We highly recommend to contact the GLORIA team at KIT or Jülich before using the data for scientific studies.

The H09-AHI-L2P-ACSPO-v2.90 dataset contains the Subskin Sea Surface Temperature (SST) produced by the NOAA ACSPO system from the Advanced Himawari Imager (AHI; largely identical to GOES-R/ABI) onboard the Himawari-9 (H09) satellite. The H09 is a Japanese weather satellite, the 9th of the Himawari geostationary weather satellite operated by the Japan Meteorological Agency. It was launched on November 2, 2016 into its nominal position at 140.7-deg E, and declared operational on December 13, 2022, replacing the Himawari-8. The AHI is the primary instrument on the Himawari Series for imaging Earth’s weather, oceans, and environment with high temporal and spatial resolutions. The H08/AHI maps SST in a Full Disk (FD) area from 80E-160W and 60S-60N, with spatial resolution 2km at nadir to 15km/VZA (view zenith angle) 67-deg, and 10-min temporal sampling. The 10-min FD data are subsequently collated in time, to produce the 1-hr product, with improved coverage and reduced cloud leakages and image noise. The L2P data is produced in GHRSST compliant netCDF4 GDS2 format, with 24 granules per day, and a total data volume 1.2 GB/day. The near-real time (NRT) data are updated hourly, with several hours latency. The NRT files are replaced with Delayed Mode (DM) files, with a latency of approximately 2-months. File names remain unchanged, and DM vs NRT can be identified by different time stamps and global attributes inside the files (MERRA instead of GFS for atmospheric profiles, and same day CMC L4 analyses in DM instead of one-day delayed in NRT processing). Pixel earth locations are not reported in the granules, as they remain unchanged from granule to granule. Pixel locations can be obtained using a flat lat/lon file or a Python script available via Documents tab from the dataset landing page. Climate and Forecast (CF) metadata aware software (e.g., Panoply, xarray) can detect and map the data as is via the granule CF projection attributes and variables. The ACSPO H09 HAI SSTs are validated against quality controlled in situ data from the NOAA iQuam system (Xu and Ignatov, 2014) and continuously monitored in the NOAA SQUAM system (Dash et al, 2010). A 0.02-deg equal-angle gridded L3C product 0.7GB/day) is available at https://podaac.jpl.nasa.gov/dataset/H09-AHI-L3C-ACSPO-v2.90

Abstract: We present high resolution measurements of trace species (e.g.: O3, H2O, HNO3, PAN, C2H6, HCOOH, NH3, solid ammonium nitrate) in the Upper Troposphere and Lowermost Stratosphere (UTLS) from the Gimballed Limb Observer for Radiance Imaging of the Atmosphere (GLORIA) during the StratoClim campaign with basis in Kathmandu, Nepal, on board the high altitude research aircraft Geophysica, 2017. TechnicalRemarks: netCDF data can be opened with a variety of software tools, including Matlab, Origin, or Python. For a simple GUI solution, Panoply is recommended: https://www.giss.nasa.gov/tools/panoply/download/ Other: We highly recommend to contact the GLORIA team at KIT or Jülich before using the data for scientific studies.

This Envisat SCIAMACHY Level 1b Geo-located atmospheric spectra V.10 dataset is generated from the full mission reprocessing campaign completed in 2023 under the _\(ESA FDR4ATMOS project\) https://atmos.eoc.dlr.de/FDR4ATMOS/ . This data product contains SCIAMACHY geo-located (ir)radiance spectra for Nadir, Limb, and Occultation measurements (Level 1), accompanied by supplementary monitoring and calibration measurements, along with instrumental parameters detailing the operational status and configuration throughout the Envisat satellite lifetime (2002-2012).

Additionally, calibrated lunar measurements, including individual readings and averaged disk measurements, have been integrated into the Level 1b product. The Level 1b product represents the lowest level of SCIAMACHY data made available to the users. The measurements undergo correction for instrument degradation applying a scan mirror model and m-factors. However, spectra are partially calibrated and require a further step to apply specific calibrations with the SCIAMACHY Calibration and Extraction Tool [_\(SciaL1c\) https://earth.esa.int/eogateway/tools/scial1c-command-line-tool ]. For many aspects, the SCIAMACHY Level 1b version 10 product marks a significant improvement with respect to previous mission datasets, supplanting the Level 1b dataset version 8.0X with product type SCI_NL_1P. Users are strongly encouraged to make use of the new datasets for optimal results.

The new products are conveniently formatted in NetCDF. Free standard tools, such as _\(Panoply\) https://www.giss.nasa.gov/tools/panoply/ , can be used to read NetCDF data. Panoply is sourced and updated by external entities. For further details, please consult our _\(Terms and Conditions page\) https://earth.esa.int/eogateway/terms-and-conditions .

Please refer to the _\(README\) https://earth.esa.int/documents/d/earth-online/rmf_0013_sci_1p_l1v10 file for essential guidance before using the data.

The files in this zip archive are data, scripts (javascript and MatLab) and associated output files in various formats that were used to generate the tables and plots in the paper: Way, M.J. (2025) ApJL "Trappist 1d: Exo-Venus, Exo-Earth or Exo-Dead?"

The DATA directory contains all of the ROCKE-3D NetCDF files that were parsed by the scripts in the top level directory.

The ACC, RSF, SOURCE and RUNDECK directories are explained in the top level README.txt file.

The files ending in pcl are javascript files that work with Panoply (https://www.giss.nasa.gov/tools/panoply/). You will first need to install Panoply, and a suitable java runtime library.

You will then need to unzip the file PanoplyCL-beta.zip and reference PanoplyCL.jar to run the pcl scripts as shown in the first few lines of each script. Of course the paths will have to change, e.g.
To run 01_swcrf_toa_in_01_ANN34000-34999.aijTrappist1d_1bar_N2_C400_Arid.pcl you would run it in this manner:
java -jar PanoplyCL.jar 01_swcrf_toa_in_01_ANN34000-34999.aijTrappist1d_1bar_N2_C400_Arid.pcl

While making sure that your path to the source NetCDF file in the pcl script is correct. In this case it is currently:
var ncfile1 = panoply.openDataset ( "file:/Users/mway/GoogleD/papers/2024-Trappist1d/FIGURES/DATA/01_ANN34000-34999.aijTrappist1d_1bar_N2_C400_Arid.nc" );

So one would need to change this path: /Users/mway/GoogleD/papers/2024-Trappist1d/

In two cases we used PowerPoint to combine multiple figures and to make legible axes, legends, etc.
That would be both Figures 2 and 3.

Fig2.pptx was built from the outputs from the pcl scripts discussed above along with the files ending in the name *colorbar.png

Fig3.pptx was built from two MatLab (Fig3A.m and Fig3B.m) generated files (Fig3A.jpg and Fig3B.jpg).

Table1.m is a MatLab script that roughly generates Table 1 from the paper. Some modest modifications
were made by hand in the LaTeX source when entered into the manuscript source LaTeX file.

This Envisat SCIAMACHY Ozone stratospheric profiles dataset has been extracted from the previous baseline (v6.01) of the SCIAMACHY Level 2 data. The dataset is generated in the framework of the full mission reprocessing campaign completed in 2023 under the _\(ESA FDR4ATMOS project\) https://atmos.eoc.dlr.de/FDR4ATMOS/ . For optimal results, users are strongly encouraged to make use of these specific ozone limb profiles rather than the ones contained in the _\(SCIAMACHY Level 2 dataset version 7.1\) https://earth.esa.int/eogateway/catalog/envisat-sciamachy-total-column-densities-and-stratospheric-profiles-sci_ol_2p- .

The new products are conveniently formatted in NetCDF. Free standard tools, such as _\(Panoply\) https://www.giss.nasa.gov/tools/panoply/ , can be used to read NetCDF data. Panoply is sourced and updated by external entities. For further details, please consult our _\(Terms and Conditions page\) https://earth.esa.int/eogateway/terms-and-conditions .

Please refer to the _\(README\) https://earth.esa.int/eogateway/documents/20142/37627/ENVI-GSOP-EOGD-QD-16-0132.pdf file (L2 v6.01) for essential guidance before using the data.

netCDF data can be opened with a variety of software tools, including Matlab, Origin, or Python. For a simple GUI solution, Panoply is recommended: https://www.giss.nasa.gov/tools/panoply/download/

This Envisat SCIAMACHY Level 2 Total column densities and stratospheric profiles v7.1 dataset is generated from the full mission reprocessing campaign completed in 2023 under the _\(ESA FDR4ATMOS project\) https://atmos.eoc.dlr.de/FDR4ATMOS/ . It provides atmospheric columnar distributions and stratospheric profiles for various trace gases based on the Level 1b version 10 products.

This SCIAMACHY Level 2 dataset contains total column densities of O3, NO2, OClO, H2O, SO2, BrO, CO, HCHO, CHOCHO and CH4 retrieved from Nadir measurements. Additionally, cloud parameters (fractional coverage, top height, optical thickness) and an aerosol absorption indicator are enclosed. Stratospheric profiles of O3, NO2, and BrO are derived from limb measurements, along with flagging information for different cloud-types. Tropospheric NO2 and BrO columns are retrieved combining limb and nadir measurements.

This SCIAMACHY Level 2 dataset version 7.1 replaces the previous version 6.01. Users are strongly encouraged to make use of the new datasets for optimal results.

For limb O3 profiles, a separate product derived from the previous Version 6 processor is provided distinctly -> _\(SCIAMACHY Level 2 - Limb Ozone [SCI_LIMBO3]\) https://earth.esa.int/eogateway/catalog/envisat-sciamachy-ozone-stratospheric-profiles-sci_limbo3 . This is because the V7.1 limb ozone data is unsuitable for long-term change studies due to its divergent behavior from earlier processor versions, particularly from 2009 onwards. This divergence stems from residual deficiencies in the Level 1, resulting in a vertical oscillating pattern in the drift and bias profiles. In contrast, Version 6 limb ozone data does not exhibit these oscillations in bias and drift. Further details on this issue can be found in the _\(latest README\) https://earth.esa.int/documents/d/earth-online/rmf_0014_sci_2p_l2v7-1 file. The new products are conveniently formatted in NetCDF. Free standard tools, such as _\(Panoply\) https://www.giss.nasa.gov/tools/panoply/ , can be used to read NetCDF data. Panoply is sourced and updated by external entities. For further details, please consult our _\(Terms and Conditions page\) https://earth.esa.int/eogateway/terms-and-conditions .

Please refer to the _\(README\) https://earth.esa.int/documents/d/earth-online/rmf_0014_sci_2p_l2v7-1 file for essential guidance before using the data.

Code and data for Section 2 of the Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines

This repository contains the code and data needed to produce the trajectories, projections, and observations for the Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines.

The report can be found on https://oceanservice.noaa.gov/hazards/sealevelrise/sealevelrise-tech-report-sections.html

An interactive tool to study the observations, trajectories, and scenarios can be accessed from https://sealevel.nasa.gov/task-force-scenario-tool

Frequently-asked questions: https://sealevel.nasa.gov/faq/16/

Authors

• William V. Sweet, NOAA National Ocean Service
• Benjamin D. Hamlington, NASA Jet Propulsion Laboratory
• Robert E. Kopp, Rutgers University
• Christopher P. Weaver, U.S. Environmental Protection Agency
• Patrick L. Barnard, U.S. Geological Survey
• Michael Craghan, U.S. Environmental Protection Agency
• Gregory Dusek, NOAA National Ocean Service
• Thomas Frederikse, NASA Jet Propulsion Laboratory
• Gregory Garner, Rutgers University
• Ayesha S. Genz, University of Hawai‘i at Mānoa, Cooperative Institute for Marine and Atmospheric Research
• John P. Krasting, NOAA Geophysical Fluid Dynamics Laboratory
• Eric Larour, NASA Jet Propulsion Laboratory
• Doug Marcy, NOAA National Ocean Service
• John J. Marra, NOAA National Centers for Environmental Information
• Jayantha Obeysekera, Florida International University
• Mark Osler, NOAA National Ocean Service
• Matthew Pendleton, Lynker
• Daniel Roman, NOAA National Ocean Service
• Lauren Schmied, FEMA Risk Management Directorate
• William C. Veatch, U.S. Army Corps of Engineers
• Kathleen D. White, U.S. Department of Defense
• Casey Zuzak, FEMA Risk Management Directorate

Contents

This data and code set contains the following directories:

Results

The Results folder contains the resulting projections, trajectories and observations from the report.

• TR_global_projections.nc: GMSL projections, trajectory, and observations
• TR_regional_projections.nc: Regional observations, projections and trajectories
• TR_local_projections.nc: Local observations, projections and trajectories
• TR_gridded_projections.nc: Gridded projections

These files are in the NetCDF forrmat. To read the NetCDF files, many free software packages are available, including ncview and Panoply. Free NetCDF packages are available to directly import the data into Julia and Python code.

Code

The Code folder contains all the computer code used to read and analyze the observations and the projections, and to generate the trajectories.

To run this code, you need Julia. The code requires the Julia packages CSV, Interpolations, JSON, LoopVectorization, MAT, NCDatasets, NetCDF, Plots, XLSX, LinearAlgebra, and Statistics. They can be installed by pressing ] at the Julia REPL and typing:

add CSV Interpolations JSON LoopVectorization MAT NCDatasets NetCDF Plots XLSX LinearAlgebra Statistics

This program also requires Hector. Hector needs to be installed or compiled. In the file Hector.jl update the path to the Hector executable on lines 30 and 104.

Run Run_TR.jl in the REPL or run julia Run_TR.jl from the command line to run the projections. The projections are then written to the .\Data directory.

The folder contains the following files:

• Run_TR.jl: This is the main routine that (eventually) calls all the functions to compute the projections.
• ConvertNCA5ToGrid.jl: Converts the original NCA5 projections to a set of netCDF files that's used throughout this code
• ProcessObservations.jl: Reads and processes the tide-gauge and altimetry observations
• GlobalProjections.jl: Reads and processes the GMSL observations and projections, and computes the trajectory
• RegionalProjections.jl: Reads and processes the regional projections and computes the trajectories
• LocalProjections.jl: Reads and processes the local projections at the tide-gauge locations and computes the trajectories
• GriddedProjections.jl: Reads the gridded NCA5 projections and add a GMSL baseline correction for the 2005 vs 2000 baseline
• SaveFigureData.jl: Reads the results and writes text files for GMT
• Hector.jl: Wrapper for Hector, used to compute trends and uncertainties.
• Masks.jl: Defines the region masks for each region.

Data

The Data directory contains the input data sets used during the computations. Please appropriately cite the input data if you use it. It contains the following:

Directories:

• ClimIdx: Map with climate indices (NAO, PDO, MEI) used to remove internal variability. All the indices come from NOAA Physical Sciences Laboratory (PSL) and NOAA Climate Prediction Centre (CPC)
• NCA5_projections Contains the NCA5 projections for each scenario (Low, IntLow, Int, IntHigh, and High). For each scenario, the GMSL projections, projections at tide-gauge locations and on a 1-degree grid are provided.

Files:

• basin_codes.nc: Map with basin codes. from Eric Leuliette/NOAA. Data provided by the NOAA Laboratory for Satellite Altimetry.
• CDS_monthly_1993_2020.nc: Monthly-mean sea level (1993-2020) from gridded altimetry. Obtained from Copernicus Climate Data Store. This dataset contains modified Copernicus Climate Change Service information [2020]
• enso_correction.mat: GMSL correction for ENSO/PDO from Hamlington, B. D., Frederikse, T., Nerem, R. S., Fasullo, J. T., & Adhikari, S. (2020). Investigating the Acceleration of Regional Sea‐level Rise During the Satellite Altimeter Era. Geophysical Research Letters. https://doi.org/10.1029/2019GL086528
• filelist_psmsl.txt: List with PSMSL file names and PSMSL IDs. Obtained from the Permanent Service for Mean Sea Level (PSMSL), 2021, Retrieved 29 Nov 2021. Simon J. Holgate, Andrew Matthews, Philip L. Woodworth, Lesley J. Rickards, Mark E. Tamisiea, Elizabeth Bradshaw, Peter R. Foden, Kathleen M. Gordon, Svetlana Jevrejeva, and Jeff Pugh (2013) New Data Systems and Products at the Permanent Service for Mean Sea Level. Journal of Coastal Research: Volume 29, Issue 3: pp. 493 – 504. https://doi.org/:10.2112/JCOASTRES-D-12-00175.1.
• GEBCO_bathymetry_05.nc: Bathymetry map of the global oceans from the General Bathymetric Chart of the Oceans (GEBCO). Source: GEBCO Compilation Group (2021) GEBCO 2021 Grid (doi:10.5285/c6612cbe-50b3-0cff-e053-6c86abc09f8f) The source data have been re-gridded onto a 0.5 degree grid.
• GIA_Caron_stats_05.nc: Glacial Isostatic Adjustment estimates from Caron, L., Ivins, E. R., Larour, E., Adhikari, S., Nilsson, J., & Blewitt, G. (2018). GIA Model Statistics for GRACE Hydrology, Cryosphere, and Ocean Science. Geophysical Research Letters, 45(5), 2203–2212. https://doi.org/10.1002/2017GL076644. The source data have been re-gridded onto a 0.5 degree grid.
• global_timeseries_measures.nc: Time series of estimated 20th-century GMSL and its components, based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. https://doi.org/10.1038/s41586-020-2591-3
• GMSL_ensembles.nc: Ensemble GMSL reconstruction from tide-gauges based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a

From the project summary:

Both satellite products and climate models have large biases in the energy and water budgets over the Southern Ocean (SO), which is not surprising given this environment's unique nature. The air is free of dust and pollution, and the surface is governed by strong winds, large waves and heavy sea spray. These conditions lead to the greatest fractional cloud cover over any place on the globe. Much of these biases are a direct consequence of a poor understanding of the structure and dynamics of the SO atmospheric boundary layer, which in turn is a consequence of the sparse observations being available due to the harsh conditions. This proposals call for employing unmanned aerial vehicles/systems from Macquarie Island to make unprecedented observations of the boundary layer processes over the SO. These observations will be used to both model the boundary layer dynamics and clouds and evaluate satellite products and numerical simulations of surface fluxes, cloud properties and sea spray.

The data was recorded at lat: -54.5, lon:158.935. The observations include fluxes for Absolute Humidity, Heat, and Carbon. The data is in netcdf4 format with medium compression, and have all available information in the attributes of each variable. The data can be easily previewed with an application like Panoply (https://www.giss.nasa.gov/tools/panoply/). The variable names are: 7500_Warn
AGC_7500_Avg
Amph_CSAT_Tot
Ampl_CSAT_Tot
CSAT_Warn
Chopper_7500_Tot
DelT_CSAT_Tot
Detector_7500_Tot
Fc_Avg
Fc_raw_Avg
Fe_Avg
Fe_raw_Avg
Fh_Avg
Fm_Avg
Pll_7500_Tot
Sync_7500_Tot
Track_CSAT_Tot
covAhAh
covAhTv
covCcAh
covCcCc
covCcTv
covTvTv
covUxAh
covUxCc
covUxTv
covUxUx
covUxUy
covUyAh
covUyCc
covUyTv
covUyUy
covUzAh
covUzCc
covUzTv
covUzUx
covUzUy
covUzUz
n_Tot
time
time_YYYYmmDDHHMMSS

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

Abstract: We present the first high resolution measurements of pollutant trace gases in the Asian Summer Monsoon Upper Troposphere and Lowermost Stratosphere (UTLS) from the Gimballed Limb Observer for Radiance Imaging of the Atmosphere (GLORIA) during the StratoClim (Stratospheric and upper tropospheric processes for better climate predictions) campaign based in Kathamandu, Nepal, 2017. Measurements of peroxyacetyl nitrate (PAN), acetylene (C$_2$H$_2$), and formic acid (HCOOH) show strong local enhancements up to altitudes of 16 km. More than 500 pptv of PAN, more than 200 pptv of C$_2$H$_2$, and more than 200 pptv of HCOOH are observed. TechnicalRemarks: netCDF data can be opened with a variety of software tools, including Matlab, Origin, or Python. For a simple GUI solution, Panoply is recommended: https://www.giss.nasa.gov/tools/panoply/download/

We present the first high resolution measurements of pollutant trace gases in the Asian Summer Monsoon Upper Troposphere and Lowermost Stratosphere (UTLS) from the Gimballed Limb Observer for Radiance Imaging of the Atmosphere (GLORIA) during the StratoClim (Stratospheric and upper tropospheric processes for better climate predictions) campaign based in Kathamandu, Nepal, 2017. Measurements of peroxyacetyl nitrate (PAN), acetylene (C$_2$H$_2$), and formic acid (HCOOH) show strong local enhancements up to altitudes of 16 km. More than 500 pptv of PAN, more than 200 pptv of C$_2$H$_2$, and more than 200 pptv of HCOOH are observed.

The Datasets are the result of computations from a source gridded data with a resolution of 0.25° x 0.25° (Ampofo et al., 2023; Domínguez-Castro et al., 2020). The datasets are the results of the analysis of the gridded netCDF precipitation dataset using Climate Data Tools (CDT) ® version 5, a component of the statistical package R 3.5.1 and developed at the International Research Institute for Climate and Society of the Columbia University. It has a Graphical User Interface (GUI) mode and has utility functions which were used for quality control, homogenization and annual computations for the above stated indices over the 56-year study period (Ampofo et al., 2023b). Other tools used were; Panoply® (https://www.giss.nasa.gov/tools/panoply/) for making customized plots from the output netCDF datasets

TechnicalRemarks: The data are described in the paper by M. Höpfner et al.: 'Ammonium nitrate particles formed in upper troposphere from ground ammonia sources during Asian monsoons', Nature Geoscience, 2019, https://doi.org/10.1038/s41561-019-0385-8.' The datasets are provided in netCDF format and can e.g. be visualized with the software Panoply which is available at 'https://www.giss.nasa.gov/tools/panoply/'.

This dataset contains alternative products to the official Level 3 (L3) product from Measurements of Pollution in the Troposphere (MOPITT) joint thermal infrared (TIR) – near infrared (NIR) Version 8 Carbon Monoxide (CO) retrievals (available here: https://doi.org/10.5067/TERRA/MOPITT/MOP03J_L3.008). The products are described and analysed in a paper in the journal Atmospheric Measurement Techniques by Ian Ashpole and Aldona Wiacek (2022, https://doi.org/10.5194/amt-2022-90). In short, whereas the official MOPITT L3 product is based on retrievals performed over both land AND water surface types, the products here are created separately from retrievals performed ONLY over land (“L3L”) OR water (“L3W”). The code for creating L3L and L3W is available here: https://github.com/ianashpole/MOPITT_L3L_L3W The version naming is consistent with the official MOPITT product version, although note that version 8 is the first version that these alternatives are produced for (i.e. although MOPITT product versions 1-7 exist, L3L and L3W do not). However, it is intended that L3L and L3W are created for MOPITT product versions after version 8. The dataset stored here consists of two main .zip archives: “MOPITT_v8.L3L.20010901_20190228.zip” “MOPITT_v8.L3W.20010901_20190228.zip” When unzipped, each archive contains 6057 individual NetCDF (".nc") files that correspond to the daily L3L and L3W data products for the period 2001-09-01 to 2019-02-28, inclusive. Daily files represent the satellite instrument measurements for a single day. Users are referred to the "README.txt" file for a full description of the individual file contents. Note that when unzipped, the products require ~22.5 GB of data storage each (45 GB total for both L3L and L3W). Because of this, a single file from each product has been uploaded separately (file date = “20020801”; see below for naming convention) to facilitate user experimentation before unpacking the full L3L/L3W products. Individual L3L/L3W NetCDF files are ~3.4 MB in size. The individual NetCDF files are named as follows: MOPITT_v8.L3L.from_MOPO2J.selected_variables.YYYYMMDD.nc (replace “L3L” with “L3W” in the filename for the corresponding L3W product.) The date corresponds to the YYYYMMDD that the retrievals were made. E.g. the file “MOPITT_v8.L3L.from_MOPO2J.selected_variables.20020801.nc” corresponds to the L3L product for MOPITT retrievals made on August 1st 2002. Variables contained within the file are described in detail in the "README.txt" file. NetCDF is a common format for gridded geoscientific data, easily readable by all widely used scientific programming languages (e.g. Python, R, Matlab, IDL…), as well as dedicated command line tools (e.g. cdo, gdal). Panoply (https://www.giss.nasa.gov/tools/panoply/) is an alternative application for quickly plotting these data without the requirement of coding experience. Most GIS packages can also handle NetCDF data. An example python code for reading and plotting data from a single L3L file is available here: https://github.com/ianashpole/MOPITT_L3L_L3W/blob/main/example_read_and_plot_MOPITT_L3L.ipynb

Abstract: We present high resolution measurements of ammonium (and other pollution trace gases) in the South Atlantic Upper Troposphere and Lowermost Stratosphere (UTLS) from the Gimballed Limb Observer for Radiance Imaging of the Atmosphere (GLORIA) during the StratoClim campaign with basis in Kathmandu, Nepal, on board the high altitude research aircraft Geophysica, 2017, and during the SouthTRAC (Transport and Composition in the Southern Hemisphere Upper Troposphere/Lower Stratosphere) campaign with bases in Oberpfaffenhofen, Germany, and Rio Grande, Argentina, on board the German High Altitude and Long range research Aircraft (HALO), 2019. TechnicalRemarks: netCDF data can be opened with a variety of software tools, including Matlab, Origin, or Python. For a simple GUI solution, Panoply is recommended: https://www.giss.nasa.gov/tools/panoply/download/