From 1 - 10 / 11
  • Overview: era5.copernicus: precipitation daily sums from 2000 to 2020 resampled with CHELSA to 1 km resolution Traceability (lineage): The data sources used to generate this dataset are ERA5-Land hourly data from 1950 to present (Copernicus Climate Data Store) and CHELSA monthly climatologies. Scientific methodology: The methodology used for downscaling follows established procedures as used by e.g. Worldclim and CHELSA. Usability: The substantial improvement of the spatial resolution together with the high temporal resolution of one day further improve the usability of the original ERA5 Land time series product which is useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states. Uncertainty quantification: The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. Data validation approaches: Validation of the ERA5 Land ddataset against multiple in-situ datasets is presented in the reference paper (Muñoz-Sabater et al., 2021). Completeness: The dataset covers the entire Geo-harmonizer region as defined by the landmask raster dataset. However, some small islands might be missing if there are no data in the original ERA5 Land dataset. Consistency: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Positional accuracy: 1 km spatial resolution Temporal accuracy: Daily maps for the years 2020-2020. Thematic accuracy: The raster values represent cumulative daily precipitation in mm x 10.

  • Overview: ems.copernicus: flood activations observed event delimitations from 2012 to 2020 resampled to 30 m Traceability (lineage): The data source used to produce this dataset is the Copernicus Emergency Management Service, specifically the Rapid Mapping component. All available information has been used, from the activation of the service in 2012 untill 2020, selecting data tagged within the flood category. For obtaining the rasters, the authors have used the delineation map product provided by EMS, when available. When not available, the First Estimate Product (FEP) was considered. According to the JRC Technical Report "Manual for CEMS-Rapid Mapping Products", the delineation product "provides an assessment of the event impact and extent and, if requested, an update of the situation (monitoring). They are derived from images acquired as soon as possible after the emergency event." The first estimate product is an early information product which aims at providing an extremely fast (yet rough) assessment of most affected locations within the area of interest. For the processing, the vector packages prepared for each of the EMS activation have been used, more specifically the "crisis information layers" detailed here: https://emergency.copernicus.eu/mapping/ems/group-b-crisis-information-layer . The main document detailing the methdology to obtain the EMS activation products is the JRC Technical Report "Manual for CEMS-Rapid Mapping Products" 2020, by Joubert-Boitat Inès, Wania Annett and Dalmasso Simone. Scientific methodology: The Copernicus EMS Rapid Mapping methodology of extracting disaster related information is detailed in the 'Manual CEMS for Rapid Mapping Products', published in August 2020 by Joubert-Boitat Inès, Wania Annett, Dalmasso Simone. The documentation is valid from April 2019 and it extends on the work published in 2018 "Product User Manual of Copernicus EMS Rapid Mapping” by Dorati et al. The Geo-harmonizer obtained product is represented by a series of rasters, one raster per each season, per each year from 2012 to 2020. In seasons where no flood activation has been registered, the raster has not been produced. For each individual raster, all flood activations relevant vector data packages have been downloaded, processed - by automatically extracting the "crisis information layers" rasterised and resampled at 30 m and mosaicked. Therefore, each raster layer - representing one season/year - stores value 100 for each pixel that has been covered by water at some point within the 3 months of the season. Usability: Given the methodology that implies VHR data and other data sources to detect flooded areas, the data product obtined is useful especially in the context of validation of other flood products, obtained through automatic means. Uncertainty quantification: Given the methodology that implies VHR data and other data sources to detect flooded areas, the data product obtined is useful especially in the context of validation of other flood products, obtained through automatic means. Data validation approaches: n/a Completeness: The CEMS Rapid Mapping functiones based on event triggered by an external entitiy - CEMS Authorised Member - therefore the dataset can not be evaluated for completeness. Consistency: The rasters are obtained by processing the CEMS vector data, that presented a series of conceptual inconsistencies documented here https://docs.google.com/document/d/1b0XPH8EagA8VQnlnNHmbQ64BWXIoz1WZUSb1ppcEjxM/edit Positional accuracy: The raster files perfectly cover the entire Geo-harmonizer region as defined by the landmask raster dataset available here [source]. For facilitating the user's interaction with the map product, an additional point vector layer has been prepared containing information on the time of activation and the Emergency Rapid Mapping Service code assigned and link to the official Copernicus Emergency Rapid Mapping Service webpage where all the information on the event has been published, making it easy to quickly identify all relevant information on the specific flood activation. The point layer has been derived as the geometric center of the polygon delineating the flood activation. Temporal accuracy: The Copernicus Emergency Service - Rapid Mapping service is activated based on date-precise triggering of Authorised Members, information that is clearly identified in the data processing. Thematic accuracy: The rasters have 2 values: 0 for non-flooded area and 100 for flooded area.

  • Overview: era5.copernicus: surface temperature daily maxima from 2000 to 2020 resampled with CHELSA to 1 km resolution Traceability (lineage): The data sources used to generate this dataset are ERA5-Land hourly data from 1950 to present (Copernicus Climate Data Store) and CHELSA monthly climatologies. Scientific methodology: The methodology used for downscaling follows established procedures as used by e.g. Worldclim and CHELSA. Usability: The substantial improvement of the spatial resolution together with the high temporal resolution of one day further improve the usability of the original ERA5 Land time series product which is useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states. Uncertainty quantification: The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. Data validation approaches: Validation of the ERA5 Land ddataset against multiple in-situ datasets is presented in the reference paper (Muñoz-Sabater et al., 2021). Completeness: The dataset covers the entire Geo-harmonizer region as defined by the landmask raster dataset. However, some small islands might be missing if there are no data in the original ERA5 Land dataset. Consistency: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Positional accuracy: 1 km spatial resolution Temporal accuracy: Daily maps for the years 2020-2020. Thematic accuracy: The raster values represent minimum, mean, and maximum daily surface temperature in degrees Celsius x 10.

  • Overview: era5.copernicus: air temperature daily maxima from 2000 to 2020 resampled with CHELSA to 1 km resolution Traceability (lineage): The data sources used to generate this dataset are ERA5-Land hourly data from 1950 to present (Copernicus Climate Data Store) and CHELSA monthly climatologies. Scientific methodology: The methodology used for downscaling follows established procedures as used by e.g. Worldclim and CHELSA. Usability: The substantial improvement of the spatial resolution together with the high temporal resolution of one day further improve the usability of the original ERA5 Land time series product which is useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states. Uncertainty quantification: The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. Data validation approaches: Validation of the ERA5 Land ddataset against multiple in-situ datasets is presented in the reference paper (Muñoz-Sabater et al., 2021). Completeness: The dataset covers the entire Geo-harmonizer region as defined by the landmask raster dataset. However, some small islands might be missing if there are no data in the original ERA5 Land dataset. Consistency: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Positional accuracy: 1 km spatial resolution Temporal accuracy: Daily maps for the years 2020-2020. Thematic accuracy: The raster values represent minimum, mean, and maximum daily air temperature 2m above ground in degrees Celsius x 10.

  • Overview: modis.mcd64a1: burned areas from winter 2000 to 2020, aggregated seasonly Traceability (lineage): nan Scientific methodology: nan Usability: nan Uncertainty quantification: nan Data validation approaches: nan Completeness: nan Consistency: nan Positional accuracy: nan Temporal accuracy: nan Thematic accuracy: nan

  • Overview: ems.copernicus: fire activations observed event delimitations from 2012 to 2020 resampled to 30 m Traceability (lineage): The data source used to produce this dataset is the Copernicus Emergency Management Service, specifically the Rapid Mapping component. All available information has been used, from the activation of the service in 2012 untill 2020, selecting data tagged within the following categories: wildfire, forest fire, land fire, urban fire. For obtaining the rasters, the authors have used the delineation map product provided by EMS, when available. When not available, the First Estimate Product (FEP) was considered. According to the JRC Technical Report "Manual for CEMS-Rapid Mapping Products", the delineation product "provides an assessment of the event impact and extent and, if requested, an update of the situation (monitoring). They are derived from images acquired as soon as possible after the emergency event." The first estimate product is an early information product which aims at providing an extremely fast (yet rough) assessment of most affected locations within the area of interest. For the processing, the vector packages prepared for each of the EMS activation have been used, more specifically the "crisis information layers" detailed here: https://emergency.copernicus.eu/mapping/ems/group-b-crisis-information-layer . The main document detailing the methdology to obtain the EMS activation products is the JRC Technical Report "Manual for CEMS-Rapid Mapping Products" 2020, by Joubert-Boitat Inès, Wania Annett and Dalmasso Simone. Scientific methodology: The Copernicus EMS Rapid Mapping methodology of extracting disaster related information is detailed in the 'Manual CEMS for Rapid Mapping Products', published in August 2020 by Joubert-Boitat Inès, Wania Annett, Dalmasso Simone. The documentation is valid from April 2019 and it extends on the work published in 2018 "Product User Manual of Copernicus EMS Rapid Mapping” by Dorati et al. The Geo-harmonizer obtained product is represented by a series of rasters, one raster per each season, per each year from 2012 to 2020. In seasons where no fire activation has been registered, the raster has not been produced. For each individual raster, all fire activations relevant vector data packages have been downloaded, processed - by automatically extracting the "crisis information layers" rasterised and resampled at 30 m and mosaicked. Usability: Given the methodology that implies VHR data to detect fire burned areas, the data product obtined is useful especially in the context of validation of other fire products, obtained through automatic means. Uncertainty quantification: nan Data validation approaches: n/a Completeness: The CEMS Rapid Mapping functiones based on event triggered by an external entitiy - CEMS Authorised Member - therefore the dataset can not be evaluated for completness. Consistency: The rasters are obtained by processing the CEMS vector data, that presented a series of conceptual inconsistencies documented here https://docs.google.com/document/d/1b0XPH8EagA8VQnlnNHmbQ64BWXIoz1WZUSb1ppcEjxM/edit Positional accuracy: The raster files cover the entire Geo-harmonizer region as defined by the landmask raster dataset available here [source]. For facilitating the user's interaction with the map product, an additional point vector layer has been prepared containing information on the time of activation and the Emergency Rapid Mapping Service code assigned and link to the official Copernicus Emergency Rapid Mapping Service webpage where all the information on the event has been published, making it easy to quickly identify all relevant information on the specific burned areas activation. The point layer has been derived as the geometric center of the polygon delineating the burned area activation. Temporal accuracy: The Copernicus Emergency Service - Rapid Mapping service is activated based on date-precise triggering of Authorised Members, information that is clearly identified in the data processing. Thematic accuracy: The rasters have 2 values: 0 for non-burned area and 100 for burned area.

  • Overview: era5.copernicus: surface temperature daily minima from 2000 to 2020 resampled with CHELSA to 1 km resolution Traceability (lineage): The data sources used to generate this dataset are ERA5-Land hourly data from 1950 to present (Copernicus Climate Data Store) and CHELSA monthly climatologies. Scientific methodology: The methodology used for downscaling follows established procedures as used by e.g. Worldclim and CHELSA. Usability: The substantial improvement of the spatial resolution together with the high temporal resolution of one day further improve the usability of the original ERA5 Land time series product which is useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states. Uncertainty quantification: The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. Data validation approaches: Validation of the ERA5 Land ddataset against multiple in-situ datasets is presented in the reference paper (Muñoz-Sabater et al., 2021). Completeness: The dataset covers the entire Geo-harmonizer region as defined by the landmask raster dataset. However, some small islands might be missing if there are no data in the original ERA5 Land dataset. Consistency: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Positional accuracy: 1 km spatial resolution Temporal accuracy: Daily maps for the years 2020-2020. Thematic accuracy: The raster values represent minimum, mean, and maximum daily surface temperature in degrees Celsius x 10.

  • Overview: era5.copernicus: air temperature daily minima from 2000 to 2020 resampled with CHELSA to 1 km resolution Traceability (lineage): The data sources used to generate this dataset are ERA5-Land hourly data from 1950 to present (Copernicus Climate Data Store) and CHELSA monthly climatologies. Scientific methodology: The methodology used for downscaling follows established procedures as used by e.g. Worldclim and CHELSA. Usability: The substantial improvement of the spatial resolution together with the high temporal resolution of one day further improve the usability of the original ERA5 Land time series product which is useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states. Uncertainty quantification: The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. Data validation approaches: Validation of the ERA5 Land ddataset against multiple in-situ datasets is presented in the reference paper (Muñoz-Sabater et al., 2021). Completeness: The dataset covers the entire Geo-harmonizer region as defined by the landmask raster dataset. However, some small islands might be missing if there are no data in the original ERA5 Land dataset. Consistency: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Positional accuracy: 1 km spatial resolution Temporal accuracy: Daily maps for the years 2020-2020. Thematic accuracy: The raster values represent minimum, mean, and maximum daily air temperature 2m above ground in degrees Celsius x 10.

  • Overview: era5.copernicus: air temperature daily averages from 2000 to 2020 resampled with CHELSA to 1 km resolution Traceability (lineage): The data sources used to generate this dataset are ERA5-Land hourly data from 1950 to present (Copernicus Climate Data Store) and CHELSA monthly climatologies. Scientific methodology: The methodology used for downscaling follows established procedures as used by e.g. Worldclim and CHELSA. Usability: The substantial improvement of the spatial resolution together with the high temporal resolution of one day further improve the usability of the original ERA5 Land time series product which is useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states. Uncertainty quantification: The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. Data validation approaches: Validation of the ERA5 Land ddataset against multiple in-situ datasets is presented in the reference paper (Muñoz-Sabater et al., 2021). Completeness: The dataset covers the entire Geo-harmonizer region as defined by the landmask raster dataset. However, some small islands might be missing if there are no data in the original ERA5 Land dataset. Consistency: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Positional accuracy: 1 km spatial resolution Temporal accuracy: Daily maps for the years 2020-2020. Thematic accuracy: The raster values represent minimum, mean, and maximum daily air temperature 2m above ground in degrees Celsius x 10.

  • Overview: era5.copernicus: surface temperature daily averages from 2000 to 2020 resampled with CHELSA to 1 km resolution Traceability (lineage): The data sources used to generate this dataset are ERA5-Land hourly data from 1950 to present (Copernicus Climate Data Store) and CHELSA monthly climatologies. Scientific methodology: The methodology used for downscaling follows established procedures as used by e.g. Worldclim and CHELSA. Usability: The substantial improvement of the spatial resolution together with the high temporal resolution of one day further improve the usability of the original ERA5 Land time series product which is useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states. Uncertainty quantification: The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. Data validation approaches: Validation of the ERA5 Land ddataset against multiple in-situ datasets is presented in the reference paper (Muñoz-Sabater et al., 2021). Completeness: The dataset covers the entire Geo-harmonizer region as defined by the landmask raster dataset. However, some small islands might be missing if there are no data in the original ERA5 Land dataset. Consistency: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Positional accuracy: 1 km spatial resolution Temporal accuracy: Daily maps for the years 2020-2020. Thematic accuracy: The raster values represent minimum, mean, and maximum daily surface temperature in degrees Celsius x 10.