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9 results for “SM2RAIN”
Monthly precipitation in mm at 1 km resolution (multisource average) based on SM2RAIN-ASCAT 2007-2021, CHELSA Climate and WorldClim
<p>Monthly precipitation in mm at 1 km resolution based on SM2RAIN-ASCAT 2007-2021 (<a href="https://doi.org/10.5281/zenodo.2615278">https://doi.org/10.5281/zenodo.2615278</a>). <a href="https://github.com/Envirometrix/LandGISmaps/tree/master/input_layers/clim1km">Downscaled to 1 km resolution using gdalwarp</a> (cubic splines) and combined with WorldClim (<a href="https://worldclim.org/data/worldclim21.html">https://worldclim.org/data/worldclim21.html</a>) and CHELSA Climate (<a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a>) monthly values. Final values are estimated as a simple average between the three precipitation data sources; a more objective approach would be to use training points e.g. meteo-station monthly values, then train an ensemble model using the 3 data sources as independent variables. Another global data source of precipitation images is the <a href="https://gpm.nasa.gov/data/imerg">monthly IMERGE dataset</a>, however this requires transformation and is available only for limited span of years.</p> <p>Processing steps are available <a href="https://gitlab.com/openlandmap/global-layers/-/tree/master/input_layers/SM2RAIN"><strong>here</strong></a>. Antarctica is not included. Standard deviation (sd) indicates a difference between the 3 data sources. To access and visualize maps use: <a href="https://openlandmap.org"><strong>https://openlandmap.org</strong></a>.<strong> </strong>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>clm = theme: climate,</li> <li>precipitation = variable: precipitation,</li> <li>wc.v2.1.chelsa.v2.1.sm2rain.oct = determination method: long-term average values for October,</li> <li>m = mean value,</li> <li>1km = spatial resolution / block support: 1 km,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>1980..2020 = time reference: from 1980 to 2020,</li> <li>v0.3 = version number: 0.3,</li> </ul>
SM2RAIN-ASCAT (2007-2021) global daily satellite rainfall including aggregated values and trend parameters as 10km resolution GeoTIFFs
<p>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"><strong>here</strong></a>. Few important notes:</p> <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> <p>Includes also long-term trends (trend.logit.ols) which was produced by fitting regression models to de-seasonalized time-series as explained in this <strong><a href="https://gitlab.com/openlandmap/global-layers/-/blob/master/input_layers/MOD13Q1/03-data-access.ipynb">python tutorial</a></strong>. Basically models are fitted for <strong>each pixel</strong> and the model parameters are saved as images.</p> <p>Monthly averages and s.d. of precipitation are available in the files:</p> <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> <p>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>).</p> <p>To cite this data set please refer to the <strong><a href="https://doi.org/10.5281/zenodo.2591214">original copy</a></strong> of the data set.</p> <ul> <li>Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., Wagner, W. (2019). <strong><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></strong>. 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>
SM2RAIN test dataset with ASCAT and SMAP satellite soil moisture (plus ERA5 evapotranspiration)
<p>Are you looking for a research contest?</p> <p>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:</p> <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> <p>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]</p> <p>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>).</p> <p>The global dataset we have developed is available here: <a href="https://zenodo.org/record/3635932">https://zenodo.org/record/3635932</a></p> <p>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.</p> <p><strong>Reference</strong><br> 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. <em>Earth System Science Data</em>, 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>.</p> <p>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> </p>
SM2RAIN-ASCAT (2007-2022): global daily satellite rainfall from ASCAT soil moisture
<p><strong>SM2RAIN-ASCAT is a new global scale rainfall product</strong> obtained from ASCAT satellite soil moisture data through the SM2RAIN algorithm (<em>Brocca et al., 2014; 2019</em>). The SM2RAIN-ASCAT rainfall dataset (in mm/day) is provided over a regular grid at 0.1-degree sampling (3600x1801) on a global scale. The product represents the accumulated rainfall between the 00:00 and the 23:59 UTC of the indicated day. The SM2RAIN method was applied to the ASCAT soil moisture product (<em>Wagner et al., 2013</em>) for the period from January 2007 to December 2022 (16 years), for version 2.1.2n.</p> <p>The rainfall dataset is provided in NetCDF format. A total of 16 NetCDF files, one per year, are provided. The quality flag provided with the dataset has been used to mask out low quality data, as well as the areas characterised by complex topographic, frozen soil, and presence of tropical forests. In addition to the daily accumulated rainfall value, also the rainfall noise (mm/day) is provided for every day.</p> <p><strong>Version 2.1.2 should not be used due to an error in the precipitation data. Version 2.1.2n with respect to version 2.1 is calibrated anew and extended to December 2022.</strong></p> <p>A GeoTIFF version of the dataset (v1.5) is available here: <a href="https://doi.org/10.5281/zenodo.2615278">https://doi.org/10.5281/zenodo.2615278</a></p> <p>A monthly version at 0.25- and 0.5-degree resolution (v1.4) is available here: <a href="https://doi.org/10.5281/zenodo.4570191">https://doi.org/10.5281/zenodo.4570191</a></p> <p>A sample dataset that can be used for testing SM2RAIN algorithm is available here: <a href="../record/2580285#.XLrYDugzbIU">https://zenodo.org/record/2580285</a></p> <p>Details on the dataset development and its assessment with ground and reanalysis observations are provided as:</p> <p><strong>Brocca, L.</strong>, 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. <em>Earth System Science Data</em>, 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>.</p> <p><strong>Simple Python and Matlab codes for the extraction of SM2RAIN-ASCAT rainfall at one and multiple station(s)\location(s) are available at (note that reader for versions <1.3 are not usable for version >1.3): </strong><a href="https://github.com/IRPIhydrology/SM2RAIN_ASCAT_reader">https://github.com/IRPIhydrology/SM2RAIN_ASCAT_reader</a></p> <p><strong>The SM2RAIN code in Python is available here</strong>: <a href="https://github.com/IRPIhydrology/sm2rain">https://github.com/IRPIhydrology/sm2rain</a><br><strong>The SM2RAIN code in Matlab is available here</strong>: <a href="https://github.com/IRPIhydrology/SM2RAIN_Matlab">https://github.com/IRPIhydrology/SM2RAIN_Matlab</a><br><strong>The SM2RAIN code in R is available here</strong>: <a href="https://github.com/IRPIhydrology/sm2rainR">https://github.com/IRPIhydrology/sm2rainR</a></p> <p> </p> <p><strong>Acknowledgements</strong></p> <ul> <li>EUMETSAT Global SM2RAIN project (contract n° EUM/CO/17/4600001981/BBo)</li> <li>EUMETSAT "Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF)" CDOP 3 (EUM/C/85/16/DOC/15).</li> </ul>
SM2RAIN-CCI (1 Jan 1998 – 31 December 2015) global daily rainfall dataset
<p>A NEW GLOBAL SCALE RAINFALL PRODUCT obtained from satellite soil moisture data through the SM2RAIN algorithm (<em>Brocca et al., 2014</em>), at 0.25 degree/daily spatial-temporal resolution, has been delivered (Ciabatta et al., 2018). The SM2RAIN method was applied to the ESA CCI soil moisture Active and Passive products (<em>Liu et al., 2011, 2012; Wagner et al., 2012</em>) for the period from January 1998 to December 2015 (18 years).</p> <p>The CCI-derived rainfall datasets (in mm/day) is gridded over a 0.25-degree grid on a global scale. The number of dates is 6574 (1998/01/01 – 2015/12/31). The product represents the cumulated rainfall between the 00:00 and the 23:59 UTC of the indicated day. A climatological correction has been applied to the data at monthly scale.</p> <p>The rainfall dataset is provided in netCDF format. A total of 18 netCDF files, one per year, are provided.</p> <p>The rainfall dataset is obtained by applying the SM2RAIN algorithm to the ESA CCI soil moisture Active and Passive products at version 03.1 separately. Then, an integration procedure based on a weighted average is applied in order to obtain the rainfall estimate. The algorithm has been calibrated during three different periods (1998-2001, 2002-2006 and 2007-2013) against the Global Precipitation Climatology Centre Full-Data daily dataset (GPCC-FDD, Schamm et al., 2015). The quality flag provided within the raw soil moisture observations has been used to mask out low quality data, as well as the areas characterized by high topographic complexity, high frozen soil and snow probability and presence of tropical forests.</p> <p><strong>References</strong></p> <p>Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S., Kidd, R., Dorigo, W., Wagner, W., Levizzani, V. (2014). Soil as a natural rain gauge: estimating global rainfall from satellite soil moisture data. <em>Journal of Geophysical Research</em>, 119(9), 5128-5141, doi:10.1002/2014JD021489.</p> <p>Ciabatta, L., Massari, C., Brocca, L., Gruber, A., Reimer, C., Hahn, S., Paulik, C., Dorigo, W., Kidd, R., and Wagner, W.: SM2RAIN-CCI: a new global long-term rainfall data set derived from ESA CCI soil moisture, Earth Syst. Sci. Data, 10, 267-280, https://doi.org/10.5194/essd-10-267-2018, 2018.</p> <p>Liu, Y. Y., Parinussa, R. M., Dorigo, W. A., De Jeu, R. A. M., Wagner, W., van Dijk, A. I. J. M., McCabe, M. F., Evans, J. P. (2011). Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrology and Earth System Sciences, 15, 425-436, doi:10.5194/hess-15-425-2011.</p> <p>Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M., Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014.</p> <p>Schamm, K., Ziese, M., Raykova, K., Becker, A., Finger, P., Meyer-Christoffer, A., Schneider, U. (2015). GPCC Full Data Daily Version 1.0 at 1.0°: Daily Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historic Data. DOI: 10.5676/DWD_GPCC/FD_D_V1_100.</p> <p>Wagner, W., Dorigo, W., de Jeu, R., Fernandez, D., Benveniste, J., Haas, E., Ertl, M. (2012). Fusion of active and passive microwave observations to create an Essential Climate Variable data record on soil moisture, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Annals), Volume I-7, XXII ISPRS Congress, Melbourne, Australia, 25 August-1 September 2012, 315-321.</p>
SM2RAIN-Climate (1998-2021): monthly global satellite rainfall dataset
<p><strong>SM2RAIN-Climate</strong> rainfall product is a new long-term global scale rainfall product developed by using the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture product v06.1 as input into the SM2RAIN algorithm (<em>Brocca et al., 2014; 2019</em>). The SM2RAIN-Climate global rainfall dataset is generated in the period 1998-2021 with monthly temporal and 1° spatial resolutions, which provide the opportunity for climatological studies.</p> <p>Four different SM2RAIN-Climate datasets are provided in NetCDF format. For each dataset, the spatial grid (latitude and longitude), the rainfall values, and the mask type is defined in each NetCDF file. Two different masks are the temperature mask in data post-processing and a threshold value (percentage of missing data) taking into account missing data within a month. Depending on the application, the user can select the more suitable product.</p> <p>Details on the dataset development is provided as:</p> <p>Mosaffa, H., Filippucci, P., Massari, C., Ciabatta, L., & Brocca, L. (2023). SM2RAIN-Climate, a monthly global long-term rainfall dataset for climatological studies. <em>Scientific Data</em>, <em>10</em>(1), 749. <a href="https://doi.org/10.1038/s41597-023-02654-6"><em>https://doi.org/10.1038/s41597-023-02654-6</em></a></p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The work is supported by the Open-Earth-Monitor Cyberinfrastructure project that has received funding from the European Union's Horizon Europe research and innovation programme (grant agreement no. 101059548) and by the European Space Agency through the Digital Twin Earth Hydrology project (grant no. ESA 4000129870/20/I-NB - CCN N. 1) and the 4DMED Hydrology project (grant no. ESA 4000136272/21/I-EF).</p>
SM2RAIN CHINA: 1 km rainfall estimation for CHINA region derived from SMCI Soil Moisture (2000-2020)
<p><strong>SM2RAIN-SMCI provides rainfall estimates for the whole CHINA at 1 km/daily spatial resolution </strong>by applying SM2RAIN algorithm (<em>Brocca et al., 2014; 2019) </em>to Soil Moisture data from SMCI Soil moisture (<a href="https://doi.org/10.5194/essd-14-5267-2022"><em>https://doi.org/10.5194/essd-14-5267-2022</em></a>) for the period from January 2000 to December 2020 (21 years). The SM2RAIN-SMCI rainfall dataset (in mm/day) is provided over a regular grid at 0.01-degree sampling. The product represents the accumulated rainfall between the 00:00 and the 23:59 UTC of the indicated day. The uncalibrated SM2RAIN version (<a href="https://doi.org/10.5194/hess-26-2481-2022">https://doi.org/10.5194/hess-26-2481-2022</a>) was adopted to obtain rainfall. Precipitation data from HRLT (<a href="https://doi.org/10.5194/essd-14-4793-2022">https://doi.org/10.5194/essd-14-4793-2022</a>) were adopted to obtain mean climatology.</p> <p>The rainfall dataset is provided in NetCDF format. To simplify data access, the CHINA region is divided in 101 subregion containing 540 (longitude) * 360 (latitude) pixels each.</p>
4DMED precipitation product: 1 km merged precipitation from CPC, IMERG-LR and SM2RAIN-ASCAT for mediterranean basin
<p>The high resolution satellite precipitation product is based on the integration of multiple precipitation and rainfall datasets to generate a high spatial (1 km) and temporal (daily) resolution precipitation product over the Mediterranean area. The following precipitation and rainfall datasets are downscaled and merged together: GPM-Late run, CPC, SM2RAIN-ASCAT. All these products are originally at coarse spatial resolution (>10 km) and have been downscaled to 1 km spatial resolution using CHELSA (1 km) climatology. The three products are then merged with a triple collocation technique. In the areas covered by snow only GPM-Late run and CPC are merged together, again using the triple collocation to obtain the relative weights (third product: ERA5 Land precipitation). The product has been developed in the framework of the 4D-MED Hydrology project. The product is available in the period 2015-2022. </p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The work is supported by the European Space Agency (ESA) through the 4D-MED Hydrology project (grant no. ESA 4000136272/21/I-EF)</p>
GPM+SM2RAIN (2007-2018): quasi-global 25km/daily rainfall product from the integration of GPM and SM2RAIN-based rainfall products
<p>Rainfall dataset based on the integration of IMERG-LR of the Global Precipitation Measurement Mission (GPM) with SM2RAIN-based rainfall estimates derived from ASCAT H113 H-SAF , SMOS L3 and SMAP L3 soil moisture products. The dataset is available +/-60 ° (2007-2018). More details on the grid and data can be found in the netCDF file.</p> <p>The quality flag provided with the dataset has been used to mask out low quality data, as well as the areas characterised by complex topographic, frozen soil, and presence of tropical forests.</p> <p>The associated <a href="https://www.hydrol-earth-syst-sci.net/24/2687/2020/">publication is avalaible on Hydrology and Earth System Science journal</a></p> <p> </p>
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