LAND_HOURLY.json
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"type": "Collection",
"id": "ECMWF/ERA5_LAND/HOURLY",
"stac_version": "1.0.0",
"description": "ERA5-Land is a reanalysis dataset providing a consistent view of the\nevolution of land variables over several decades at an enhanced resolution\ncompared to ERA5. ERA5-Land has been produced by replaying the land\ncomponent of the ECMWF ERA5 climate reanalysis. Reanalysis combines model\ndata with observations from across the world into a globally complete and\nconsistent dataset using the laws of physics. Reanalysis produces data that\ngoes several decades back in time, providing an accurate description of the\nclimate of the past. This dataset includes all 50 variables as available on\nCDS.\n\nPlease note that the convention for accumulations used in ERA5-Land differs\nwith that for ERA5. The accumulations are treated the same as those in\nERA-Interim or ERA-Interim/Land, i.e., they are accumulated from the\nbeginning of the forecast to the end of the forecast step. This happens\nwithin every day and gets reset on midnight. The Earth Engine Data team\nadded 19 additional bands,\none for each of the accumulation bands, with the hourly values computed as\nthe difference between two consecutive forecast steps.\n\nERA5-Land data is available from 1950 to three months from real-time. More\ninformation can be found at the\n[Copernicus Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview).\n",
"description": "ERA5-Land is a reanalysis dataset providing a consistent view of the\nevolution of land variables over several decades at an enhanced resolution\ncompared to ERA5. ERA5-Land has been produced by replaying the land\ncomponent of the ECMWF ERA5 climate reanalysis. Reanalysis combines model\ndata with observations from across the world into a globally complete and\nconsistent dataset using the laws of physics. Reanalysis produces data that\ngoes several decades back in time, providing an accurate description of the\nclimate of the past. This dataset includes all 50 variables as available on\nCDS.\n\nERA5-Land data is available from 1950 to three months from real-time.\n\nPlease consult [the ERA5-Land \"Known Issues\" section](https://confluence.ecmwf.int/display/CKB/ERA5-Land%3A+data+documentation). In particular, note that three components of the total evapotranspiration have values swapped as follows:\n\n* variable \"Evaporation from bare soil\" (mars parameter code 228101 (evabs)) has the values corresponding to the \"Evaporation from vegetation transpiration\" (mars parameter 228103 (evavt)),\n* variable \"Evaporation from open water surfaces excluding oceans (mars parameter code 228102 (evaow)) has the values corresponding to the \"Evaporation from bare soil\" (mars parameter code 228101 (evabs)),\n* variable \"Evaporation from vegetation transpiration\" (mars parameter code 228103 (evavt)) has the values corresponding to the \"Evaporation from open water surfaces excluding oceans\" (mars parameter code 228102 (evaow)).\n\n\nPlease note that the convention for accumulations used in ERA5-Land differs\nwith that for ERA5. The accumulations are treated the same as those in\nERA-Interim or ERA-Interim/Land, i.e., they are accumulated from the\nbeginning of the forecast to the end of the forecast step. This happens\nwithin every day and gets reset on midnight. The Earth Engine Data team\nadded 19 additional bands,\none for each of the accumulation bands, with the hourly values computed as\nthe difference between two consecutive forecast steps.\n",
"links": [
{
"rel": "root",
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"interval": [
[
"1950-01-01T01:00:00Z",
"2024-12-30T23:00:00Z"
"2024-12-31T23:00:00Z"
]
]
}
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2 changes: 1 addition & 1 deletion datasets/ECMWF_ERA5_LAND_MONTHLY.json
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"type": "Collection",
"id": "ECMWF/ERA5_LAND/MONTHLY",
"stac_version": "1.0.0",
"description": "ERA5-Land is a reanalysis dataset providing a consistent view of the\nevolution of land variables over several decades at an enhanced resolution\ncompared to ERA5. ERA5-Land has been produced by replaying the land\ncomponent of the ECMWF ERA5 climate reanalysis. Reanalysis combines model\ndata with observations from across the world into a globally complete and\nconsistent dataset using the laws of physics. Reanalysis produces data that\ngoes several decades back in time, providing an accurate description of the\nclimate of the past. This dataset includes all 50 variables as available on\nCDS.\n\nThe data presented here is a subset of the full ERA5-Land dataset\npost-processed by ECMWF. Monthly-mean averages have been pre-calculated to\nfacilitate many applications requiring easy and fast access to the data,\nwhen sub-monthly fields are not required.\n\nERA5-Land data is available from 1950 to three months from real-time. More\ninformation can be found at the\n[Copernicus Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means?tab=overview).\n",
"description": "ERA5-Land is a reanalysis dataset providing a consistent view of the\nevolution of land variables over several decades at an enhanced resolution\ncompared to ERA5. ERA5-Land has been produced by replaying the land\ncomponent of the ECMWF ERA5 climate reanalysis. Reanalysis combines model\ndata with observations from across the world into a globally complete and\nconsistent dataset using the laws of physics. Reanalysis produces data that\ngoes several decades back in time, providing an accurate description of the\nclimate of the past. This dataset includes all 50 variables as available on\nCDS.\n\nERA5-Land data is available from 1950 to three months from real-time.\n\nPlease consult [the ERA5-Land \"Known Issues\" section](https://confluence.ecmwf.int/display/CKB/ERA5-Land%3A+data+documentation). In particular, note that three components of the total evapotranspiration have values swapped as follows:\n\n* variable \"Evaporation from bare soil\" (mars parameter code 228101 (evabs)) has the values corresponding to the \"Evaporation from vegetation transpiration\" (mars parameter 228103 (evavt)),\n* variable \"Evaporation from open water surfaces excluding oceans (mars parameter code 228102 (evaow)) has the values corresponding to the \"Evaporation from bare soil\" (mars parameter code 228101 (evabs)),\n* variable \"Evaporation from vegetation transpiration\" (mars parameter code 228103 (evavt)) has the values corresponding to the \"Evaporation from open water surfaces excluding oceans\" (mars parameter code 228102 (evaow)).\n\n\nThe data presented here is a subset of the full ERA5-Land dataset\npost-processed by ECMWF. Monthly-mean averages have been pre-calculated to\nfacilitate many applications requiring easy and fast access to the data,\nwhen sub-monthly fields are not required.\n\nERA5-Land data is available from 1950 to three months from real-time. More\ninformation can be found at the\n[Copernicus Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means?tab=overview).\n",
"links": [
{
"rel": "root",
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