We implemented automated workflows using Jupyter notebooks for each state. The GIS processing, crucial for merging, extracting, and projecting GeoTIFF data, was performed using ArcPy—a Python package for geographic data analysis, conversion, and management within ArcGIS (Toms, 2015). After generating state-scale LES (large extent spatial) datasets in GeoTIFF format, we utilized the xarray and rioxarray Python packages to convert GeoTIFF to NetCDF. Xarray is a Python package to work with multi-dimensional arrays and rioxarray is rasterio xarray extension. Rasterio is a Python library to read and write GeoTIFF and other raster formats. Xarray facilitated data manipulation and metadata addition in the NetCDF file, while rioxarray was used to save GeoTIFF as NetCDF. These procedures resulted in the creation of three HydroShare resources (HS 3, HS 4 and HS 5) for sharing state-scale LES datasets. Notably, due to licensing constraints with ArcGIS Pro, a commercial GIS software, the Jupyter notebook development was undertaken on a Windows OS.

This dataset contains information on the Surface Soil Moisture (SM) state derived from satellite observations in the microwave domain.

The operational (ACTIVE, PASSIVE, COMBINED) ESA CCI SM products are available at https://catalogue.ceda.ac.uk/uuid/c256fcfeef24460ca6eb14bf0fe09572/

Abstract

Understanding whether the soil surface is frozen or thawed is crucial for interpreting satellite-based soil moisture measurements and for many Earth system applications. The physical state of water in the soil strongly affects its dielectric properties, which in turn determine how satellites sense moisture content. Current ESA CCI Soil Moisture products exclude data when the surface is likely frozen, as reliable retrievals are not possible under such conditions. Yet, the freeze/thaw state itself carries valuable environmental information: it reflects the changing energy and water exchange between land and atmosphere, shapes seasonal hydrological cycles, and influences agriculture, ecosystems, and climate feedbacks across much of the Northern Hemisphere.

This dataset provides global estimates of the soil moisture freeze/thaw state for the period from 11-1978 to 12-2024 derived from PASSIVE (radiometer) satellite observations within the ESA CCI Soil Moisture framework. These radiometers, operating in the K-band frequency range, are sensitive to surface temperature, enabling the detection of frozen versus thawed conditions at daily temporal and ~25 km spatial sampling. Data from L-band missions (e.g., SMAP, SMOS) are not included, resulting in a total number of 12 satellites.

The classification algorithm, described in Van der Vliet et al. (2020), was originally developed to flag frozen conditions in soil moisture retrievals and has since evolved into a dedicated data product. It applies a decision-tree approach using multi-frequency satellite measurements to classify the surface state for each sensor. Individual classifications are then merged into a single spatiotemporal record using a conservative unanimity rule—if any contributing satellite detects a frozen surface, the merged product is classified as “frozen.”

While this approach ensures reliability, it may lead to some over-flagging, which could be refined in future versions. The current product achieves an estimated accuracy of 75% against in situ surface temperature observations and 92% compared to ERA5 reanalysis data.

Summary

• Daily binary (true/false) freeze/thaw surface soil moisture state classification dataset (~25 km spatial sampling) for the period November 1978 to December 2024.
• Based on a satellite brightness temperature (K-band) classification algorithm (Van der Vliet et al., 2020) from 12 satellite radiometers.
• A pixel is classified as "frozen" if it was classified accordingly for at least one satellite. This can lead to potential over-flagging in the current version.
• Approximately 75% agreement with in situ surface temperature measurements (Dorigo et al., 2021) and 92% with ERA5-Land reanalysis temperature fields (Muñoz-Sabater et al., 2021)

Programmatic (bulk) download

You can use command-line tools such as wget or curl to download (and extract) data for multiple years. The following command will download and extract the complete data set to the local directory ~/Download on Linux or macOS systems.

#!/bin/bash

# Set download directory
DOWNLOAD_DIR=~/Downloads

base_url="https://researchdata.tuwien.at/records/m3g2x-a6958/files"

# Loop through years 1978 to 2024 and download & extract data
for year in {1978..2024}; do
  echo "Downloading $year.zip..."
  wget -q -P "$DOWNLOAD_DIR" "$base_url/$year.zip"
  unzip -o "$DOWNLOAD_DIR/$year.zip" -d $DOWNLOAD_DIR
  rm "$DOWNLOAD_DIR/$year.zip"
done

Data details

Filename template

The dataset provides global daily estimates for the 1978-2024 period at 0.25° (~25 km) horizontal grid resolution. Daily images are grouped by year (YYYY), each subdirectory containing one netCDF image file for a specific day (DD) and month (MM) of that year in a 2-dimensional (longitude, latitude) grid system (CRS: WGS84). The file name follows the convention:

ESACCI-SOILMOISTURE-L3S-FT-YYYYMMDD000000-fv09.2.nc

Data Variables

Each netCDF file contains 3 coordinate variables

• lon: longitude (WGS84), [-180,180] degree W/E
• lat: latitude (WGS84), [-90,90] degree N/S
• time: datetime, encoded as "number of days since 1970-01-01 00:00:00 UTC"

and the following data variables

• ft: (int) Soil moisture freeze-thaw state binary indicator (0=not frozen, 1=frozen, -1=missing data)
• ft_agreement (float): Classification agreement between available sensors. 1 means that the frozen/unfrozen classification was the same for all merged sensors. The number decreases as the classification results between available satellites contradict.
• sensor_count (int): Total number of merged sensors/overpasses
• sensor_count_frozen (int): Total number of measuring sensors/overpasses that detected frozen soils
• mode: (int) Indicator for satellite orbit(s) used in the retrieval (1=ascending, 2=descending, 3=both, 0=missing data)
• sensor: (int) Indicator for satellite sensor(s) used in the retrieval. For more details, see netcdf attributes.

Additional information for each variable is given in the netCDF attributes.

Version Changelog

Changes in v9.2 (first released version):

• This version uses the classification algorithm described by Van der Vliet et al. (2020) applied to 12 sensors and a unanimous merging approach. Covers the period from 11-1978 to 12-2024.

Software to open netCDF files

These data can be read by any software that supports Climate and Forecast (CF) conform metadata standards for netCDF files, such as:

• https://github.com/pydata/xarray" target="_blank" rel="noopener">Xarray (Python)
• https://unidata.github.io/netcdf4-python/" target="_blank" rel="noopener">netCDF4 (Python)
• https://github.com/TUW-GEO/esa_cci_sm" target="_blank" rel="noopener">esa_cci_sm (Python)
• Similar tools exist for other programming languages (Matlab, R, etc.)
• Software packages and GIS tools can open netCDF files, e.g. CDO, NCO, QGIS, ArcGIS
• You can also use the GUI software Panoply to view the contents of each file

References

This item contains data and code used in experiments that produced the results for Sadler et. al (2022) (see below for full reference). We ran five experiments for the analysis, Experiment A, Experiment B, Experiment C, Experiment D, and Experiment AuxIn. Experiment A tested multi-task learning for predicting streamflow with 25 years of training data and using a different model for each of 101 sites. Experiment B tested multi-task learning for predicting streamflow with 25 years of training data and using a single model for all 101 sites. Experiment C tested multi-task learning for predicting streamflow with just 2 years of training data. Experiment D tested multi-task learning for predicting water temperature with over 25 years of training data. Experiment AuxIn used water temperature as an input variable for predicting streamflow. These experiments and their results are described in detail in the WRR paper. Data from a total of 101 sites across the US was used for the experiments. The model input data and streamflow data were from the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) dataset (Newman et. al 2014, Addor et. al 2017). The water temperature data were gathered from the National Water Information System (NWIS) (U.S. Geological Survey, 2016). The contents of this item are broken into 13 files or groups of files aggregated into zip files:

1. input_data_processing.zip: A zip file containing the scripts used to collate the observations, input weather drivers, and catchment attributes for the multi-task modeling experiments
2. flow_observations.zip: A zip file containing collated daily streamflow data for the sites used in multi-task modeling experiments. The streamflow data were originally accessed from the CAMELs dataset. The data are stored in csv and Zarr formats.
3. temperature_observations.zip: A zip file containing collated daily water temperature data for the sites used in multi-task modeling experiments. The data were originally accessed via NWIS. The data are stored in csv and Zarr formats.
4. temperature_sites.geojson: Geojson file of the locations of the water temperature and streamflow sites used in the analysis.
5. model_drivers.zip: A zip file containing the daily input weather driver data for the multi-task deep learning models. These data are from the Daymet drivers and were collated from the CAMELS dataset. The data are stored in csv and Zarr formats.
6. catchment_attrs.csv: Catchment attributes collatted from the CAMELS dataset. These data are used for the Random Forest modeling. For full metadata regarding these data see CAMELS dataset.
7. experiment_workflow_files.zip: A zip file containing workflow definitions used to run multi-task deep learning experiments. These are Snakemake workflows. To run a given experiment, one would run (for experiment A) 'snakemake -s expA_Snakefile --configfile expA_config.yml'
8. river-dl-paper_v0.zip: A zip file containing python code used to run multi-task deep learning experiments. This code was called by the Snakemake workflows contained in 'experiment_workflow_files.zip'.
9. random_forest_scripts.zip: A zip file containing Python code and a Python Jupyter Notebook used to prepare data for, train, and visualize feature importance of a Random Forest model.
10. plotting_code.zip: A zip file containing python code and Snakemake workflow used to produce figures showing the results of multi-task deep learning experiments.
11. results.zip: A zip file containing results of multi-task deep learning experiments. The results are stored in csv and netcdf formats. The netcdf files were used by the plotting libraries in 'plotting_code.zip'. These files are for five experiments, 'A', 'B', 'C', 'D', and 'AuxIn'. These experiment names are shown in the file name.
12. sample_scripts.zip: A zip file containing scripts for creating sample output to demonstrate how the modeling workflow was executed.
13. sample_output.zip: A zip file containing sample output data. Similar files are created by running the sample scripts provided.

Data Summary: US states grid mask file and NOAA climate regions grid mask file, both compatible with the 12US1 modeling grid domain. Note:The datasets are on a Google Drive. The metadata associated with this DOI contain the link to the Google Drive folder and instructions for downloading the data. These files can be used with CMAQ-ISAMv5.3 to track state- or region-specific emissions. See Chapter 11 and Appendix B.4 in the CMAQ User's Guide for further information on how to use the ISAM control file with GRIDMASK files. The files can also be used for state or region-specific scaling of emissions using the CMAQv5.3 DESID module. See the DESID Tutorial and Appendix B.4 in the CMAQ User's Guide for further information on how to use the Emission Control File to scale emissions in predetermined geographical areas. File Location and Download Instructions: Link to GRIDMASK files Link to README text file with information on how these files were created File Format: The grid mask are stored as netcdf formatted files using I/O API data structures (https://www.cmascenter.org/ioapi/). Information on the model projection and grid structure is contained in the header information of the netcdf file. The output files can be opened and manipulated using I/O API utilities (e.g. M3XTRACT, M3WNDW) or other software programs that can read and write netcdf formatted files (e.g. Fortran, R, Python). File descriptions These GRIDMASK files can be used with the 12US1 modeling grid domain (grid origin x = -2556000 m, y = -1728000 m; N columns = 459, N rows = 299). GRIDMASK_STATES_12US1.nc - This file containes 49 variables for the 48 states in the conterminous U.S. plus DC. Each state variable (e.g., AL, AZ, AR, etc.) is a 2D array (299 x 459) providing the fractional area of each grid cell that falls within that state. GRIDMASK_CLIMATE_REGIONS_12US1.nc - This file containes 9 variables for 9 NOAA climate regions based on the Karl and Koss (1984) definition of climate regions. Each climate region variable (e.g., CLIMATE_REGION_1, CLIMATE_REGION_2, etc.) is a 2D array (299 x 459) providing the fractional area of each grid cell that falls within that climate region. NOAA Climate regions: CLIMATE_REGION_1: Northwest (OR, WA, ID) CLIMATE_REGION_2: West (CA, NV) CLIMATE_REGION_3: West North Central (MT, WY, ND, SD, NE) CLIMATE_REGION_4: Southwest (UT, AZ, NM, CO) CLIMATE_REGION_5: South (KS, OK, TX, LA, AR, MS) CLIMATE_REGION_6: Central (MO, IL, IN, KY, TN, OH, WV) CLIMATE_REGION_7: East North Central (MN, IA, WI, MI) CLIMATE_REGION_8: Northeast (MD, DE, NJ, PA, NY, CT, RI, MA, VT, NH, ME) + Washington, D.C.* CLIMATE_REGION_9: Southeast (VA, NC, SC, GA, AL, GA) *Note that Washington, D.C. is not included in any of the climate regions on the website but was included with the “Northeast” region for the generation of this GRIDMASK file.

This dataset contains the experimental results described in Bergfeld et al. (2025). It includes three Propagation Saw Test (PST) experiments, each approximately 9 m long, performed side-by-side on a 37° slope. For each PST, we provide the full field of view along the crack-propagation direction. For the second and third PSTs, we additionally provide close-up recordings focused on the weak layer where cracking occurred. All data are supplied as netCDF files containing displacement and strain measurements derived from Digital Image Correlation (DIC) analysis. Metadata describing dimensions and units are stored directly within the netCDF files. We recommend using the xarray package in Python to read and work with these datasets. All figures presented in Bergfeld et al. (2025) can be reproduced using the included Python scripts. Information about the snowpack is provided in PDF, pickle, and CAAML file formats.

PV-gradient tropopause time series

General description

These datasets contain time series of the PV-gradient tropopause (PVG tropopause) introduced by A. Kunz (2011, doi:10.1029/2010JD014343) and calculated by K. Turhal (2024, paper " Variability and Trends in the PVG Tropopause", preprint in EGUsphere: https://doi.org/10.5194/egusphere-2024-471).

Data and methods

The PVG tropopause has been computed by means of the Eddy Tracking Toolkit (developed by J. Clemens and K. Turhal, to be published):

• from four reanalyses: ERA5, ERA-Interim, MERRA-2 and JRA-55
• for the time range 1980/01/01 -- 2017/12/31 in time steps of the according reanalyses, i.e. four times daily at 00h, 06h, 12h and 18h
• on each isentropic level, with potential temperatures (theta) ranging from 320 K to 380 K, in steps of 5 K for ERA5 and 10 K for the other reanalyses.

Contents

Datasets are provided for each year and isentropic level in NetCDF4 format, every file consisting of two groups for the northern and southern hemisphere. Each group contains the following variables, with time as dimension:

• time in seconds since 2000/01/01 00:00 UTC
• u_lim: Zonal wind speed at the PVG tropopause
• vh_lim: Horizontal wind speed at the PVG tropopause
• q_lim: Maximum of Q = vh * Grad PV
• eqlat_lim: Location of the PVG tropopause in equivalent latitudes
• latmean_lim: Location of the PVG tropopause in latitudes
• pv_lim: PV value at the PVG tropopause

In this upload, the PVG tropopause time series are included as *.zip files:

• ERA5 dataset: "pvg-tp_era5_ts.zip"
• ERA-Interim dataset: "pvg-tp_eraint_ts.zip"
• MERRA-2 dataset: "pvg-tp_merra2_ts.zip"
• JRA-55 dataset: "pvg-tp_jra55_ts.zip"
• Plots of time series for each reanalysis of the variables eqlat_lim, latmean_lim and pv_lim: "pvg_tropopause_timeseries_plots.zip".

How to use

The variables in these netCDF files are grouped by hemisphere. To read in the data, specify the group first ("NorthernHemisphere" or "SouthernHemisphere") and then the variable name (see list above). In Python, this can be done as follows:

import netCDF4 as nc

file="

If you would like to read in all variables in both hemispheres, you can loop e.g. as follows:

import netCDF4 as nc

file = "

Funding

This project has been funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – TRR 301 – Project-ID 428312742, TPChange: The Tropopause Region in a Changing Atmosphere (https://tpchange.de/).

Wave and sea surface temperature measurements collected by a Sofar Spotter buoy in 2023. The buoy was deployed on July 27, 2023 at 11:30 UTC northwest of Culebra Island, Puerto Rico, (18.3878 N, 65.3899 W) and recovered on Nov 5, 2023 at 12:45 UTC. Data are saved here in netCDF format, organized by month, and include directional wave statistics, GPS, and SST measurements at 30-minute intervals. Figures produced from these data are provided here as well. They include timeseries of wave height/period/direction and SST, GPS location, wave roses, and directional spectra. Additionally, raw CSV files from the Spotter's memory card can also be found below. NetCDF files can be read in python using the netCDF4 or Xarray packages, or through MATLAB using the "ncread()" command.

Data Summary:

CMAQv5.3 input data for a 01/01/2016 - 12/31/2016 simulation over the Continental US. Note:The datasets are on a Google Drive. The metadata associated with this DOI contain the link to the Google Drive folder and instructions for downloading the data.

File Location and Download Instructions:

• Link to input files for 2016_12US1 benchmark case
• Download instructions

File Format:

Abstract

Summary

• Global estimates of root-zone soil moisture from 01-1980 to 12-2024 at ~25 km spatial sampling based on the COMBINED product of ESA CCI SM v9.2.
• Method: Exponential filter model, calibrated with in situ measurements for 3 depth layers: 0-10, 10-40, 40-100 cm with uncertainty estimates. Additionally, one layer representing the average condition from 0-1 m depth is provided. See Pasik et al. (2023) for more details.
• Good agreement with independent reanalysis data (R ~0.6 [-] and ubRMSD <0.04 m³/m³), decreasing performance for deeper layers due to weaker coupling with surface SM.

Programmatic (bulk) download

You can use command-line tools such as wget or curl to download (and extract) data for multiple years. The following command will download and extract the complete data set to the local directory ~/Download on Linux or macOS systems.

#!/bin/bash

# Set download directory
DOWNLOAD_DIR=~/Downloads

base_url="https://researchdata.tuwien.at/records/tqrwj-t7r58/files"

# Loop through years 1980 to 2024 and download & extract data
for year in {1980..2024}; do
  echo "Downloading $year.zip..."
  wget -q -P "$DOWNLOAD_DIR" "$base_url/$year.zip"
  unzip -o "$DOWNLOAD_DIR/$year.zip" -d $DOWNLOAD_DIR
  rm "$DOWNLOAD_DIR/$year.zip"
done

Data details

Version Changelog

Changes in v9.2:

• The COMBINED product of v9.2 is used as input.
• The period was extended to 12-2024.

Software to open netCDF files

These data can be read by any software that supports Climate and Forecast (CF) conform metadata standards for netCDF files, such as:

• https://github.com/pydata/xarray" target="_blank" rel="noopener">Xarray (Python)
• https://unidata.github.io/netcdf4-python/" target="_blank" rel="noopener">netCDF4 (Python)
• https://github.com/TUW-GEO/esa_cci_sm" target="_blank" rel="noopener">esa_cci_sm (Python)
• Similar tools exist for other programming languages (Matlab, R, etc.)
• Software packages and GIS tools can open netCDF files, e.g. CDO, NCO, QGIS, ArcGIS
• You can also use the GUI software Panoply to view the contents of each file

Related Records

This record and all related records are part of the ESA CCI Soil Moisture science data records community.

This data repository contains the accompanying data for the study by Stradiotti et al. (2025). Developed as part of the ESA Climate Change Initiative (CCI) Soil Moisture project. Project website: https://climate.esa.int/en/projects/soil-moisture/

Journal Article (Open Access)

This dataset was created as part of the following study, which contains a description of the algorithm and validation results.

Stradiotti, P., Gruber, A., Preimesberger, W., & Dorigo, W. (2025). Accounting for seasonal retrieval errors in the merging of multi-sensor satellite soil moisture products. Science of Remote Sensing, 12, 100242. https://doi.org/10.1016/j.srs.2025.100242

Summary

This repository contains the final, merged soil moisture and uncertainty values from Stradiotti et al. (2025), derived using a novel uncertainty quantification and merging scheme. In the accompanying study, we present a method to quantify the seasonal component of satellite soil moisture observations, based on Triple Collocation Analysis. Data from three independent satellite missions are used (from ASCAT, AMSR2, and SMAP). We observe consistent intra-annual variations in measurement uncertainties across all products (primarily caused by dynamics on the land surface such as seasonal vegetation changes), which affect the quality of the received signals. We then use these estimates to merge data from the three missions into a single consistent record, following the approach described by Dorigo et al. (2017). The new (seasonal) uncertainty estimates are propagated through the merging scheme, to enhance the uncertainty characterization of the final merged product provided here.

Evaluation against in situ data suggests that the estimated uncertainties of the new product are more representative of their true seasonal behaviour, compared to the previously used static approach. Based on these findings, we conclude that using a seasonal TCA approach can provide a more realistic characterization of dataset uncertainty, in particular its temporal variation. However, improvements in the merged soil moisture values are constrained, primarily due to correlated uncertainties among the sensors.

Technical details

The dataset provides global daily gridded soil moisture estimates for the 2012-2023 period at 0.25° (~25 km) resolution. Daily images are grouped by year (YYYY), each subdirectory containing one netCDF image file for a specific day (DD), month (MM) in a 2-dimensional (longitude, latitude) grid system (CRS: WGS84). All file names follow the naming convention:

L3S-SSMS-MERGED-SOILMOISTURE-YYYYMMDD000000-fv0.1.nc

Data Variables

Each netCDF file contains 3 coordinate variables (WGS84 longitude, latitude and time stamp), as well as the following data variables:

• sm: (float) The Soil Moisture variable contains the daily average volumetric soil moisture content (m3/m3) in the soil surface layer (~0-5 cm) over a whole grid cell (0.25 degree). Based on (merged) observations from ASCAT, AMSR2 and SMAP using the new merging scheme described in our study.
• sm_uncertainty: (float) The Soil Moisture Uncertainty variable contains the uncertainty estimates (random error) for the ‘sm’ field. Based on the uncertainty estimation and propagation scheme described in our study.
• dnflag: (int) Indicator for satellite orbit(s) used in the retrieval (day/nighttime). 1=day, 2=night, 3=both
• flag: (int) Indicator for data quality / missing data indicator. For more details, see netcdf attributes.
• freqbandID: (int) Indicator for frequency band(s) used in the retrieval. For more details, see netcdf attributes.
• mode: (int) Indicator for satellite orbit(s) used in the retrieval (ascending, descending)
• sensor: (int) Indicator for satellite sensor(s) used in the retrieval. For more details, see netcdf attributes.
• t0: (float) Representative time stamp, based on overpass times of all merged satellites.

Software to open netCDF files

After extracting the .nc files from the downloaded zip archived, they can read by any software that supports Climate and Forecast (CF) standard conform netCDF files, such as:

• Xarray (python)
• netCDF4 (python)
• esa_cci_sm (python)
• Similar tools exists for other programming languages (Matlab, R, etc.)
• GIS and netCDF tools such as CDO, NCO, QGIS, ArCGIS.
• You can also use the GUI software Panoply to view the contents of each file

Funding

This dataset was produced with funding from the European Space Agency (ESA) Climate Change Initiative (CCI) Plus Soil Moisture Project (CCN 3 to ESRIN Contract No: 4000126684/19/I-NB "ESA CCI+ Phase 1 New R&D on CCI ECVS Soil Moisture"). Project website: https://climate.esa.int/en/projects/soil-moisture/

Data Summary:

File Location and Download Instructions:

• Link to MCIP Output Files
• Download instructions

File Format:

# ERA-NUTS (1980-2018)

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 350 599 hourly samples (from 01-01-1980 00:00:00 to 31-12-2019 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` of 0.01 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 3.7.

NOTE: air temperature, solar radiation, runoff and wind speed hourly data have been rounded with two decimal digits.

## 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 files`on 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.

This dataset provides global daily estimates of Root-Zone Soil Moisture (RZSM) content at 0.25° spatial grid resolution, derived from gap-filled merged satellite observations of 14 passive satellites sensors operating in the microwave domain of the electromagnetic spectrum. Data is provided from January 1991 to December 2023.

This dataset was produced with funding from the European Space Agency (ESA) Climate Change Initiative (CCI) Plus Soil Moisture Project (CCN 3 to ESRIN Contract No: 4000126684/19/I-NB "ESA CCI+ Phase 1 New R&D on CCI ECVS Soil Moisture"). Project website: https://climate.esa.int/en/projects/soil-moisture/" target="_blank" rel="noopener">https://climate.esa.int/en/projects/soil-moisture/. Operational implementation is supported by the Copernicus Climate Change Service implemented by ECMWF through C3S2 312a/313c.

Studies using this dataset [preprint]

This dataset is used by Hirschi et al. (2025) to assess recent summer drought trends in Switzerland.

Hirschi, M., Michel, D., Schumacher, D. L., Preimesberger, W., and Seneviratne, S. I.: Recent summer soil moisture drying in Switzerland based on measurements from the SwissSMEX network, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2025-416, in review, 2025.

Abstract

ESA CCI Soil Moisture is a multi-satellite climate data record that consists of harmonized, daily observations from various microwave satellite remote sensing sensors (Dorigo et al., 2017, 2024; Gruber et al., 2019). This version of the dataset uses the PASSIVE record as input, which contains only observations from passive (radiometer) measurements (scaling reference AMSR-E). The surface observations are gap-filled using a univariate interpolation algorithm (Preimesberger et al., 2025). The gap-filled passive observations serve as input for an exponential filter based method to assess soil moisture in different layers of the root-zone of soil (0-200 cm) following the approach by Pasik et al. (2023). The final gap-free root-zone soil moisture estimates based on passive surface input data are provided here at 4 separate depth layers (0-10, 10-40, 40-100, 100-200 cm) over the period 1991-2023.

Summary

• Gap-free root-zone soil moisture estimates from 1991-2023 at 0.25° spatial sampling from passive measurements
• Fields of application include: climate variability and change, land-atmosphere interactions, global biogeochemical cycles and ecology, hydrological and land surface modelling, drought applications, agriculture and meteorology
• More information: See Dorigo et al. (2017, 2024) and Gruber et al. (2019) for a description of the satellite base product and uncertainty estimates, Preimesberger et al. (2025) for the gap-filling, and Pasik et al. (2023) for the root-zone soil moisture and uncertainty propagation algorithm.

Programmatic Download

You can use command line tools such as wget or curl to download (and extract) data for multiple years. The following command will download and extract the complete data set to the local directory ~/Downloads on Linux or macOS systems.

#!/bin/bash

# Set download directory
DOWNLOAD_DIR=~/Downloads

base_url="https://researchdata.tuwien.ac.at/records/8dda4-xne96/files"

# Loop through years 1991 to 2023 and download & extract data
for year in {1991..2023}; do
  echo "Downloading $year.zip..."
  wget -q -P "$DOWNLOAD_DIR" "$base_url/$year.zip"
  unzip -o "$DOWNLOAD_DIR/$year.zip" -d $DOWNLOAD_DIR
  rm "$DOWNLOAD_DIR/$year.zip"
done

Data details

The dataset provides global daily estimates for the 1991-2023 period at 0.25° (~25 km) horizontal grid resolution. Daily images are grouped by year (YYYY), each subdirectory containing one netCDF image file for a specific day (DD), month (MM) in a 2-dimensional (longitude, latitude) grid system (CRS: WGS84). The file name has the following convention:

ESA_CCI_PASSIVERZSM-YYYYMMDD000000-fv09.1.nc

Data Variables

Each netCDF file contains 3 coordinate variables (WGS84 longitude, latitude and time stamp), as well as the following data variables:

• rzsm_1: (float) Root Zone Soil Moisture at 0-10 cm. Given in volumetric units [m3/m3].
• rzsm_2: (float) Root Zone Soil Moisture at 10-40 cm. Given in volumetric units [m3/m3].
• rzsm_3: (float) Root Zone Soil Moisture at 40-100 cm. Given in volumetric units [m3/m3].
• rzsm_4: (float) Root Zone Soil Moisture at 100-200. Given in volumetric units [m3/m3].
• uncertainty_1: (float) Root Zone Soil Moisture uncertainty at 0-10 cm from propagated surface uncertainties [m3/m3].
• uncertainty_2: (float) Root Zone Soil Moisture uncertainty at 10-40 cm from propagated surface uncertainties [m3/m3].
• uncertainty_3: (float) Root Zone Soil Moisture uncertainty at 40-100 cm from propagated surface uncertainties [m3/m3].
• uncertainty_4: (float) Root Zone Soil Moisture uncertainty at 100-200 cm from propagated surface uncertainties [m3/m3].

Additional information for each variable is given in the netCDF attributes.

Version Changelog

• v9.1
  • Initial version based on PASSIVE input data from ESA CCI SM v09.1 as used by Hirschi et al. (2025).

Software to open netCDF files

These data can be read by any software that supports Climate and Forecast (CF) conform metadata standards for netCDF files, such as:

• https://github.com/pydata/xarray" target="_blank" rel="noopener">Xarray (python)
• https://unidata.github.io/netcdf4-python/" target="_blank" rel="noopener">netCDF4 (python)
• https://github.com/TUW-GEO/esa_cci_sm">esa_cci_sm (python)
• Similar tools exists for other programming languages (Matlab, R, etc.)
• Software packages and GIS tools can open netCDF files, e.g. CDO, NCO, QGIS, ArCGIS
• You can also use the GUI software Panoply to view the contents of each file

References

• Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D., Hirschi, M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y., Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C., van der Schalie, R., Seneviratne, S. I., Smolander, T., and Lecomte, P.: ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions, Remote Sensing of Environment, 203, 185-215, 10.1016/j.rse.2017.07.001, 2017
• Dorigo, W., Stradiotti, P., Preimesberger, W., Kidd, R., van der Schalie, R., Frederikse, T., Rodriguez-Fernandez, N., & Baghdadi, N. (2024). ESA Climate Change Initiative Plus - Soil Moisture Algorithm Theoretical Baseline Document (ATBD) Supporting Product Version 09.0. Zenodo. https://doi.org/10.5281/zenodo.13860922
• Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W.: Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019, 2019.
• Hirschi, M., Michel, D., Schumacher, D. L., Preimesberger, W., Seneviratne, S. I.: Recent summer soil moisture drying in Switzerland based on the SwissSMEX network, 2025 (paper submitted)
• Pasik, A., Gruber, A., Preimesberger, W., De Santis, D., and Dorigo, W.: Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations, Geosci. Model Dev., 16, 4957–4976, https://doi.org/10.5194/gmd-16-4957-2023, 2023
• Preimesberger, W., Stradiotti, P., and Dorigo, W.: ESA CCI Soil Moisture GAPFILLED: An independent global gap-free satellite climate data record with uncertainty estimates, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-610, in review, 2025.

Related Records

Please see the ESA CCI Soil Moisture science data records community for more records based on ESA CCI SM.

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