ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on single levels from 1940 to present".

land and oceanic climate variables. The data cover the Earth on a 31km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80km. ERA5 includes information about uncertainties for all variables at reduced spatial and temporal resolutions.

After many years of research and technical preparation, the production of a new ECMWF climate reanalysis to replace ERA-Interim is in progress. ERA5 is the fifth generation of ECMWF atmospheric reanalyses of the global climate, which started with the FGGE reanalyses produced in the 1980s, followed by ERA-15, ERA-40 and most recently ERA-Interim. ERA5 will cover the period January 1950 to near real time.

ERA5 is produced using high-resolution forecasts (HRES) at 31 kilometer resolution (one fourth the spatial resolution of the operational model) and a 62 kilometer resolution ten member 4D-Var ensemble of data assimilation (EDA) in CY41r2 of ECMWF's Integrated Forecast System (IFS) with 137 hybrid sigma-pressure (model) levels in the vertical, up to a top level of 0.01 hPa. Atmospheric data on these levels are interpolated to 37 pressure levels (the same levels as in ERA-Interim). Surface or single level data are also available, containing 2D parameters such as precipitation, 2 meter temperature, top of atmosphere radiation and vertical integrals over the entire atmosphere. The IFS is coupled to a soil model, the parameters of which are also designated as surface parameters, and an ocean wave model. Generally, the data is available at an hourly frequency and consists of analyses and short (12 hour) forecasts, initialized twice daily from analyses at 06 and 18 UTC. Most analyses parameters are also available from the forecasts. There are a number of forecast parameters (for example, mean rates and accumulations) that are not available from the analyses.

Improvements to ERA5, compared to ERA-Interim, include use of HadISST.2, reprocessed ECMWF climate data records (CDR), and implementation of RTTOV11 radiative transfer. Variational bias corrections have not only been applied to satellite radiances, but also ozone retrievals, aircraft observations, surface pressure, and radiosonde profiles.

ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset. ERA5 replaces its predecessor, the ERA-Interim reanalysis. ERA5 MONTHLY provides aggregated values for each month for seven ERA5 climate reanalysis parameters: 2m air temperature, 2m dewpoint temperature, total precipitation, mean sea level pressure, surface pressure, 10m u-component of wind and 10m v-component of wind. Additionally, monthly minimum and maximum air temperature at 2m has been calculated based on the hourly 2m air temperature data. Monthly total precipitation values are given as monthly sums. All other parameters are provided as monthly averages. ERA5 data is available from 1940 to three months from real-time, the version in the EE Data Catalog is available from 1979. More information and more ERA5 atmospheric parameters can be found at the Copernicus Climate Data Store. Provider's Note: Monthly aggregates have been calculated based on the ERA5 hourly values of each parameter.

ben-ge-8k: BigEarthNet Extended with Geographical and Environmental Data

M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023.

ben-ge-8k is a small-scale multimodal dataset for Earth observation that is a subset of the ben-ge dataset (https://github.com/HSG-AIML/ben-ge), which in turn serves as an extension to the BigEarthNet dataset. ben-ge complements the Sentinel-1/2 data contained in BigEarthNet by providing additional data modalities:

* elevation data extracted from the Copernicus Digital Elevation Model GLO-30;
* land-use/land-cover data extracted from ESA Worldcover;
* climate zone information extracted from Beck et al. 2018;
* environmental data concurrent with the Sentinel-1/2 observations from the ERA-5 global reanalysis;
* a seasonal encoding.

ben-ge-8k contains 8000 patches out of 590,326 patches in the full ben-ge dataset. These 8000 patches were sampled in such a way that for each of the 8 most common ESA WorldCover land-use/land-cover classes (tree cover, shrubland, grassland, cropland, built-up, bare/sparse vegetation, permanent water bodies, herbaceous wetland), we sampled 1000 patches randomly and used the fractional coverage of this class as a weight in the sampling process. As a result, these classes are slightly more balanced in ben-ge-8k than in the full dataset.

Data Modalities and Products

Meta Data

Relevant meta data for the ben-ge-8k dataset are compiled in the file ben-ge-8k_meta.csv. This file resides on the root level of this archive and contains the following data for each patch:
* patch_id: the Sentinel-2 patch id, which plays a central role for cross-referencing different data modalities for individual patches;
* patch_id_s1: the Sentinel-1 patch id for this specific patch;
* timestamp_s2: the timestamp for the Sentinel-2 observation;
* timestamp_s1: the timestamp for the Sentinel-1 observation;
* season_s2: the seasonal encoding (see below) for the time of the Sentinel-2 observation;
* season_s1: the seasonal encoding (see below) for the time of the Sentinel-1 observation;
* lon: longitude (WGS-84) of the center of the patch [degrees];
* lat: latitude (WGS-84) of the center of the patch [degrees];
* climatezone: integer value indicating the climate zone based on Beck et al. 2018 (see below for details).

Digital Elevation Model (Copernicus DEM GLO-30)

DEM data are contained in the dem/ directory of this archive.

Topographic maps are generated based on the global Copernicus Digital Elevation Model (GLO-30) (https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model). Relevant GLO-30 map tiles from the 2021 data release were downloaded through AWS (https://registry.opendata.aws/copernicus-dem/), reprojected into the coordinate frame of the corresponding Sentinel-1/2 patches and interpolated with bilinear resampling to 10 m resolution on the ground.

Elevation data are provided in a separate geotiff file for each patch. The naming convention for these files uses the Sentinel-2 patch_id to which we append _dem.tif. Each file contains a single band with 16-bit integer values that refer to the elevation of that pixel over sea level.

Land-use/Land-cover Data (ESA WorldCover)

Land-use/land-cover data are contained in the esaworldcover/ directory of this archive.

Land-use/land-cover map tiles matching the Sentinel-1/2 patches were extracted from ESA WorldCover (https://esa-worldcover.org). Relevant tiles were downloaded and reprojected into the coordinate frame of the corresponding Sentinel-1/2 patches. WorldCover data are available both as maps and as class fractions that are aggregated over each patch.

Land-use/land-cover map data are provided in a separate geotiff file for each patch. The naming convention for these files uses the Sentinel-2 patch_id to which we append _esaworldcover.tif. Each file contains a single band with 8-bit integer values that map to land-use/land-cover definitions provided by the ESA WorldCover Product User Manual (https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PUM_V2.0.pdf) (page 15).

The file ben-ge-8k_esaworldcover.csv contains the fractions by which each of the different classes cover the corresponding patch. This product may be useful to generate single-label or multi-label targets for different classification setups.

Environmental Data (ERA-5)

Weather data are contained in the ben-ge-8k_era-5.csv file.

Weather data at the time of observation (temperature at 2 m above the ground, relative humidity, wind vectors at 10 m above the ground) are extracted from the ERA-5 global reanalysis (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels) for the pressure level at the mean elevation of the observed scene and the time of observation (separately queried for Sentinel-1/2 observations).

Environmental data are available in the file ben-ge-8k_era-5.csv. For each patch, identified through the Sentinel-2 patch_id or the corresponding Sentinel-1 patch id patch_id_s1, the file contains the following parameters:
* atmpressure_level: atmospheric pressure level at which parameters have been queried [mbar]
* temperature_s2: temperature 2m above ground at the time of the Sentinel-2 observation [K]
* temperature_s1: temperature 2m above ground at the time of the Sentinel-1 observation [K]
* wind-u_s2: eastward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-2 observation [m/s]
* wind-u_s1: eastward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-1 observation [m/s]
* wind-v_s2: northward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-2 observation [m/s]
* wind-v_s1: northward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-2 observation [m/s]
* relhumidity_s2: relative humidity at the time of the Sentinel-2 observation [%]
* relhumidity_s1: relative humidity at the time of the Sentinel-1 observation [%]

as extracted from the ERA-5 global reanalysis (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels) for the patch location. Please see the corresponding documentation for details.

Seasonal Encoding
To capture the season at the time of observation, we apply a non-linear encoding that scale the date of the observation into the interval [0, 1], referring to [winter, summer] solstice. For any given date, we derive the fractional year and shift it by 9 days such that 21 June has the fractional year 0.5 and 22 December has the fractional year 0 or 1. To account for this ambiguity and the periodicity of the seasons, we modulate the fractional year with a sine function such that 21 June leads to a seasonal encoding of 1 and 22 December leads to a seasonal encoding of 0.

Seasonal encodings are provided by the column season in the ben-ge-8k_meta.csv file. Season values cover the interval [0,1] as a continuous variable where 1 refers to summer solstice and 0 refers to winter solstice.

Climate zone classification (Beck et al. 2018)

Patch-based climate zone classifications, based on the Köppen-Geiger scheme, were extracted from Beck et al. (2018) (https://www.nature.com/articles/sdata2018214), utilizing their present-day 1-km resolution map. Due to geographical focus of BigEarthNet on Europe, only 11 out of 27 different classes are present in this dataset. Please note that patches that are fully covered by surface water have no climate zone class assigned to them (class label equals zero in this case). Labels are encoded as discrete integer values that follow the schema introduced by Beck et al. 2018 in their legend.txt file that is included here: https://doi.org/10.6084/m9.figshare.6396959.

Climate zone classification labels are provided by the column climatezone in the ben-ge-8k_meta.csv file.

File and directory structure

This archive contains the following directory and file structure:

|
|--- README (this file)
|--- ben-ge-8k_meta.csv (ben-ge-8k meta data)
|--- ben-ge-8k_era-5.csv (ben-ge-8k environmental data)
|--- ben-ge-8k_esaworldcover.csv (patch-wise ben-ge-8k land-use/land-cover data)
|--- dem/ (digital elevation model data)
| |--- S2A_MSIL2A_20171208T093351_3_82_dem.tif
| ...
|--- esaworldcover/ (land-use/land-cover data)
| |--- S2B_MSIL2A_20170914T93030_26_83_esaworldcover.tif
| ...
|--- sentinel-1/ (Sentinel-1 SAR data)
| |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43/
| |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_labels_metadata.json (BigEarthNet label file)
| |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VH.tif (BigEarthNet/Sentinel-1 VH polarization data)
| |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VV.tif (BigEarthNet/Sentinel-1 VV polarization data)
| ...
|--- sentinel-2/ (Sentinel-2 multispectral data)
| |--- S2B_MSIL2A_20170818T112109_31_83/
| |--- S2B_MSIL2A_20170818T112109_31_83_B01.tif (BigEarthNet/Sentinel-2 Band 1

Climate maps (raster layers .tif) of basic-ecvs with a spatial resolution of 5.5 km (1 km for Azores) obtained by statistically downscaling a set of CMIP6 simulations for different IPCC climate scenarios (historical, SSP1-2.6, SSP2-4.5, SSP5-8.5) and time horizons (reference, short time-horizon, medium time-horizon, long time-horizon). Data are representative of specific climate normals (yearly averaged values) and created by RethinkAction project.

We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF.

Moreover, we acknowledge the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) to provide access to CMIP6, CERRA, ERA5 and ERA5-Land data:

Copernicus Climate Change Service, Climate Data Store, (2021): CMIP6 climate projections. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.c866074c.

Schimanke S., Ridal M., Le Moigne P., Berggren L., Undén P., Randriamampianina R., Andrea U., Bazile E., Bertelsen A., Brousseau P., Dahlgren P., Edvinsson L., El Said A., Glinton M., Hopsch S., Isaksson L., Mladek R., Olsson E., Verrelle A., Wang Z.Q., (2021): CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.622a565a

Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D.,Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47

Muñoz Sabater, J. (2019): ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.e2161bac

Acknowledgement also to:

DRAAC, 2023, Regional climate data provided by the Regional Ditectorate for the Environment and Climate Change of the Regional Autonomous Government of Azores (https://portal.azores.gov.pt/en/web/draac)

SRAA\CCIAM, 2017. Programa Regional de Alterações Climáticas (PRAC), Secretaria Regional do Ambiente e Ação Climática (SRAA) of the Governo dos Açores, Climate Change Impacts, Adaptation and Modelling (CCIAM) of the Faculdade de Ciências da Universidade de Lisboa (FCUL), https://snig.dgterritorio.gov.pt/rndg/srv/por/catalog.search#/metadata/8804acd9-9d0f-40fb-bc2e-e4dff8c2b4b1

Total volume of liquid water (mm3) precipitated over the period 00h-24h local time per unit of area (mm2), per month. Unit: mm month-1. The Precipitation flux variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb

The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Data publication: 2021-01-30

Data revision: 2021-10-05

Contact points:

Metadata Contact: ECMWF - European Centre for Medium-Range Weather Forecasts

Resource Contact: ECMWF Support Portal

Data lineage:

Agrometeorological data were aggregated to daily time steps at the local time zone and corrected towards a finer topography at a 0.1° spatial resolution. The correction to the 0.1° grid was realized by applying grid and variable-specific regression equations to the ERA5 dataset interpolated at 0.1° grid. The equations were trained on ECMWF's operational high-resolution atmospheric model (HRES) at a 0.1° resolution. This way the data is tuned to the finer topography, finer land use pattern and finer land-sea delineation of the ECMWF HRES model.

Resource constraints:

License Permission This License is free of charge, worldwide, non-exclusive, royalty free and perpetual. Access to Copernicus Products is given for any purpose in so far as it is lawful, whereas use may include, but is not limited to: reproduction; distribution; communication to the public; adaptation, modification and combination with other data and information; or any combination of the foregoing. Where the Licensee communicates or distributes Copernicus Products to the public, the Licensee shall inform the recipients of the source by using the following or any similar notice: • 'Generated using Copernicus Climate Change Service information [Year]' and/or • 'Generated using Copernicus Atmosphere Monitoring Service information [Year]'

More information on Copernicus License in PDF version at https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf

Online resources:

Dataset in support of the study “Getting the leaves right matters for estimating temperature extremes"

ERA-NUTS (1980-2021) This dataset contains a set of time-series of meteorological variables based on Copernicus Climate Change Service (C3S) ERA5 reanalysis. The data files can be downloaded from here while notebooks and other files can be found on the associated Github repository. This data has been generated with the aim of providing hourly time-series of the meteorological variables commonly used for power system modelling and, more in general, studies on energy systems. An example of the analysis that can be performed with ERA-NUTS is shown in this video. Important: this dataset is still a work-in-progress, we will add more analysis and variables in the near-future. If you spot an error or something strange in the data please tell us sending an email or opening an Issue in the associated Github repository. ## Data The time-series have hourly/daily/monthly frequency and are aggregated following the NUTS 2016 classification. NUTS (Nomenclature of Territorial Units for Statistics) is a European Union standard for referencing the subdivisions of countries (member states, candidate countries and EFTA countries). This dataset contains NUTS0/1/2 time-series for the following variables obtained from the ERA5 reanalysis data (in brackets the name of the variable on the Copernicus Data Store and its unit measure): - t2m: 2-meter temperature (2m_temperature, Celsius degrees) - ssrd: Surface solar radiation (surface_solar_radiation_downwards, Watt per square meter) - ssrdc: Surface solar radiation clear-sky (surface_solar_radiation_downward_clear_sky, Watt per square meter) - ro: Runoff (runoff, millimeters) There are also a set of derived variables: - ws10: Wind speed at 10 meters (derived by 10m_u_component_of_wind and 10m_v_component_of_wind, meters per second) - ws100: Wind speed at 100 meters (derived by 100m_u_component_of_wind and 100m_v_component_of_wind, meters per second) - CS: Clear-Sky index (the ratio between the solar radiation and the solar radiation clear-sky) - HDD/CDD: Heating/Cooling Degree days (derived by 2-meter temperature the EUROSTAT definition. For each variable we have 367 440 hourly samples (from 01-01-1980 00:00:00 to 31-12-2021 23:00:00) for 34/115/309 regions (NUTS 0/1/2). The data is provided in two formats: - NetCDF version 4 (all the variables hourly and CDD/HDD daily). NOTE: the variables are stored as int16 type using a scale_factor to minimise the size of the files. - Comma Separated Value ("single index" format for all the variables and the time frequencies and "stacked" only for daily and monthly) All the CSV files are stored in a zipped file for each variable. ## Methodology The time-series have been generated using the following workflow: 1. The NetCDF files are downloaded from the Copernicus Data Store from the ERA5 hourly data on single levels from 1979 to present dataset 2. The data is read in R with the climate4r packages and aggregated using the function /get_ts_from_shp from panas. All the variables are aggregated at the NUTS boundaries using the average except for the runoff, which consists of the sum of all the grid points within the regional/national borders. 3. The derived variables (wind speed, CDD/HDD, clear-sky) are computed and all the CSV files are generated using R 4. The NetCDF are created using xarray in Python 3.8. ## Example notebooks In the folder notebooks on the associated Github repository there are two Jupyter notebooks which shows how to deal effectively with the NetCDF data in xarray and how to visualise them in several ways by using matplotlib or the enlopy package. There are currently two notebooks: - exploring-ERA-NUTS: it shows how to open the NetCDF files (with Dask), how to manipulate and visualise them. - ERA-NUTS-explore-with-widget: explorer interactively the datasets with jupyter and ipywidgets. The notebook exploring-ERA-NUTS is also available rendered as HTML. ## Additional files In the folder additional fileson the associated Github repository there is a map showing the spatial resolution of the ERA5 reanalysis and a CSV file specifying the number of grid points with respect to each NUTS0/1/2 region. ## License This dataset is released under CC-BY-4.0 license.

[ Derived from parent entry - See data hierarchy tab ]

Hindcast atmospheric simulation for the North Sea using COSMO6.0-CLM version driven with ERA5 reanalysis data. The covered period is from 2012 to 2022 with hourly frequency output. The model uses a rotated grid with 356 x 396 grid points and a grid spacing of 0.02 degrees, the rotated North pole is located at 180 W, 30 N. We gratefully acknowledge financial support through the H2Mare PtX-Wind project with funds provided by the Federal Ministry of Education and Research (BMBF) under Grant No. 03HY302J.

Minimum air temperature calculated at a height of 2 metres above the surface. Unit: K. The Minimum air temperature variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

The dataset is the supplementary material for the following journal paper:

Parisse B.*, Alilla R., Pepe A.G., De Natale F., MADIA - Meteorological variables for Agriculture: a Dataset for the Italian Area, Data in Brief, 46 (2023), 108843, 10.1016/j.dib.2022.108843, (https://www.sciencedirect.com/science/article/pii/S2352340922010460)

Abstract

The MADIA gridded dataset provides the series of the main agro-meteorological variables derived from ERA5 hourly surface data, across the Italian domain for the period 1981-2022, and their respective 1981-2010 and 1991-2020 climate normals, as well as the following statistics on the 30-year dekadal values of each variable: absolute minimum and maximum, 5th, 10th, 50th, 90th, 95th percentiles. Temporal and spatial resolutions are 10-daily and 0.25 degrees respectively. The dataset contains time series of minimum, average and maximum air temperature, minimum and maximum air relative humidity, wind speed, solar radiation, precipitation and reference evapotranspiration according to the FAO Penman-Monteith method. The dataset is provided in both NetCDF and csv format. In addition, discovery and description metadata are provided. In order to facilitate the data reuse for computing statistics at Italian NUTS 2 and 3 levels, a complementary vector file is provided which reports the cell weight in terms of fraction covered of each administrative unit considered. Another vector file is included with the ERA5 cell polygons covering the Italian country for visualizing and mapping csv data.

A daily version of the MADIA dataset (only in csv format) is also available on Zenodo at https://doi.org/10.5281/zenodo.7621453.

Both MADIA datasets will be periodically updated.

Attached content

A ZIP archive composed by the following folders

nc_data: annual time series from 1981 to 2022 and climate normals (1981-2010 and 1991-2020) in NetCDF format

csv_data: annual time series from 1981 to 2022 and climate normals (1981-2010 and 1991-2020) in csv format

metadata: discovery and description metadata

shp_data: two complementary vector layers with the NUTS2-3 cover fractions and the ERA5 cell polygons for Italy

Acknowledgments

This work was supported by the Italian Ministry of Agricultural, Food and Forestry Policies (AgriDigit-Agromodelli, DM n. 36502 of 20/12/2018)

This repository contains the SEWA-MHWs dataset, which consists of daily fields of Marine heatwaves (MHWs) macroevents, daily fields of MHWs characteristics, and daily fields of relevant atmospheric variables over Southern Europe and Western Asia region. It contains also the codes to detect MHWs macroevents and their characteristics. The SEWA-MHWs dataset is derived from the European Space Agency (ESA) Climate Change Initiative (CCI) Sea Surface Temperature (SST) v2.1 dataset and it covers the 1981-2016 period. This dataset is presented and described in detail in the "Southern Europe and Western Asia Marine Heat Waves (SEWA-MHWs): a dataset based on macroevents" manuscript by Giulia Bonino, Simona Masina, Giuliano Galimberti, and Matteo Moretti submitted to Earth System Science Data journal (Bonino et al., 2022). This repository contains 3 compressed (*.zip) folders: A) MHWs: it contains daily fields of MHWs macroevents, daily fields of MHWs characteristics SEWA_labels.nc: daily fields of labels. Each unique label represents a macro event. SEWA_IndStart.nc: daily fields of MHWs index start. SEWA_IndPeak.nc: daily fields of MHWs index peak. SEWA_IndEND.nc: daily fields of MHWs index end. SEWA_Category.nc: daily fields of MHWs categories. SEWA_IntMAx.nc: daily fields of MHWs maximum intensity [°C]. SEWA_IntMean.nc: daily fields of MHWs mean intensity [°C]. B) CODES: Python notebooks to detect MHWs macroevents and their characteristics. MHWs_stl.ipynb to detect MHWs and their characteristics SEWA_LABEL.ipynb: to generate the MHWs macroevents MHWs_filter.ipynb: to filter out the smallest macroevents STL_MarineHeatwaves.py: it contains the function “detect_stl” to detect MHWs using STL method used in MHW_stl.ipynb. This function is a modification of the “detect” function in the marineHeatWaves package created by Eric Oliver (https://github.com/ecjoliver/marineHeatWaves). C) ATM: daily mean fields of relevant atmospheric variables taken from ERA5 (Hersbach et al., 2020, freely available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview). These data are subsets of the ERA5 dataset, after minimal post-processing manipulation. The area extracted for these meteorological parameters is slightly bigger than the SEWA region, allowing the investigation of remote influences and/or responses of these variables in relationship with MHWs macroevents. The covered area is from 10°N to 70°N in latitude and from 50°W to 80°E in longitude. The covered period is 1981-2016. SEWA_T2.nc: daily mean fields of 2 meter temperature [K]. “2m temperature” is the native variable name in ERA5 dataset. Unlike ERA5 dataset the data are provided as daily mean. SEWA_LAT.nc: daily mean fields of surface latent heat flux [W/m2]. “Surface latent heat flux” is the native variable name in ERA5 dataset. Unlike ERA5 the data are provided as daily mean and in W/m2 instead of J/m2. SEWA_SENS.nc: daily mean fields of surface sensible heat flux [W/m2]. “Surface sensible heat flux” is the native variable name in ERA5 dataset. Unlike ERA5 the data are provided as daily mean and in W/m2 instead of J/m2. SEWA_SLP.nc: daily mean fields of mean sea level pressure [Pa]. “Surface pressure” is the native variable name in ERA5 dataset. Unlike ERA5 the data are provided as daily mean. SEWA_WIND.nc: daily mean fields of 10 meter wind speed [m/s]. This variable is calculated from the wind components “10m u-component of wind” and “10m v-component of wind” of the ERA5 dataset. Unlike ERA5 the data are provided as daily mean. SEWA_SW.nc: daily mean fields of incoming solar radiation [W/m2]. “Surface solar radiation downwards” is the native variable name in ERA5 dataset. Unlike ERA5 the data are provided as daily mean and in W/m2 instead of J/m2. REFERENCES: Bonino, G., Masina, S., Galimberti, G., & Moretti, M. (2022). Southern Europe and Western Asia marine heat waves (SEWA-MHWs): a dataset based on macro events. Earth System Science Data Discussions, 1-19. Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., et al.: The ERA5 global reanalysis, Quarterly Journal of the Royal Meteorological Society, 146, 1999–2049, 2020. This research has been funded by the European Space Agency (ESA) as part of the FEVERSEA Climate Change Initiative (CCI) fellowship (ESA ESRIN/Contract No. 4000133282/20/I/NB).

Flooding is one of the most destructive natural hazards in tropical mountain basins, yet detailed vulnerability assessments remain scarce where observational data are limited. In this study, we compiled and harmonized high‐resolution geomorphological, hydroclimatic, land‐cover, soil, and population datasets for the 1,777 km² Guatiquía River watershed in the Colombian Andes, covering the period 1991–2022 (DEM at 12.5 m, CHIRPS precipitation at 5.5 km, ERA5 reanalysis at 25 km, MapBiomas land cover at 30 m, and IGAC soil maps) ArticleGuatiquiaRiverWa…. We derived key conditioning factors: slope, Topographic Wetness Index (TWI), Curve Number (CN3), population density, and an Extreme Precipitation Susceptibility Index (EPSI) composed of six ETCCDI climate extremes, and applied a Frequency Ratio (FR) model to quantify their spatial correlation with historical flood occurrences. The resulting vulnerability map highlights the middle‐lower basin, particularly around Villavicencio, as the most susceptible zone, driven by flat terrain, high moisture accumulation, low infiltration (CN3 > 70), and recurrent intense rainfall. Model validation via Receiver Operating Characteristic (ROC) analysis yielded an Area Under the Curve of 0.82, demonstrating robust predictive performance. This work provides the first comprehensive, data‐driven flood vulnerability assessment for the Guatiquía watershed and offers a transferable methodology for other data‐scarce Andean basins. All processed datasets and derived layers are publicly available to support regional water‐resources management and climate‐adaptation planning.

Total volume of liquid water (mm3) precipitated over the period 00h-24h local time per unit of area (mm2), per dekade. Unit: mm dekade-1. The Precipitation flux variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb

The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Data publication: 2021-01-30

Data revision: 2021-10-05

Contact points:

Metadata Contact: ECMWF - European Centre for Medium-Range Weather Forecasts

Resource Contact: ECMWF Support Portal

Data lineage:

Agrometeorological data were aggregated to daily time steps at the local time zone and corrected towards a finer topography at a 0.1° spatial resolution. The correction to the 0.1° grid was realized by applying grid and variable-specific regression equations to the ERA5 dataset interpolated at 0.1° grid. The equations were trained on ECMWF's operational high-resolution atmospheric model (HRES) at a 0.1° resolution. This way the data is tuned to the finer topography, finer land use pattern and finer land-sea delineation of the ECMWF HRES model.

Resource constraints:

License Permission This License is free of charge, worldwide, non-exclusive, royalty free and perpetual. Access to Copernicus Products is given for any purpose in so far as it is lawful, whereas use may include, but is not limited to: reproduction; distribution; communication to the public; adaptation, modification and combination with other data and information; or any combination of the foregoing. Where the Licensee communicates or distributes Copernicus Products to the public, the Licensee shall inform the recipients of the source by using the following or any similar notice: • 'Generated using Copernicus Climate Change Service information [Year]' and/or • 'Generated using Copernicus Atmosphere Monitoring Service information [Year]'

More information on Copernicus License in PDF version at https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf

Online resources:

This dataset was generated by the Remote Sensing Group of the TU Wien Department of Geodesy and Geoinformation, within projects funded by the European Structural and Investments Funds, the Austrian Space Applications Programme, and the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF). Open use is granted under the CC BY 4.0 license. The provided dataset publication aims to support users of the ASCAT soil moisture data as provided by the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) to mask invalid retrievals due to subsurface scattering. This phenomenon has been discerned as the principal source of error in the current version of ASCAT soil moisture retrievals, as it contradicts the assumption that soil backscatter increases monotonically with soil moisture content. This happens because, in dry soil conditions, the presence of stones, rocks, or distinct soil layers can disrupt this expected relationship. At TU Wien, we have developed one statistical (P_ano) and two physically based indicators (P_sub, S_sub) that show the widespread occurrence of subsurface scattering not only in desert regions but also in more humid climates with distinct dry seasons. These indicators offer a means to identify subsurface scattering effects, enabling users of H SAF ASCAT soil moisture data to mask such effects. By selecting one of the provided indicators and setting a suitable threshold, users can tailor the masking process to their specific needs. As a baseline, we recommend using the monthly subsurface scattering mask included here in its dedicated file. We encourage the user community to leverage this data record to enhance the design of ASCAT soil moisture validation and application experiments. Moreover, this resource invites a reevaluation of conclusions drawn from earlier ASCAT studies and presents an opportunity to extend insights to other active microwave sensors operating at lower microwave frequencies (S-, L-, and P-band). Dataset Record ASCAT Mask Results This file contains the a mask recommended for ASCAT soil moisture retrievals, designed to address various conditions that can render ASCAT soil moisture data unreliable. These conditions encompass frozen soils, snow cover, wetlands, and areas with dense vegetation. Regarding the mitigation of subsurface scattering effects, the applied mask is derived from the monthly statistical indicator P_ano, with a threshold set at 0.1. This mask is applied to pixels that exceed this threshold for more than nine months. The results are gridded to the 12.5 km fixed Earth grid used for ASCAT (WARP5 grid). By implementing this mask, ASCAT pixels with potentially unreliable soil moisture measurements are excluded. Note that the subsurface scattering mask represents mean monthly conditions over the years 2007 to 2021, i.e. the behavior in single years may deviate from these conditions. The interpretation of values is as follows: 0: indicates no mask is applied, signifying that the soil moisture retrieval is considered reliable 1: indicates the application of the mask, implying that the soil moisture retrieval may not be reliable ERA5-Land Data Analysis For those seeking a more comprehensive exploration of subsurface scattering or desiring to generate customized masks utilizing the provided indicators, this file offers insights into ASCAT backscatter and surface soil moisture in combination with ERA5-Land soil moisture as well as ancillary information. The underlying data spans from 2007 to 2021 and is again gridded to the 12.5 km WARP5 grid. Within this file, users can access the following information: Subsurface scattering indicators: P_ano - Probability of the occurrence of backscatter anomalies, depicts how frequently the ASCAT backscatter data exhibit anomalies P_ano_MM - P_ano calculated on a monthly basis P_sub - Probability of detecting subsurface scattering, derived from a physically based method that compares the goodness of fit of two backscatter models S_sub - Subsurface scattering signal strength, displays the signal range of the subsurface scatteringterm from completely dry to wet conditions Correlations before and after masking: R_unmasked - Pearson correlation coefficient between ASCAT surface soil moisture and ERA5-Land soil moisture with no mask applied R_masked - Pearson correlation after applying the mask provided in the file above Selected specific masks: cold_mask - Frozen soil and snow cover mask using ASCAT confidence flag (bit 1), ERA5-Land soil temperature (≤ 2 °C) and snow depth data (> 0 mm after averaging with a sliding window of 31 days) veg_mask - Dense vegetation mask based on ASCAT confidence flags (bits 4 and 5) and Copernicus Global Land Monitoring service (CGLS) leaf area index (LAI) data (LAI > 3) wet_mask - Wetland mask using Global Lakes and Wetlands Database (GLWD), land cover information, and ASCAT confidence flag subsurface_mask - Subsurface scattering mask based on monthly P_ano data (P_ano > 0.1) if threshold is exceeded for more than nine months

Relative humidity at 06h (local time) at a height of 2 metres above the surface. This variable describes the amount of water vapour present in air expressed as a percentage of the amount needed for saturation at the same temperature. Unit: %. The Relative humidity variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

This catalogue entry provides the gridded climate data (monthly/annual timeseries) used for the Copernicus Climate Change Service Atlas (C3S Atlas). The gridded datasets consist of in-situ and satellite observation-based datasets, reanalyses (CERRA, ERA5, ERA5-Land, and ORAS5) and global (CMIP5 and CMIP6) and regional (CORDEX) climate projections for the variables and indices included in the C3S Atlas. This dataset complements the Gridded monthly climate projection dataset underpinning the IPCC AR6 Interactive Atlas (IPCC Atlas dataset hereafter), including new datasets, variables and indices. The variables and indices describe various types of climatic impact characteristics: heat and cold, wet and dry, snow and ice, wind and radiation, ocean, circulation and drought characteristics of the climate system. All data sources included in this entry are available in the Climate Data Store (CDS, see “Related data” in the sidebar). Contrary to the frozen IPCC Atlas dataset, this entry will update adding new data on a regular basis. This dataset includes gridded information with monthly/annual temporal resolution for observations/reanalyses of the recent past and climate projections for the 35 variables and indices computed from daily/monthly data across the different datasets. The climate projections are based on Representative Concentration Pathways (RCP) / Shared Socioeconomic Pathways (SSP) scenarios. The datasets are harmonised using regular latitude-longitude grids. Bias correction is available for threshold-based indices. Two methods are available, depending on the variable; linear scaling and the ISIMIP method. This dataset allows the reproduction, expansion and customisation of the climate change products provided interactively by the Copernicus Interactive Climate Atlas. This is an interactive web application displaying global/regional maps of observed trends and climate changes for future periods across scenarios or for global warming levels, and regionally aggregated time series, seasonal cycle plots and climate stripes.

This archive contains model forcing and output for the Shyft model, along with scripts of the related data processing. The structure of the archive (folders) is as follows:

1. "lalm" and "elverum: contain the meteorological forcing data for the two catchments Lalm and Elverum, respectively.
2. "model forcing for historical periods": contains the bias corrected climate model data representing the three historical periods.
3. "model simulation output": contains the model output data for the simulations of the three historical periods.
4. "shyft workspace": contains the data processing scripts.

1. This dataset consists of the folders: "senorge", "era5" and "hysn5". The included data variables are: temperature, precipitation, wind speed, relative humidity and radiation. Temperature and precipitation are found in "senorge". Wind speed is found in "era5". Lastly, relative humidity and radiation are found in "hysn5". The dataset is of the netCDF-format. The folders contain data that was downloaded from the sources: SeNorge2018 (The Norwegian Meteorological institute, 2022), ERA5-land (Muñoz, 2019; Muñoz, 2021) and HYSN5 (Haddeland, 2022). The data is described as follows:

temperature:

• Description: daily mean air temperature
• Unit: degrees Celsius
• Spatial resolution: 1x1 km
• Grid mapping: UTM Zone 33
• Dimension: time, latitude and longitude

precipitation:

• Description: daily mean precipitation
• Unit: mm/day
• Spatial resolution: 1x1 km
• Grid mapping: UTM Zone 33
• Dimension: time, latitude and longitude

wind speed:

• Description: daily mean wind speed
• Unit: m/s
• Spatial resolution: 0.1x0.1 degree (native resolution of 9 km)
• Grid mapping: EPSG:4326
• Dimension: time, latitude and longitude

relative humidity:

• Description: daily mean near-surface relative humidity
• Unit: %
• Spatial resolution: 1x1 km
• Grid mapping: UTM Zone 33
• Dimension: time, latitude and longitude

radiation:

• Description: daily mean surface downwelling shortwave radiation
• Unit: W/m²
• Spatial resolution: 1x1 km
• Grid mapping: UTM Zone 33
• Dimension: time, latitude and longitude

2. This dataset contains climate model data for the three historical periods: Medieval Warm Period (MWP; 1000-1150 AD), Little Ice Age (LIA; 1600-1750 AD) and Industrial Time (IT; 1800-1950 AD). The data covers the two catchments Lalm (L) and Elverum (E) for simulations using both low solar variability (Solar 1; S1) and high solar variability (Solar 2; S2). The data consists of the variables: temperature (temp), precipitation (prec), wind speed (wind), relative humidity (humi) and radiation (radi). The dataset is of the netCDF-format. The related source data is not published here, due to licences. Contact Lu Li at the NORCE research centre regarding data accessibility. The data is described as follows:

temperature:

• Description: daily mean air temperature
• Unit: degrees Celsius
• Spatial resolution: 1x1 km
• Grid mapping: UTM Zone 33
• Dimension: time, latitude and longitude

precipitation:

• Description: daily mean precipitation
• Unit: mm/hour
• Spatial resolution: 1x1 km
• Grid mapping: UTM Zone 33
• Dimension: time, latitude and longitude

wind speed:

• Description: daily mean wind speed
• Unit: m/s
• Spatial resolution: 0.1x0.1 degree (native resolution of 9 km)
• Grid mapping: UTM Zone 33
• Dimension: time, latitude and longitude

relative humidity:

• Description: daily mean near-surface relative humidity
• Unit: -
• Spatial resolution: 1x1 km
• Grid mapping: UTM Zone 33
• Dimension: time, latitude and longitude

radiation:

• Description: daily mean surface downwelling shortwave radiation
• Unit: W/m²
• Spatial resolution: 1x1 km
• Grid mapping: UTM Zone 33
• Dimension: time, latitude and longitude

3. This dataset contains time series data for the three historical periods: Medieval Warm Period (MWP; 1000-1150 AD), Little Ice Age (LIA; 1600-1750 AD) and Industrial Time (IT; 1800-1950 AD), which are output from the Shyft model. The data covers the two catchments Lalm (L) and Elverum (E) for simulations using both low solar variability (Solar 1; S1) and high solar variability (Solar 2; S2). The data consists of the variables: discharge, temperature, precipitation, wind_speed, relative_humidity and radiation, snow water equivalent (SWE) and snow covered area (SCA). The dataset is of the csv-format.

NB: the datetime index of the data suggests that the data covers the period of 1700-1850, however this is only true for IT. This inconsistency is caused by a limitation of datetime64 in pandas, which does not handle dates prior to the year 1678.

The data is described as follows:

discharge:

• Description: daily mean air temperature
• Unit: degrees Celsius
• Dimension: time

temperature:

• Description: daily mean air temperature
• Unit: degrees Celsius
• Dimension: time

precipitation:

• Description: daily mean precipitation
• Unit: mm/hour
• Dimension: time

wind_speed:

• Description: daily mean wind speed
• Unit: m/s
• Dimension: time

relative_humidity:

• Description: daily mean near-surface relative humidity
• Unit: -
• Dimension: time

radiation:

• Description: daily mean surface downwelling shortwave radiation
• Unit: W/m²
• Dimension: time

SWE:

• Description: daily mean snow water equivalent
• Unit: mm
• Dimension: time

SCA:

• Description: daily mean snow covered area (% of total catchment area)
• Unit: -
• Dimension: time

4. The scripts make up the workflow of the thesis. In order to reproduce the results, the first script has to be run firstly, then the second script is applied on the output from the first etc. Keep in mind that manual adjustments inside the scripts are required in order to obtain some of the results. The scripts are described as follows:

1. "Subsetting_data.ipynb": This script subsets forcing data (temperature, precipitation, wind speed, relative humidity and radiation) from the sources (SeNorge2018, ERA5-Land and HySN5) to the catchments of Lalm and Elverum.
2. "convert_netcdf.ipynb": This script converts netCDF-files of temperature, precipitation, wind speed, relative humidity and radiation to fit as model forcing to the Shyft modeling framework. It also creates a cell data file containing information about the catchments (Lalm and Elverum) forest, lake and glacier fraction, which are required in Shyft.
3. "QDM_lalm.ipynb" and "QDM_elverum.ipynb": These scripts perform the bias correction approach, Quantile Delta Mapping (QDM), on the climate model data (temperature, precipitation, wind speed, relative humidity and radiation).
4. "extract_historical_periods.ipynb": This script extracts the three historical periods of 1000-1150 (Medieval Warm Period), 1600-1750 (Little Ice Age) and 1800-1950 (Industrial Time) from the climate model data (temperature, precipitation, wind speed, relative humidity and radiation).
5. "calibration_lalm.ipynb" and "calibration_elverum.ipynb": Scripts that runs the calibration of Lalm and Elverum catchment using the Shyft model, respectively.*
6. "simulation_lalm.ipynb" and "simulation_elverum.ipynb": Scripts that runs the simulation of Lalm and Elverum catchment using the Shyft model, respectively.*
7. "data_analysis.ipynb": This script contains the data analysis performed on the Shyft model simulation output. The analysis includes: calculations of mean monthly values of the climate variables (discharge, temperature, precipitation, snow water equivalent and snow covered area), decadal time series of the climate variables, calculations of mean floods and 100-year floods, flood and extreme precipitation frequency analysis, calculation of season index, estimation of flood generating processes, plotting of flood roses and estimation of Standardised Precipitation Index.

*For the Shyft model configuration, simulation and calibration files (yaml-files) are included in the folder "yaml_lalm" and "yaml_elverum" for the two catchments. These yaml-files are described as follows:

• simulation.yaml: is used for configuration of the model simulation
• calibration.yaml: is used for configuration of the model calibration
• calibrated_model.yaml: contains the calibrated model parameters
• datasets.yaml: contains the paths to the data variables
• interpolation.yaml: contains the interpolation methods and parameters
• region.yaml: contains the modeling domain

References:

Haddeland, I. (2022). HySN2018v2005ERA5 (Version 1) [Data set]. Zenodo. (Accessed on: 19-09-2022). doi: https://doi.org/10.5281/zenodo.5947547.

Muñoz Sabater, J. (2019). ERA5-Land hourly data from 1981 to present [Dataset]. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on: 19-09-2022). doi: https://doi.org/10.24381/cds.e2161bac.

Muñoz Sabater, J. (2021).

Global Atlas of Fraction of Snowfall in Forest (FSF)

The FSF (FSF_11km.tif) is the product of the fraction of snowfall (Snowfall_Fraction*) from ERA5-Land (Muñoz Sabater et al., 2019) with the tree cover (TreeCover_11km.tif) from Hansen et al. (2013).

Digital elevation model (DEM_11km.tif) from the Copernicus GLO30 product and Köppen-Geiger climate (Koppen_11km.tif) are also provided on the same grid as FSF.

The FSF is also averaged per hydrological basin of level 3 in the HydroBasin dataset (Lehner et al., 2013) and mountain range (Snethlage et al., 2022) respectively in data_hybas.geojson and data_gmba.geojson.

References

Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., Townshend, J. R. G. (2013). High-Resolution Global Maps of 21st-Century Forest Cover Change. Science, 342(6160), 850–853. https://doi.org/10.1126/science.1244693

Kottek, M., Grieser, J., Beck, C., Rudolf, B., & Rubel, F. (2006). World Map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift, 15(3), 259–263. https://doi.org/10.1127/0941-2948/2006/0130

Lehner, B., & Grill, G. (2013). Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrological Processes, 27(15), 2171–2186. https://doi.org/10.1002/hyp.9740

Muñoz Sabater, J. (2019). ERA5-Land monthly averaged data from 1981 to present [Data set]. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). https://doi.org/doi:10.24381/cds.68d2bb30

Snethlage, Mark A., Geschke, J., Ranipeta, A., Jetz, W., Yoccoz, N. G., Körner, C., et al. (2022). A hierarchical inventory of the world’s mountains for global comparative mountain science. Scientific Data, 9(1), 149. https://doi.org/10.1038/s41597-022-01256-y