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

Dataset Paper (Open Access)

A description of this dataset, including the methodology and validation results, is available at:

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, 17, 4305–4329, https://doi.org/10.5194/essd-17-4305-2025, 2025.

Abstract

ESA CCI Soil Moisture is a multi-satellite climate data record that consists of harmonized, daily observations coming from 19 satellites (as of v09.1) operating in the microwave domain. The wealth of satellite information, particularly over the last decade, facilitates the creation of a data record with the highest possible data consistency and coverage.
However, data gaps are still found in the record. This is particularly notable in earlier periods when a limited number of satellites were in operation, but can also arise from various retrieval issues, such as frozen soils, dense vegetation, and radio frequency interference (RFI). These data gaps present a challenge for many users, as they have the potential to obscure relevant events within a study area or are incompatible with (machine learning) software that often relies on gap-free inputs.
Since the requirement of a gap-free ESA CCI SM product was identified, various studies have demonstrated the suitability of different statistical methods to achieve this goal. A fundamental feature of such gap-filling method is to rely only on the original observational record, without need for ancillary variable or model-based information. Due to the intrinsic challenge, there was until present no global, long-term univariate gap-filled product available. In this version of the record, data gaps due to missing satellite overpasses and invalid measurements are filled using the Discrete Cosine Transform (DCT) Penalized Least Squares (PLS) algorithm (Garcia, 2010). A linear interpolation is applied over periods of (potentially) frozen soils with little to no variability in (frozen) soil moisture content. Uncertainty estimates are based on models calibrated in experiments to fill satellite-like gaps introduced to GLDAS Noah reanalysis soil moisture (Rodell et al., 2004), and consider the gap size and local vegetation conditions as parameters that affect the gapfilling performance.

Summary

• Gap-filled global estimates of volumetric surface soil moisture from 1991-2023 at 0.25° sampling
• Fields of application (partial): climate variability and change, land-atmosphere interactions, global biogeochemical cycles and ecology, hydrological and land surface modelling, drought applications, and meteorology
• Method: Modified version of DCT-PLS (Garcia, 2010) interpolation/smoothing algorithm, linear interpolation over periods of frozen soils. Uncertainty estimates are provided for all data points.
• More information: See Preimesberger et al. (2025) and https://doi.org/10.5281/zenodo.8320869" target="_blank" rel="noopener">ESA CCI SM Algorithm Theoretical Baseline Document [Chapter 7.2.9] (Dorigo et al., 2023)

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 ~/Download on Linux or macOS systems.

#!/bin/bash

# Set download directory
DOWNLOAD_DIR=~/Downloads

base_url="https://researchdata.tuwien.at/records/3fcxr-cde10/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:

ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED_GAPFILLED-YYYYMMDD000000-fv09.1r1.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 reflects estimates of daily average volumetric soil moisture content (m3/m3) in the soil surface layer (~0-5 cm) over a whole grid cell (0.25 degree).
• sm_uncertainty: (float) The Soil Moisture Uncertainty variable reflects the uncertainty (random error) of the original satellite observations and of the predictions used to fill observation data gaps.
• sm_anomaly: Soil moisture anomalies (reference period 1991-2020) derived from the gap-filled values (`sm`)
• sm_smoothed: Contains DCT-PLS predictions used to fill data gaps in the original soil moisture field. These values are also provided for cases where an observation was initially available (compare `gapmask`). In this case, they provided a smoothed version of the original data.
• gapmask: (0 | 1) Indicates grid cells where a satellite observation is available (1), and where the interpolated (smoothed) values are used instead (0) in the 'sm' field.
• frozenmask: (0 | 1) Indicates grid cells where ERA5 soil temperature is <0 °C. In this case, a linear interpolation over time is applied.

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

Version Changelog

Changes in v9.1r1 (previous version was v09.1):

• This version uses a novel uncertainty estimation scheme as described in Preimesberger 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

• 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, 17, 4305–4329, https://doi.org/10.5194/essd-17-4305-2025, 2025.
• Dorigo, W., Preimesberger, W., Stradiotti, P., Kidd, R., van der Schalie, R., van der Vliet, M., Rodriguez-Fernandez, N., Madelon, R., & Baghdadi, N. (2023). ESA Climate Change Initiative Plus - Soil Moisture Algorithm Theoretical Baseline Document (ATBD) Supporting Product Version 08.1 (version 1.1). Zenodo. https://doi.org/10.5281/zenodo.8320869
• Garcia, D., 2010. Robust smoothing of gridded data in one and higher dimensions with missing values. Computational Statistics & Data Analysis, 54(4), pp.1167-1178. Available at: https://doi.org/10.1016/j.csda.2009.09.020
• Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data Assimilation System, Bulletin of the American Meteorological Society, 85, 381 – 394, https://doi.org/10.1175/BAMS-85-3-381, 2004.

Related Records

The following records are all part of the ESA CCI Soil Moisture science data records community

Phsyical Oceanographic Circulation Study _NCProperties=version=2,netcdf=4.6.3,hdf5=1.10.4 acknowledgement=N/A cdm_altitude_proxy=PRES cdm_data_type=Profile cdm_profile_variables=DIRECTION,JULD_QC,JULD_LOCATION,latitude,longitude,POSITION_QC,PROFILE_PRES_QC,PROFILE_PSAL_QC,PROFILE_TEMP_QC,PRES,PRES_QC,PRES_ADJUSTED,PRES_ADJUSTED_QC,PRES_ADJUSTED_ERROR,PSAL,PSAL_QC,PSAL_ADJUSTED,PSAL_ADJUSTED_QC,PSAL_ADJUSTED_ERROR,TEMP,TEMP_QC,TEMP_ADJUSTED,TEMP_ADJUSTED_QC,TEMP_ADJUSTED_ERROR,profile comment=N/A contributor_email=devops@rpsgroup.com contributor_name=RPS contributor_role=editor contributor_role_vocabulary=https://vocab.nerc.ac.uk/collection/G04/current/ contributor_url=rpsgroup.com Conventions=ACDD-1.3, CF-1.7, IOOS-1.2 date_metadata_modified=2021-03-15T14:51:43.046093 Easternmost_Easting=-86.85241 featureType=Profile geospatial_bounds=MULTIPOINT (-86.92479 27.44745, -86.94875 27.50054, -86.97515 27.55698, -86.98268 27.59141, -86.95361 27.75151, -87.08473 27.93702, -87.16455 27.8003 , -87.48748 27.69569, -87.79848 27.65302, -88.00196 27.50943, -87.9856 27.50101, -87.86047 27.63956, -88.06809 27.73081, -88.09864 27.78135, -88.10715 27.82397, -88.1108 27.85767, -88.10087 27.91051, -88.13338 27.86221, -88.16878 27.85377, -88.05664 28.00782, -88.02269 28.14189, -87.93753 28.32363, -87.77153 28.46238, -87.66765 28.34248, -87.59172 28.36801, -87.50798 28.56317, -87.38511 28.60202, -87.31172 28.60896, -87.28674 28.62204, -87.24137 28.4645 , -86.85241 28.17752, -87.06037 28.34397, -87.13829 28.3452 , -87.31952 28.54903, -87.38997 28.94832, -87.6393 29.06555, -87.41635 29.08024, -87.38668 28.99967, -87.47774 29.01179, -87.33341 28.79479, -87.45669 28.63872, -87.53495 28.50758, -87.54689 28.29854, -87.74877 28.45214, -87.79449 28.38046, -87.62084 28.30637, -87.65327 28.29676, -87.51157 28.37384, -87.52016 28.64711, -87.49763 28.90244, -87.6509 29.01665, -88.01583 28.92765, -88.35217 28.78962, -88.30744 28.64899, -88.17691 28.58762, -88.22921 28.35054, -88.66493 28.01616, -88.87602 27.89182, -89.26091 27.80431, -89.32015 27.70812, -89.37818 27.71823, -89.58081 27.68473, -89.54697 27.82638, -89.06157 28.11111, -88.64328 28.26838, -88.33775 28.23144, -88.58438 27.98143, -88.2644 26.93499, -88.62807 26.15466, -88.84813 26.56506, -89.15347 26.48345, -89.65883 26.93092, -89.35677 26.68739, -90.03865 26.61342, -90.55446 26.69557, -91.06189 26.41492, -91.43119 26.85679, -91.31301 26.97152, -90.96983 26.77915, -90.90907 26.79166, -90.99742 27.01004, -90.86617 26.89158, -90.83422 26.61472, -91.14603 26.23077, -92.33082 25.88828, -93.33612 26.01557, -94.64683 26.00732, -94.94638 26.10918, -95.11384 25.84752, -95.52623 24.59142, -95.94594 23.87184, -96.39403 23.6656 , -96.67977 23.76634, -96.92028 23.55518, -96.95649 23.3301 , -96.87699 23.22797) geospatial_bounds_crs=EPSG:4326 geospatial_bounds_vertical_crs=EPSG:5831 geospatial_lat_max=29.08024 geospatial_lat_min=23.22797 geospatial_lat_units=degrees_north geospatial_lon_max=-86.85241 geospatial_lon_min=-96.95649 geospatial_lon_units=degrees_east geospatial_vertical_positive=down history=_prof.nc and _meta.nc concatenated and enhanced metadata by RPS 2021-03-15T14:51:43.046075 id=4901476 infoUrl=https://gcoos.org institution=US Argo (US GDAC) instrument=US ARGO Profiler naming_authority=edu.tamucc.gulfhub Northernmost_Northing=29.08024 note_CHAR_variables=RPS METADATA ENHANCEMENT NOTE Variables of data type 'CHAR' have been altered by the xarray and netCDF4-python libraries to contain an extra dimension (often denoted as 'string1'). This is due to an underlying issue in the libraries: https://github.com/pydata/xarray/issues/1977. Upon examination, one will find the data has not been altered but only changed shape. We realize this is sub-optimal and apologize for any inconveniences this may cause. platform=subsurface_float platform_id=4901476 platform_name=US Argo APEX Float 4901476 platform_vocabulary=GCMD Keywords Version 8.7 processing_level=Data QA/QC performed by USARGO program=Deep Langrangian Observations in the Gulf of Mexico (funding: BOEM) project=Deep Circulation in the Gulf of Mexico: A Lagrangian Study references=https://espis.boem.gov/final%20reports/5471.pdf source=observation sourceUrl=(local files) Southernmost_Northing=23.22797 standard_name_vocabulary=CF Standard Name Table v72 time_coverage_duration=P0002-03-14T16:38:02 time_coverage_end=2015-11-21T22:46:01Z time_coverage_resolution=P0000-00-00T10:50:04 time_coverage_start=2013-08-07T06:07:59Z Westernmost_Easting=-96.95649

These data are ocean profile data measured by profiling Argo S2A floats at a specific latitude, longitude, and date nominally from the surface to 2000 meters depth. Pressure, in situ temperature (ITS-90), and practical salinity are provided at 1-m increments through the water column. Argo data from Gulf of Mexico (GOM) LC1 (9 floats) and LC2 (12 floats) were delayed mode quality controlled and submitted to Global Data Assembly Centers (GDACs) in May 2020. All available profiles are planned to be revisited and evaluated in early 2021. Float no. 4903233 started showing drift in salinity at profile no. 77, and the salinity data will be carefully examined with a new adjustment in early 2021. _NCProperties=version=2,netcdf=4.6.3,hdf5=1.10.4 cdm_altitude_proxy=PRES cdm_data_type=Profile cdm_profile_variables=profile comment=free text contributor_email=devops@rpsgroup.com contributor_name=RPS contributor_role=editor contributor_role_vocabulary=https://vocab.nerc.ac.uk/collection/G04/current/ contributor_url=https://www.rpsgroup.com/ Conventions=CF-1.7, ACDD-1.3, IOOS-1.2, Argo-3.2, COARDS date_metadata_modified=2020-12-22T15:54:25Z Easternmost_Easting=-89.55605 featureType=Profile geospatial_bounds=POINT (-89.55605 26.15305) geospatial_bounds_crs=EPSG:4326 geospatial_lat_max=26.15305 geospatial_lat_min=26.15305 geospatial_lat_units=degrees_north geospatial_lon_max=-89.55605 geospatial_lon_min=-89.55605 geospatial_lon_units=degrees_east history=2020-03-29T14:00:40Z creation id=R4903256_038 infoUrl=http://www.argodatamgt.org/Documentation institution=GCOOS instrument=Argo instrument_vocabulary=GCMD Earth Science Keywords. Version 5.3.3 keywords_vocabulary=GCMD Science Keywords naming_authority=edu.tamucc.gulfhub Northernmost_Northing=26.15305 note_CHAR_variables=RPS METADATA ENHANCEMENT NOTE Variables of data type 'CHAR' have been altered by the xarray and netCDF4-python libraries to contain an extra dimension (often denoted as 'string1'). This is due to an underlying issue in the libraries: https://github.com/pydata/xarray/issues/1977. Upon examination, one will find the data has not been altered but only changed shape. We realize this is sub-optimal and apologize for any inconveniences this may cause. note_FillValue=RPS METADATA ENHANCEMENT NOTE Many variables in this dataset are of type 'char' and have a '_FillValue' attribute which is interpreted through NumPy as 'b', an empty byte string. This causes serialization issues. As a result, all variables of type 'char' with '_FillValue = b' have had the _FillValue attribute removed to avoid serialization conflicts. However, no data has been changed, so the _FillValue is still "b' '". platform=subsurface_float platform_name=Argo Float platform_vocabulary=IOOS Platform Vocabulary processing_level=Argo data are received via satellite transmission, decoded and assembled at national DACs. These DACs apply a set of automatic quality tests (RTQC) to the data, and quality flags are assigned accordingly. In the delayed-mode process (DMQC), data are subjected to visual examination and are re-flagged where necessary. For the float data affected by sensor drift, statistical tools and climatological comparisons are used to adjust the data for sensor drift when needed. For each float that has been processed in delayed-mode, the OWC method (Owens and Wong, 2009; Cabanes et al., 2016) is run with four different sets of spatial and temporal decorrelation scales and the latest available reference dataset. If the salinity adjustments obtained from the four runs all differ significantly from the existing adjustment, then the salinity data from the float are re-examined and a new adjustment is suggested if necessary. The usual practice is to examine the profiles in delayed-mode initially about 12 months after they are collected, and then revisit several times as more data from the floats are obtained (see details in Wong et al., 2020). program=Understanding Gulf Ocean Systems (UGOS) project=National Academy of Science Understanding Gulf Ocean Systems 'LC-Floats - Near Real-time Hydrography and Deep Velocity in the Loop Current System using Autonomous Profilers' Program references=http://www.argodatamgt.org/Documentation sea_name=Gulf of Mexico source=Argo float sourceUrl=(local files) Southernmost_Northing=26.15305 standard_name_vocabulary=CF Standard Name Table v67 subsetVariables=CYCLE_NUMBER, DIRECTION, DATA_MODE, time, JULD_QC, JULD_LOCATION, latitude, longitude, POSITION_QC, CONFIG_MISSION_NUMBER, PROFILE_PRES_QC, PROFILE_TEMP_QC, PROFILE_PSAL_QC time_coverage_duration=P0000-00-00T00:00:00 time_coverage_end=2020-03-24T12:32:26Z time_coverage_resolution=P0000-00-00T00:00:00 time_coverage_start=2020-03-24T12:32:26Z user_manual_version=3.2 Westernmost_Easting=-89.55605

This dataset is used as a "ground truth" for investigating the performance of a volumetric reconstruction technique of electric current densities, intended to be applied to the EISCAT 3D radar system. The technique is outlined in a mnuscript in preparation, to be referred to here once submitted. The volumetric reconstruction code can be found here: https://github.com/jpreistad/e3dsecs

This dataset contain four files:

1) Dataset file 'gemini_dataset.nc'. This is a dump from the end of a GEMINI model run driven with a pair of up/down FAC above the region around the EISCAT 3D facility. Detailes of the GEMINI model can be found here: https://doi.org/10.5281/zenodo.3528915 . This is a NETCDF file, intended to be opened with xarray in python:

import xaray

dataset = xarray.open_dataset('gemini_dataset.nc')

2) Grid file 'gemini_grid.h5'. This file is needed to get information about the grid that the values from GEMINI are represented in. The E3DSECS library (https://github.com/jpreistad/e3dsecs) has the necessary code to open this file and put it into the dictionary structure used in that package.

3) The GEMINI simulation config file 'config.nml' used to produce the simulation.

4) The GEMINI boundary file 'fac_said.py' used to produce the boundary conditions for the simulation

Together files 3 and 4 could be used to reproduce the full simulation of the GEMINI model, which is freely available at https://github.com/gemini3d

The configuration files for this particular run are also available at this location:

https://github.com/gemini3d/gemini-examples/tree/main/init/aurora_curv

SHNITSEL

The Surface Hopping Nested Instances Training Set for Excited-State Learning (SHNITSEL) is a comprehensive data repository designed to support the development and benchmarking of excited-state dynamics methods.

Configuration Space

SHNITSEL contains datasets for nine organic molecules that represent a diverse range of photochemical behaviors. The following molecules are included in the dataset:

Alkenes: ethene (A01), propene (A02), 2-butene (A03)

Ring structures: fulvene (R01), 1,3-cyclohexadiene (R02), tyrosine (R03)

Other molecules: methylenimmonium cation (I01), methanethione (T01), diiodomethane (H01)

Property Space

These datasets provide key electronic properties for singlet and triplet states, including energies, forces, dipole moments, transition dipole moments, nonadiabatic couplings, and spin-orbit couplings, computed at the multi-reference ab initio level. The data is categorized into static and dynamic data, based on its origin and purpose.

Static data (#147,169 data points in total) consists of sampled molecular structures without time-dependent information, covering relevant vibrational and conformational spaces. These datasets are provided for eight molecules: A01, A02, A03, R01, R03, I01, T01, and H01

Dynamic data (#444,581 data points in total) originates from surface hopping simulations and captures the evolution of molecular structures and properties over time, as they propagate on potential energy surfaces according to Newton’s equations of motion. These datasets are provided for five molecules: A01, A02, A03, R02, and I01

Data Structure and Workflow

The data is stored in xarray format, using xarray.Dataset objects for efficient handling of multidimensional data. Key dimensions include electronic states, couplings, atoms, and time frames for dynamic data. The dataset is scalable and compatible with large datasets, stored in NetCDF4 format within HDF5 for optimal performance. Tools for data processing, visualization, and integration into machine learning workflows are provided by the shnitsel Python package published on Github (shnitsel-tools) .(https://github.com/SHNITSEL/shnitsel-tools).

An overview of the molecular structures and visualizations of key properties (from trajectory data) are compiled on the SHNITSEL webpage (https://shnitsel.github.io/).

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.

Results from the Python Coastal Impacts and Adaptation Model (pyCIAM), the inputs and source code necessary to replicate these outputs, and the results presented in Depsky et al. 2023.

All zipped Zarr stores can be downloaded and accessed locally or can be directly accessed via code similar to the following:

from fsspec.implementations.zip import ZipFileSystem import xarray as xr xr.open_zarr(ZipFileSystem(url_of_file_in_record}}).get_mapper())

File Inventory

Products

pyCIAM_outputs.zarr.zip: Outputs of the pyCIAM model, using the SLIIDERS dataset to define socioeconomic and extreme sea level characteristics of coastal regions and the 17th, 50th, and 83rd quantiles of local sea level rise as projected by various modeling frameworks (LocalizeSL and FACTS) and for multiple emissions scenarios and ice sheet models.

pyCIAM_outputs_{case}.nc: A NetCDF version of pyCIAM_outputs, in which the netcdf files are divided up by adaptation "case" to reduce file size.

diaz2016_outputs.zarr.zip: A replication of the results from Diaz 2016 - the model upon which pyCIAM was built, using an identical configuration to that of the original model.

suboptimal_capital_by_movefactor.zarr.zip: An analysis of the observed present-day allocation of capital compared to a "rational" allocation, as a function of the magnitude of non-market costs of relocation assumed in the model. See Depsky et al. 2023 for further details.

Inputs

ar5-msl-rel-2005-quantiles.zarr.zip: Quantiles of projected local sea level rise as projected from the LocalizeSL model, using a variety of temperature scenarios and ice sheet models developed in Kopp 2014, Bamber 2019, DeConto 2021, IPCC SROCC. The results contained in pyCIAM_outputs.zarr.zip cover a broader (and newer) range of SLR projections from a more recent projection framework (FACTS); however, these data are more easily obtained from the appropriate Zenodo records and thus are not hosted in this one.

diaz2016_inputs_raw.zarr.zip: The coastal inputs used in Diaz 2016, obtained from GitHub and formatted for use in the Python-based pyCIAM. These are based on the Dynamic Integrated Vulnerability Assessment (DIVA) dataset.

surge-lookup-seg(_adm).zarr.zip: Pre-computed lookup tables estimating average annual losses from extreme sea levels due to mortality and capital stock damage. This is an intermediate output of pyCIAM and is not necessary to replicate the model results. However, it is more time consuming to produce than the rest of the model and is provided for users who may wish to start from the pre-computed dataset. Two versions are provided - the first contains estimates for each unique intersection of ~50km coastal segment and state/province-level administrative unit (admin-1). This is derived from the characteristics in SLIIDERS. The second is simply estimated on a version of SLIIDERS collapsed over administrative units to vary only over coastal segments. Both are used in the process of running pyCIAM.

ypk_2000_2100.zarr.zip: An intermediate output in creating SLIIDERS that contains country-level projections of GDP, capital stock, and population, based on the Shared Socioeconomic Pathways (SSPs). This is only used in normalizing costs estimated in pyCIAM by country and global GDP to report in Depsky et al. 2023. It is not used in the execution of pyCIAM but is provided to replicate results reported in the manuscript.

Source Code

pyCIAM.zip: Contains the python-CIAM package as well as a notebook-based workflow to replicate the results presented in Depsky et al. 2023. It also contains two master shell scripts (run_example.sh and run_full_replication.sh) to assist in executing a small sample of the pyCIAM model or in fully executing the workflow of Depsky et al. 2023, respectively. This code is consistent with release 1.2.0 in the pyCIAM GitHub repository and is available as version 1.2.0 of the python-CIAM package on PyPI.

Version history:

1.2

Point data-acquisition.ipynb to updated Zenodo deposit that fixes the dtype of subsets variable in diaz2016_inputs_raw.zarr.zip to be bool rather than int8

Variable name bugfix in data-acquisition.ipynb

Add netcdf versions of SLIIDERS and the pyCIAM results to upload-zenodo.ipynb

Update results in Zenodo record to use SLIIDERS v1.2

1.1.1

Bugfix to inputs/diaz2016_inputs_raw.zarr.zip to make the subsets variable bool instead of int8.

1.1.0

Version associated with publication of Depsky et al., 2023

The grid of precomputed synthetic spectra of planetary atmosphere BT-SETTL CIFIST (Allard et al. 2013) in the xarray format. This format is used in the forward modeling code ForMoSA (https://github.com/exoAtmospheres/ForMoSA, Petrus et al. 2020, 2021, 2023).

This grid explores the parameters: Teff = 1200 - 5000 log(g) = 2.5 - 5.5

# 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.

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.

The grid of precomputed synthetic spectra of planetary atmosphere Exo-REM (Charnay et al. 2018) in the xarray format. This format is used in the forward modeling code ForMoSA (https://github.com/exoAtmospheres/ForMoSA, Petrus et al. 2020, 2021, 2023).

This grid explores the parameters: Teff = 400 - 2000 log(g) = 3.0 - 5.0 [M/H] = -0.5 - 0.5 C/O = 0.1 - 0.8

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/

These are netCDF files, created using python/xarray. They contain the simulation results gotten from running the self_lens package (https://github.com/guynir42/self_lens) with a few surveys (ZTF, TESS, LSST, DECAM, CURIOS, CURIOS_ARRAY, LAST) over simulated binaries containing two white dwarfs (WDs).

Each file contains the results for the number of detections and effective volume for one survey, over a large parameter space of WD-WD binaires. For each binary we simulate the self-lensing flare and estimate the ability of a survey to observe that flare, at different distances of the system from Earth.

These datasets are needed to make the plots for an upcoming paper (Nir & Bloom, in prep). In the self_lens package, run the test_produce_plots.py to pull down these files to a local folder and use them to make plots.

An accompanying dataset includes the same files for WDs in binaries with neutron stars and black holes (BHs).

Abstract: Theoretical and experimental interest in the transport and deposition of sediments from rivers to oceans has increased rapidly over the last two decades. The marine ecosystem is strongly affected by mixing at river mouths, with for instance anthropogenic actions like pollutant spreading. Particle-laden flows entering a lighter ambient fluid (hyperpycnal flows) can plunge at a sufficient depth, and their deposits might preserve a remarkable record across a variety of climatic and tectonic settings. Numerical simulations play an essential role in this context since they provide information on all flow variables for any point of time and space. This work offers valuable Spatio-temporal information generated by turbulence-resolving 3D simulations of poly-disperse hyperpycnal plumes over a tilted bed. The simulations are performed with the high-order flow solver Xcompact3d, which solves the incompressible Navier-Stokes equations on a Cartesian mesh using high-order finite-difference schemes. Five cases are presented, with different values for flow discharge and sediment concentration at the inlet. A detailed comparison with experimental data and analytical models is already available in the literature. The main objective of this work is to present a new data-set that shows the entire three-dimensional Spatio-temporal evolution of the plunge phenomenon and all the relevant quantities of interest.

Description: Data from the five simulations are included (cases 2, 4, 5, 6, and 7). The output files from Xcompact3d were converted to NetCDF, including coordinates and metadata, aiming to be more friendly than raw binaries.

More details, including examples about how to read and plot the dataset using Python and xarray, are available at GitHub.

These data were produced with WRFlux v1.2.1 (https://github.com/matzegoebel/WRFlux/) from a numerical simulation with the community model WRF. Simulations represent the evolution of a convective boundary layer in the atmosphere over an idealized 2D mountain ridge. The data are published in connection with the article "Numerically consistent budgets of potential temperature, momentum and moisture in Cartesian coordinates: Application to the WRF model" in "Geoscientific Model Development" (https://doi.org/10.5194/gmd-15-669-2022).

Three-dimensional (x, z, t) fields of five prognostic variables are provided: Potential temperature (T), water vapor mixing ratio (Q), cross-mountain (U), along-mountain (V), and vertical windspeed (W). All fields are averaged in time (30 min averaging interval) and in the along-mountain direction y.

The repository contains the following files:

grid.nc : variables related to the WRF numerical grid, air density [U,W,T,Q]_flux.nc : resolved and subgrid-scale fluxes [U,W,T,Q]_tendency.nc : resolved and subgrid-scale tendency components UVWT_MEAN.nc : averaged values of the variables themselves plotting.py : python script to approximately reproduce the figures of the paper. Requires the python packages matplotlib, xarray, and netcdf4.

Figure 6 in the paper cannot be accurately reproduced with these data since the original figure uses 4D (x, y, z, t) output.

For details on the simulation, refer to the article.