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

Dataset paper (public preprint)

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 Discuss. [preprint], https://doi.org/10.5194/essd-2024-610, in review, 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 Discuss. [preprint], https://doi.org/10.5194/essd-2024-610, in review, 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 Soil Moisture Climate Data Records from satellites community

If you used our dataset, please cite our reference:

Zhang, Q., Yuan, Q., Li, J., Wang, Y., Sun, F., and Zhang, L.: Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019, Earth Syst. Sci. Data, 13, 1385–1401, https://doi.org/10.5194/essd-13-1385-2021, 2021.

Description:

• A seamless global daily (SGD) AMSR2 soil moisture long-term (2013-2019) dataset is generated through the proposed model. This daily products include 2553 global soil moisture NetCDF4 files, starting from Jan 01, 2013 to Dec 31, 2019 (about 20GB memory after uncompressing this zip file).
• To further validate the effectiveness of these products, three verification ways are employed as follow: 1) In-situ validation; 2) Time-series validation; And 3) simulated missing regions validation. More validation results can be viewed at SGD-SM.
• An example Python code of extracting this dataset is also available at https://github.com/qzhang95/SGD-SM.
• Official LPRM AMSR2 Descending L3 soil moisture products indeed only have 28 daily files in May 2013 (missing data files in date May 11, May 12, and May 13).
• This soil moisture dataset is comprised of netCDF4 (*.nc) files. Therefore, users need to install netCDF4 toolkit before reading the data:

  pip install netCDF4
  pip install numpy

• It should be noted that the original and reconstructed soil moisture data are both recorded in one NC file. User can read the original data, reconstructed data, and mask data as follows:

• Data = nc.Dataset(NC_file_position)
  Ori_data = Data.variables['original_sm_c1']
  Rec_data = Data.variables['reconstructed_sm_c1']
  Ori = Ori_data[0:720, 0:1440]
  Rec = Rec_data[0:720, 0:1440]
  Mask_ori = np.ma.getmask(Ori)

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

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

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.

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/

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/

The netCDF file included here corresponds to datasets used in the Biogeosciences paper entitled "Evaluating the Arabian Sea as a regional source of atmospheric CO2: seasonal variability and drivers" by Alain de Verneil, Zouhair Lachkar, Shafer Smith, and Marina Levy The data included here comprises of model output used in the paper to generate figures in the main manuscript. Many of the figures also contain data from publicly available sources, which is detailed in the "Data availability" section at the end of the paper. The data are in standard netCDF file format, readily readable using netCDF tools (i.e. netCDF4 package in Python, ncread function in Matlab, etc.). Variables names, dimensions, and units are described in the metadata within the netCDF file. Questions regarding this dataset and how it can be used to reproduce the results in the article can be forwarded to Alain de Verneil through email at ajd11@nyu.edu

This database consists of a high-resolution village-level drought dataset for major Indian states for the past 43 years (1981 – 2022) for each month. It was created by utilising the CHIRPS precipitation and GLEAM evapotranspiration datasets. GLEAMS dataset based on the well recognised Priestley-Taylor equation to estimate potential evapotranspiration (PET) based on observations of surface net radiation and near-surface air temperature. The SPEI was calculated for spatial grids of 5x5 km for the SPEI 3-month time scale, suitable for agricultural drought monitoring.
This high-resolution SPEI dataset was integrated with Indian village boundaries and associated census attribute dataset. This allows researchers to perform multi-disciplinary investigations, e.g., climate migration modelling, drought hazards, and exposure assessment. The development of the dataset has been performed while keeping potential users in mind. Therefore, the dataset can be integrated into a GIS system for visualization (using .mid/.mif format) and into Python programming for modelling and analysis (using .csv). For advanced analysis, I have also provided it in netCDF format, which can be read in Python using xarray or the netcdf4 library. More details are in the README.pdf file.

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=-86.32756 featureType=Profile geospatial_bounds=POINT (-86.32756 26.2932) geospatial_bounds_crs=EPSG:4326 geospatial_lat_max=26.2932 geospatial_lat_min=26.2932 geospatial_lat_units=degrees_north geospatial_lon_max=-86.32756 geospatial_lon_min=-86.32756 geospatial_lon_units=degrees_east history=2020-04-15T22:00:52Z creation id=R4903232_060 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=26.2932 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=26.2932 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=2020-04-10T19:39:59Z time_coverage_resolution=P0000-00-00T00:00:00 time_coverage_start=2020-04-10T19:39:59Z user_manual_version=3.2 Westernmost_Easting=-86.32756

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="" d = nc.Dataset(file)

read in a variable. Syntax: d["group name"]["variable name"][:]. For example:

latmean_lim = d["NorthernHemisphere"]["latmean_lim"][:]

test print

print(f"First value of latmean_lim in NH: {latmean_lim[0]}")

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

import netCDF4 as nc

file = "" d = nc.Dataset(file)

iterate through both hemispheres

for hem in ["NorthernHemisphere", "SouthernHemisphere"]:

# select the group to each hemisphere in the netCDF file
g = d.groups[hem]

# iterate through variables in each hemisphere. "v" is the name of each variable in the group.
for v in g.variables:

  # read in the data for variable 'v' in hemisphere 'hem' as an array
  var = g[v][:]

  # just a test print, optional
  print(f"First value of {v} in {hem.replace('Hem', ' Hem')} is {var[0]}")

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/).

Cropland Data Layer (CDL) data from the US Department of Agriculture's National Agricultural Statistics Service (NASS), subset spatially to cover the Snake River Basin, USA for years 2010-2017, inclusive. This data is the raw data used to support initialization of the Janus agent based model of land use land cover change. It was developed by downloading CDL data from the USDA NASS site for an area of interest encompassing the Snake River Basin for individual years from 2010-2017. Data were converted to a georeferenced GeoTiff format using the Geospatial Data Abstraction Library (GDAL) command line interface. They were then concatenated into a single dataset using the rioxarray python library and saved as a CF-compliant NetCDF4 file using the xarray python library. Note that this file is saved with zlib compression level 1 and, therefore, users may experience a slowdown upon initial reading of the file.

High - resolution Multi - precipitation Concentration Indicators Dataset for Mainland ChinaDataset OverviewThis dataset provides high - resolution multi - precipitation concentration indicators for mainland China, covering both historical data from 1961 - 2022 and future projections from 2015 - 2100. The precipitation concentration indicators include Precipitation Concentration Degree (PCD), Precipitation Concentration Period (PCP), Daily Precipitation Concentration Index (DPCI), and Monthly Precipitation Concentration Index (MPCI).Historical DataThe historical data is sourced from two main components:Station Observations: Annual data for each of the four indicators from 1961-2020 at various stations across mainland China, stored in CSV format.Grid Observations: Gridded data with a resolution of 0.25° based on the CN05.1 dataset from 1961-2022, stored in NetCDF format.Future ProjectionsThe future projections are based on four Shared Socioeconomic Pathways (SSPs): SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5.Data FormatCSV files: The station - based annual data in CSV format can be easily opened and analyzed using spreadsheet software like Microsoft Excel or programming languages such as Python with libraries like Pandas.NetCDF files: The gridded data in NetCDF format can be processed using libraries like netCDF4 in Python or nco (NetCDF Operators) in the command - line environment.

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=-88.1067 featureType=Profile geospatial_bounds=POINT (-88.1067 26.04625) geospatial_bounds_crs=EPSG:4326 geospatial_lat_max=26.04625 geospatial_lat_min=26.04625 geospatial_lat_units=degrees_north geospatial_lon_max=-88.1067 geospatial_lon_min=-88.1067 geospatial_lon_units=degrees_east history=2019-12-02T08:01:04Z creation id=R4903254_013 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=26.04625 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=26.04625 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=2019-11-27T06:58:36Z time_coverage_resolution=P0000-00-00T00:00:00 time_coverage_start=2019-11-27T06:58:36Z user_manual_version=3.2 Westernmost_Easting=-88.1067

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.

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=-86.23443 featureType=Profile geospatial_bounds=POINT (-86.23443 24.44382) geospatial_bounds_crs=EPSG:4326 geospatial_lat_max=24.44382 geospatial_lat_min=24.44382 geospatial_lat_units=degrees_north geospatial_lon_max=-86.23443 geospatial_lon_min=-86.23443 geospatial_lon_units=degrees_east history=2019-12-27T05:00:22Z creation id=R4903250_019 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=24.44382 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=24.44382 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=2019-12-22T04:17:33Z time_coverage_resolution=P0000-00-00T00:00:00 time_coverage_start=2019-12-22T04:17:33Z user_manual_version=3.2 Westernmost_Easting=-86.23443

- Motivation

The potential of using radar remote sensing data for agricultural applications has been demonstrated in recent years, but this type of data is largely underused due to the complexity of its pre-processing and to the non-obvious physical interpretation of the derived features. To address these challenges, pre-processed datasets including synthetic aperture radar (SAR) images in analysis ready format are welcome.

- Dataset

The SAR and Optical Dataset for Agriculture in Seville (SODAS) integrates time series of radar images (Sentinel-1), optical images (Sentinel-2), precipitation records, and crop-type maps. The radar and optical time series consist of georeferenced Sentinel-1/-2 images over an agricultural area in Seville, Spain, spanning five years, from 2017 to 2021. Crop types include 18 different classes and fallow. The SAR images are provided in the form of 1) dual-polarimetric covariance matrices, which include the backscattering coefficient, and 2) repeat-pass interferometric coherence (amplitude and phase) at VV and VH polarimetric channels. The optical images correspond to partially or fully cloud-free Sentinel-2 reflectivity at red, green, blue, and near infra-red bands, as well as normalized difference vegetation index (NDVI) images. All images and crop-type maps are represented in the same cartographical grid in UTM coordinates, and the dataset is provided in NetCDF4 format.

- Application

This dataset has many potential uses, such as development of algorithms for crop-type mapping, retrieval of biophysical parameters, crop monitoring, and data fusion.

- Usage

A jupyter notebook for inspecting and illustrating the dataset features is provided, together with a python file which includes multiple functions to load the dataset, visualise images, derive additional features, and create and compare time series.

- Documentation

A journal paper for documenting the dataset (pre-processing, structure, and usage) is under preparation.

- Acknowledgments

All the data employed to prepare the annual crop-type reference maps were kindly provided by the Regional Government of Andalusia (Consejería de Agricultura, Pesca, Agua y Desarrollo Rural, Junta de Andalucía).

Daily rainfall data were obtained from the Agroclimatic Information System for Irrigation (SIAR) of the Government of Spain (Ministerio de Agricultura, Pesca y Alimentación).

All Sentinel-1 images were downloaded from the Alaska SAR Facility (https://search.asf.alaska.edu/).

All Sentinel-2 images were obtained through the French Theia Land Data Centre (https://www.theia-land.fr/en/homepage-en/).

All data and images included in this dataset are open access and publicly available.