Find research datasets worth reusing

opencc-by-4.0Jun 2023View details →

Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the Gobabeb Site in Namibia

The HYPERNETS project (www.hypernets.eu; Ruddick et al. 2024) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical satellite products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu; Kuusk et al. 2024) dedicated to land and water surface reflectance validation with instrument pointing capabilities. This instrument has been deployed over various sites covering a range of water and land types and a range of climatic and logistic conditions. Here, we provide the first fully quality-checked data for the Gobabeb HYPERNETS site in Namibia (GHNA). The HYPERNETS data products were processed using the HYPERNETS_processor (De Vis et al. 2024b). The provided NetCDF files are the L2B hypernets products with surface reflectances, their associated uncertainties and error-correlation information. The reflectance in these products is the Hemispherical-conical Reflectance Factor (HCRF) defined as: HCRF = π L / E where L is the conical upwelling radiance (with field of view of 5 degrees) and E is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). These reflectances have dimensions of wavelength and series, where each series is a set of measurements for a given geometry (combination of viewing zenith and azimuth angle). In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, number of valid scans used, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info. The GHNA site has minimal daily variation in surface cover and weather conditions and is an ideal location for sustained, homogeneous measurements. The site is well characterised as it is very close to an instrument already recognised as a radiometric calibration site (GONA) as part of the RadCalNet network (Bialek et al. 2016). The HYPERNETS site itself (23.60153 degrees S, 15.12589 degrees E) is 650 m from the RadCalNet site, and is located on a gravel plain near a dry riverbed which separates it from the neighbouring dune sea. The HYPSTAR®-XR sensor was installed May 2022 at the top of a 9m mast on an extended 1 m horizontal boom to minimise interruption of the field of view. Data are collected every 30 minutes between 9am and 6pm local time (UTC+02) between viewing zenith angles of 0 and 60 degrees. No measurements are taken at 2pm and 2:30pm local time to avoid the hottest part of the day. The HYPSTAR®-XR (eXtended Range) instruments deployed at each land HYPERNETS site consist of a VNIR and a SWIR sensor and autonomously collect data between 380-1700 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm and the SWIR sensor has 220 channels between 1000 and 1700 nm with a FWHM of 10 nm. The hypernets_processor (De Vis et al. 2024b) automatically processes all this data into various products, including the L2A surface reflectance product provided here. All of the products have associated uncertainties (divided into random and systematic uncertainties, including error-correlation information) which were propagated using the CoMet toolkit (www.comet-toolkit.org). For an example using these data for satellite vicarious calibration, see De Vis et al. 2024a). To obtain this dataset, we start from the full GHNA data record and omit data that does not pass the relevant quality checks (QC). Some QC are performed during the near-real time processing done by the hypernets_processor (see https://hypernets-processor.readthedocs.io/en/latest/content/atbd/processing/quality_checks.html) to produce the L2A files. Then, a number of site-specific QC are performed as post-processing to produce the L2B files. These site-specific QC cover things such as removing flags in the L2A data, avoiding periods with bad deployment conditions, removing unsuitable viewing and solar angles, as well as poorly performing wavelength ranges and individual sequences. Any potential misalignment of the sensor is also corrected, affecting L1D irradiances, and L2B reflectances. These corrected data are then used in a more stringent clear sky check, and in a check that verifies the reflectances are within realistic ranges for a given angle and time of year for the given site.  There was a rain event in Gobabeb in March 2025, resulting in the growth of grass at the site. We expect the site will be back to its normal surface cover in the near future. Since the rain event, less data passed the site-specific QC. A dedicated QC will be developed for this period, as the data with grass surface cover will still be useful for satellite validation. These updated data will be made available in the future.

opencc-by-sa-4.0Oct 2019View details →

SM2RAIN-ASCAT (2007-2021) global daily satellite rainfall including aggregated values and trend parameters as 10km resolution GeoTIFFs

This is a GeoTIFF version of the <a href="http://hydrology.irpi.cnr.it/download-area/sm2rain-data-sets/">SM2RAIN-ASCAT (2007-2021): global daily satellite rainfall from ASCAT soil moisture</a> data set v1.1 (Brocca et al. 2019). Conversion steps are available <a href="https://github.com/Envirometrix/LandGISmaps/tree/master/input_layers/SM2RAIN">here</a>. Few important notes: <ul> <li>Daily values are stored as integers, whereas in the NetCDF the dataset is rounded to one decimal place.</li> <li>The NetCDF has also a Quality Flag for a better and more informed use of the data (here omitted).</li> <li>P05, P50 and P95 indicate quantiles derived per pixel.</li> </ul> Includes also long-term trends (trend.logit.ols) which was produced by fitting regression models to de-seasonalized time-series as explained in this <a href="https://gitlab.com/openlandmap/global-layers/-/blob/master/input_layers/MOD13Q1/03-data-access.ipynb">python tutorial</a>. Basically models are fitted for each pixel and the model parameters are saved as images. Monthly averages and s.d. of precipitation are available in the files: <ul> <li>clm_precipitation_sm2rain.*_m_10km_s0..0cm_2007..2021_v1.5.tif = monthly precipitation in mm,</li> <li>clm_precipitation_sm2rain.*_sd.10_10km_s0..0cm_2007..2021_v1.5.tif = standard deviation of precipitation in mm * 10 per month (multiplied by 10 so Integers can be used),</li> </ul> Downscaled monthly averages (1 km) are also available (<a href="https://doi.org/10.5281/zenodo.1435912">https://doi.org/10.5281/zenodo.1435912</a>). To cite this data set please refer to the <a href="https://doi.org/10.5281/zenodo.2591214">original copy</a> of the data set. <ul> <li>Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., Wagner, W. (2019). <a href="https://doi.org/10.5194/essd-11-1583-2019">SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations</a>. Earth Syst. Sci. Data, 11, 1583–1601. <a href="https://doi.org/10.5194/essd-11-1583-2019">https://doi.org/10.5194/essd-11-1583-2019</a></li> </ul>

opencc-by-4.0Jul 2024View details →

The extrAIM dataset: A merged satellite-based daily precipitation dataset for the Mediterranean region (including an ensemble of 20 synthetic realisations)

extrAIM dataset is a new merged daily precipitation product (extraim_merged_data.nc) for the Mediterranean region with the following characteristics: <ul> <li>Dataset format: NetCDF</li> <li>Spatial resolution: 25 x 25 km</li> <li>Temporal resolution: 1 day</li> <li>Spatial coverage: Longitude: from -6.25 to 38.25, Latitude: 27.75 to 49</li> <li>Temporal coverage: 01-01-2007 to 30-09-2021</li> <li>Merging approach: Two-step merging (classification and regression) <ul> <li>Algorithm: Random Forest for both classification and regression</li> <li>Training strategy: Full training strategy</li> </ul> </li> <li>Merged precipitation products: SM2Rain-ASCAT and GPM Late Run</li> <li>Reference precipitation product: EMO5</li> <li>Static covariates: Longitude, Latitude and Elevation, in both classification and regression step <ul> <li>Classification step: probability dry and probability dry of the 5 neighboring points around the target locations</li> <li>Regression step: mean, standard deviation and skewness of daily precipitation, of the entire series and non-zero amounts, as well as mean precipitation of the 5 neighboring points around the target locations</li> </ul> </li> </ul> In addition, an ensemble of 20 synthetic realizations (equiprobable and bias-adjusted) of the merged dataset is provided (files named: “extraim_realisation_XX.nc”). The synthetic realisations were produced using the extrAIM’s uncertainty-quantification approach and the associated conditional sampling method.

opencc-by-4.0Nov 2024View details →

Satellite-to-Ground QKD SKR dataset for P&M and Entanglement-based Protocols

This dataset provides calculated Key Performance Indicators (KPIs) for satellite-to-ground quantum key distribution (QKD) links, modeled across Low Earth Orbit (LEO), Medium Earth Orbit (MEO), and Geostationary Orbit (GEO). The LEO orbit is modeled over a two-week period, while MEO and GEO orbits are modeled over a single day, whereas samples are provided with a sampling rate of 10 seconds. The satellite downlink channel is simulated using two QKD protocols: the Prepare-and-Measure protocol (Decoy-BB84) and the Entanglement-based protocol (BBM92). System specifications align with the LaiQa project source prototype, incorporating SNSPDs as the detection technology and assuming telescope-to-fiber coupling for ground reception. This dataset includes essential input metrics such as elevation angles for different orbits and various Optical Ground Stations (OGS) over time, along with key output metrics, including Secure Key Rates (SKR), Quantum Bit Error Rate (QBER), and Link Loss. Additionally, a comprehensive PDF guide is provided to assist with data handling and interpretation. Note: The results that are presented in the READ_ME file provide the volume of distilled keys and the number of distilled AES 256 keys over a time period of two weeks for different satellite orbits.

opencc-by-4.0Sep 2021View details →

Satellite-observed surface flow speed within Russell sector, West Greenland, bi-weekly average of 2015-2019

An average horizontal surface ice velocity of Russell sector (Greenland) with 2-week temporal and 150m spatial resolution. Derived from satellite images collected between 2015 and 2019 by Landsat-8, Sentinel-1, and Sentinel-2. The details on the data processing can be found in https://doi.org/10.5194/tc-2021-170. Dataset contains 24 independent NetCDF files (one per 2-weeks time step) with maps of vx and vy velocity components, maps of associated uncertainties per velocity component (STD of the 2-weeks averaged raw satellite measurements), and map of number of averaged measurements.

opencc-by-4.0Sep 2021View details →

Seasonal evolution of basal conditions within Russell sector, West Greenland, inverted from satellite observations of surface flow

An annual set of model-inferred basal and surface properties of ice flow at Russell Gletcher sector in Western Greenland with half-month temporal resolution. Derived using the Elmer/Ice ice-flow model by inversion of satellite-observed ice surface velocity (10.5281/zenodo.5535532). The details on the data creatoin can be found in 10.5194/tc-15-5675-2021 . Dataset contains 24 independent NetCDF files (one per 2-weeks time step) with: * alpha - inverted be model basal friction coefficient in log10 (log10(MPa m-1 a) * base - basal topography altitude (m) * lithk - ice thickness (m) * orog - surface altitude (m) * strbasemag - magnitude of basal friction tb (MPa) * xvelbase, yvelbase, zvelbase - 3D basal velocity  (m/yr) * xvelmean, yvelmean - vertically average mean horizontal velocity (m/yr) * xvelsurf, yvelsurf, zvelsurf - 3D surface velocity (m/yr) * n - effective pressure (MPa) The additional WinterMeanState NetCDF file (inversion from the mean velocity of january, Febriary, Mars) contains the same set of variables (except the effective pressure), and in addition contains the As Weertman sliding coeffitient. The results have been interpolated from the native unstructured model grid to the regular grid used for the observed velocity (10.5281/zenodo.5535624).

opencc-by-4.0Jun 2023View details →

Initial Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the Bare soil at Marquardt, Germany

The HYPERNETS project (www.hypernets.eu) aims to ensure that high-quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument-pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the ATB HYPERNETS site in Marquardt, Germany [52°27'59.40"N, 12°57'35.16"E] (ATGE). It is a subset of the complete data record, consisting of the measurements which could be used for satellite validation.  The provided NetCDF files are the L2A hypernets products with surface reflectances, their associated uncertainties and error-correlation information. The reflectance in the L2A products is the Hemispherical-directional Reflectance Factor (HDRF) defined as HDRF = π L / E where L is the directional upwelling radiance (with the field o, view of 5 dgrees), and E is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). These reflectances have dimensions of wavelength and series, where each series is a set of measurements for a given geometry (combination of viewing zenith and azimuth angle). In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, number of valid scans used, and quality flags (typically, no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info. The HYPSTAR®-XR sensor was installed on 11 Oct 2022 at the top of a 5m mast on an extended 5 m horizontal boom to minimise interruption of the field of view. The boom faces South at the right angle towards bare soil. The mast is located at 52.466778°N, 12.959778°E. Data are collected every 30 minutes between 9:00 and 17:00 (UTC) from different zenith and azimuth angle. The HYPSTAR®-XR (eXtended Range) instruments deployed at each land HYPERNETS site consist of a VNIR and a SWIR sensor and autonomously collect data between 380-1700 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm, and the SWIR sensor has 220 channels between 1000 and 1700 nm with a FWHM of 10 nm. The hypernets_processor (Goyens et al. 2021; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. All products have associated uncertainties (divided into random and systematic uncertainties, including error-correlation information) which were propagated using the CoMet toolkit (www.comet-toolkit.org).  To obtain this dataset, we start from the full ATGE data record and omit all the data that do not pass all quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was also developed to remove outliers and supply the best quality data suitable for satellite validation. To remove the outliers, a sigma-clipping method is used. First, reflectances are extracted in separate 2-hour windows throughout the day (to account for BRDF differences due to different solar positions) for four different wavelengths (500, 900, 1100 and 1600 nm). Outliers in these reflectances are then identified by iteratively calculating the mean reflectance trend with time (by binning the data per maximum of 30 data points), calculating the standard deviation from this trend, and masking any data that is more than three standard deviations away from the trend. This process is repeated on the unmasked data until the standard deviation does not vary by more than 5% between two iterations. The masks for the four different wavelengths are then combined (keeping only measurements for which none of the four wavelengths is an outlier). The reflectances and associated uncertainties for any masked series (i.e. a geometry that is masked either by the sigma-clipping procedure or from the masks of the hypernets_processor) are replaced by NaNs. Any sequence that has more than half of its series masked is removed entirely.

openCC0Aug 2020View details →

Satellite-derived estimate of artificial lakes in the Phoenix, Arizona, USA Metropolitan area (2000)

Artificial lakes are a prominent example of a local hydrogeomorphic modification in the Phoenix, Arizona metropolitan area. Although lakes are not a natural feature of Sonoran desert ecosystems, numerous artificial lakes are evident in the region. Here we attempt to quantify the heretofore unknown number and extent of artificial lakes. As official GIS layers from local and state agencies had significant misclassifications and omissions, we used two published land cover datasets to estimate the number and areal extent of artificial lakes. We discovered that there are 908-1,390 lakes in the Phoenix area, with the number varying according to level of aggregation. Accurate data on the extent and distribution of these designed ecosystems is vital for water-resources planning and stormwater management. Results, discussion, and additional details are available in document DOI 10.1007/s11252-011-0208-1.

openCC (other)Nov 2022View details →

Globally distributed lake surface water temperatures collected in situ and by satellites; 1985-2009

Global environmental change has influenced lake surface temperatures, a key driver of ecosystem structure and function. Recent studies have suggested significant warming of water temperatures in individual lakes across many different regions around the world. However, the spatial and temporal coherence associated with the magnitude of these trends remains unclear. Thus, a global dataset of water temperature is required to understand and synthesize global, long-term trends in surface water temperatures of inland bodies of water. We assembled a database of summer lake surface temperatures for 291 lakes collected in situ and/or by satellites for the period 1985-2009. In addition, corresponding climatic drivers (air temperatures, solar radiation, and cloud cover) and geomorphometric characteristics (latitude, longitude, elevation, lake surface area, maximum depth, mean depth, and volume) that influence lake surface temperatures were compiled for each lake. This unique dataset offers an invaluable baseline perspective on global-scale lake thermal conditions as environmental change continues. This dataset accompanies a data publication in the journal Scientific Data

openCC (other)Aug 2024View details →

Seasonal sea ice indices including the timing of ice-edge advance and ice-edge retreat (in year day), the ice season duration (in days) and number of actual ice days (versus open water days) within the ice season, extracted for various PAL LTER sub-regions West of the Antarctic Peninsula and derived from passive microwave satellite data for 1979/80 to 2023/24 ice seasons.

Seasonal sea ice indices including the timing of ice-edge advance and ice-edge retreat (in year day), the ice season duration (in days) and number of actual ice days (versus open water days) within the ice season, extracted for various PAL LTER sub-regions West of the Antarctic Peninsula and derived from passive microwave satellite data for 1979/80 to 2023/24 ice seasons. The ice season duration is defined as the time elapsed between day of ice-edge advance and day of ice-edge retreat within a given sea ice year, which begins mid-February (mean minimum of summer sea ice extent for the Southern Ocean) and ends the following mid-February. See Stammerjohn et al (2008, JGR) for further details.

openCC (other)Aug 2024View details →

Average monthly sea ice coverage for various PAL LTER sub-regions West of the Antarctic Peninsula derived from passive microwave satellite data, 1978 - June 2024.

Monthly sea ice coverage derived from passive microwave satellite measurements and extracted for various PAL LTER subregions. Several different sea-ice metrics are provided including (1) monthly sea-ice extent, sea-ice area, and open-water area (km^2) extracted for the greater WAP region (from the AP to 80W) and for 3 PAL LTER grid regions: the original PAL grid (000-900 lines), the PAL 'DSR' grid (200-600 lines), and the PAL 'new' grid (-200 to 600 lines); and (2) monthly sea-ice concentration (%) extracted for small PAL subregions, including the nominal penguin foraging areas (~200km by ~200km) southwest of King George Island (KGI), Anvers, Avian and Charcot islands, as well as for Marguerite Bay (~140km by ~140km) (inland of the area defined for Avian).

openCC (other)Aug 2024View details →

Average yearly sea ice coverage for various PAL LTER sub-regions West of the Antarctic Peninsula derived from passive microwave satellite data, 1979 - 2023.

Annual sea ice coverage derived from passive microwave satellite measurements and extracted for various PAL LTER subregions. Several different sea-ice metrics are provided including: monthly sea-ice extent, sea-ice area, and open-water area (km^2) extracted for the greater WAP region (from the AP to 80W) and for 3 PAL LTER grid regions: the original PAL grid (000-900 lines), the PAL 'DSR' grid (200-600 lines), and the PAL 'new' grid (-200 to 600 lines).

opencc-by-4.0Feb 2019View details →

SM2RAIN test dataset with ASCAT and SMAP satellite soil moisture (plus ERA5 evapotranspiration)

Are you looking for a research contest? Here [SM_RAIN_EVAP_1009points.nc] you can find a 5-year dataset at 1009 points in Italy, the United States, India and Australia of co-located in space and time: <ol> <li>satellite soil moisture (from ASCAT, Wagner et al., 2013, doi:10.1127/0941-2948/2013/0399)</li> <li>evapotranspiration (from ERA5 reanalysis by ECMWF: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview)</li> <li>ground-based rainfall.</li> </ol> and a ~3-year dataset at the same points including soil moisture from SMAP (April-2015 --> December 2017) [SM_SMAP_ASCAT_ETERA5_Pobs_1009opints.nc] The dataset can be used for testing multiple approaches for rainfall estimation from soil moisture, as done in <a href="https://www.linkedin.com/feed/hashtag/?keywords=%23SM2RAIN">#SM2RAIN</a> algorithm (<a href="http://hydrology.irpi.cnr.it/research/sm2rain/">http://hydrology.irpi.cnr.it/research/sm2rain/</a>). The global dataset we have developed is available here: <a href="https://zenodo.org/record/3635932">https://zenodo.org/record/3635932</a> The NetCDF file contains all the data, and the figures (PNG files) represent an example of the results we have obtained in the paper and of the new dataset including SMAP. Reference Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., Wagner, W. (2019). SM2RAIN-ASCAT (2007-2018): global daily satellite rainfall from ASCAT soil moisture. Earth System Science Data, 11, 1583–1601, doi:10.5194/essd-11-1583-2019. <a href="https://doi.org/10.5194/essd-11-1583-2019">https://doi.org/10.5194/essd-11-1583-2019</a>. For clarifications and support contact me at <a href="mailto:luca.brocca@irpi.cnr.it?subject=SM2RAIN%20test%20dataset">luca.brocca@irpi.cnr.it</a>

opencc-by-4.0May 2020View details →

PEATCLSM(Tb): A land surface data assimilation product for peatlands using PEATCLSM and brightness temperature (Tb) satellite observations (Northern Hemisphere output)

The datasets archived here include simulation results shown in the paper, “Improved Groundwater Table and L-band Brightness Temperature Estimates for Northern Hemisphere Peatlands Using New Model Physics and SMOS Observations in a Global Data Assimilation Framework”, published in Remote Sensing of Environment Journal (Bechtold et al., 2020). The output was produced by combining peatland-specific land surface modeling (Bechtold et al., 2019b) embedded in the NASA Catchment Land Surface Model (CLSM) with L-band brightness temperature (Tb) observations (SMOS), applying the data assimilation framework of the SMAP Level‐4 Soil Moisture product (Reichle et al., 2019). We provide netcdf files (9-km resolution EASEv2 grid, period Jan 2010 – Nov 2019, and between 45°N and 70°N, NE Asia excluded) of the four experiments of the manuscript: model-only (open-loop, OL) and data assimilation (DA) for each land model version, that is CLSM without and with the use of the PEATCLSM modules. The highest accuracy is provided by the DA product using PEATCLSM and Tb observations. When referring to the latter product use the name ‘PEATCLSM(Tb)’. We provide three types of netcdf files: •    daily_images_*.nc: Daily land states and fluxes (Table 1), provided as netCDF image-chunked image stack •    ObsFcstAna_images_*.nc: Brightness temperature observations, forecasts and analysis (Table 2), provided as netCDF image-chunked image stack •    incr_timeseries_*.nc: Data assimilation increments (Table 3), provided as netCDF timeseries-chunked image stack The file content is described in the file PEATCLSM_Tb_Documentation_20200505.pdf Please contact Michel Bechtold (michel.bechtold@kuleuven.be) for any questions. Data usage statement: This work is licensed under a Creative Commons Attribution 4.0 International License: https://creativecommons.org/licenses/by/4.0/ If you decide to work with this data, we kindly ask to be informed at the outset of the nature of this work. If the data are essential to the work, or if an important result or conclusion depends on the PEATCLSM(Tb) data product, we would appreciate that you discuss these findings with us to ensure correct use and interpretation of the PEATCLSM(Tb) product. Furthermore, we are continuously improving the data assimilation product, a discussion of your work at an early stage may (i) help us to improve our product, and (ii) allow us to provide you with a newer version. Thanks! References: Bechtold, M., De Lannoy, G. J. M., Reichle, R. H., & Koster, R. D. (2019a). PEAT-CLSM simulation output (Northern Peatlands) version 1. https://doi.org/10.17605/OSF.IO/E58YM Bechtold, M. et al. (2019b). PEAT‐CLSM: A Specific Treatment of Peatland Hydrology in the NASA Catchment Land Surface Model. Journal of Advances in Modeling Earth Systems, 11(7), 2130–2162. https://doi.org/10.1029/2018MS001574 Bechtold, M., De Lannoy, G. J. M., Reichle, R. H., Roose, D., Balliston, N., Burdun, I., Devito, K., Kurbatova, J., Strack, M., & Zarov, E. A. (2020). Improved Groundwater Table and L-band Brightness Temperature Estimates for Northern Hemisphere Peatlands Using New Model Physics and SMOS Observations in a Global Data Assimilation Framework. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2020.111805 Reichle, R. H., Liu, Q., Koster, R. D., Crow, W. T., De Lannoy, G. J. M., Kimball, J. S., Ardizzone, J. V., Bosch, D., Colliander, A., Cosh, M., Kolassa, J., Mahanama, S. P., Prueger, J., Starks, P., & Walker, J. P. (2019). Version 4 of the SMAP Level-4 Soil Moisture Algorithm and Data Product. Journal of Advances in Modeling Earth Systems, 11(10), 3106–3130. https://doi.org/10.1029/2019MS001729

opencc-by-4.0May 2020View details →

Enrichment index related to seamounts and islands in the South West Indian Ocean from chlorophyll-a satellite remote sensing data

This data set is the result of the calculation of an original “enrichment index” (EI) from chlorophyll-a (chl-a) remote sensing data (MODIS-Aqua sensor) and initially dedicated to highlight localized chl-a enrichments associated to isolated seamounts and islands in the South West Indian Ocean, in order to estimate their contribution in increasing the local primary productivity. Details and results are described in the DSR-II paper entitled “Satellite observations of phytoplankton enrichments around seamounts in the South West Indian Ocean with a special focus on the Walters Shoal” from Demarcq et al. 2020.     1. Initial data used We used daily L3 data chl-a and sea surface temperature (SST) collected by the MODIS (Moderate-resolution Imaging Spectroradiometer) sensor on board the Aqua platform (downloaded from https://oceancolor.gsfc.nasa.gov/) from January 2003 to December 2018. This has  a spatial resolution of 1/24° (ca. 4.5–5 km). The data covers the region  (45°S – 10°S / 25°W – 80°W).     2. The calculation method The calculations were done at the pixel level. The EI is the difference (expressed in %) between the value of each ‘candidate pixel’ and its medium range surrounding, defined as the average value of all chl-a values around the candidate pixel between a fix range of distance between 30 and 90 km, the R1 and R2 terms of the equation enclosed.     3. Data sets The data set contains two files:   - the monthly climatology (12 frames) of the EI from January to December (2003 to 2018 average), in an internally compressed netCDF-4 format (NC-compliant or almost)   - the yearly average of the EI (period 01/2003 - 12/2018) Two images are joined with this data set:   -  a "technical view" of the yearly average of the index for the full region sub-region (45°S – 10°S / 25°W – 80°W)      (file: indsw4_modis_p100_4km_16y_20030101_20181231.R2018.0.enrichment-index.dist-30-90km.png).   -  a slightly improved view of the yearly average of the index for the sub-region (40°S – 10°S / 30°W – 70°W).      (file: Figure-enrichment-index.pdf) An improved version of this index will be available in a near future.

opencc-by-4.0Jun 2021View details →

ML-TOMCAT V2.0: Machine-Learning-Based Satellite-Corrected Global Stratospheric Ozone Profile Dataset

MLTOMCAT V2 is 46 years (1979-2024) of gap free ozone profile data sets that is created by correcting biases in a TOMCAT Chemical Transport Model (CTM) simulated ozone profiles. We use Random Forest regression model to correct model biases.  Each file contain monthly mean zonal mean ozone profiles. There are 6 data files. <a href="https://zenodo.org/api/files/0416f9bd-c908-4d3f-9368-47ca2e04d7bd/MLTOMCAT_1979_2020_72_ht_vmr.nc">MLTOMCAT_1979_2024_72_ht_vmr_V2.nc</a> contains ozone profiles on geometric height levels (1 to 60 km)  in mixing ratio units, whereas  <a href="https://zenodo.org/api/files/0416f9bd-c908-4d3f-9368-47ca2e04d7bd/MLTOMCAT_1979_2020_72_ht_vmr.nc">MLTOMCAT_1979_2024_72_ht_nd_V2.nc</a> contains ozone profile in number density units. Similarly,  <a href="https://zenodo.org/api/files/0416f9bd-c908-4d3f-9368-47ca2e04d7bd/MLTOMCAT_1979_2020_72_ht_vmr.nc">MLTOMCAT_1979_2024_72_plev_vmr_V2.nc</a> contains ozone profiles on 43 MLS pressure levels  (1000 to 0.1 hPa)  in mixing ratio units, whereas  <a href="https://zenodo.org/api/files/0416f9bd-c908-4d3f-9368-47ca2e04d7bd/MLTOMCAT_1979_2020_72_ht_vmr.nc">MLTOMCAT_1979_2024_72_plev_nd_V2.nc</a> contains ozone profile in number density units. Please note that data below 300 hPa (~8km) and 1 hPa (~50 km) should be used with caution. There are two straospheric column files ML-TOMCAT-SCO_120ppb_boundary_V2_197901-202412.nc and ML-TOMCAT-SCO_150ppb_boundary_V2_197901-202412.nc Stratospheric column files calculated using 120 ppb and 150 ppb as a chemical ozone boundaries. A manuscript describing MLTOMCAT would be published in EESD (Dhomse et al., 2021).