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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 14 Aug 2019

Submitted as: research article | 14 Aug 2019

Review status
A revised version of this preprint is currently under review for the journal HESS.

A daily/25 km short-latency rainfall product for data scarce regions based on the integration of the GPM IMERG Early Run with multiple satellite soil moisture products

Christian Massari1, Luca Brocca1, Thierry Pellarin2, Gab Abramowitz3, Paolo Filippucci1, Luca Ciabatta1, Viviana Maggioni4, Yann Kerr5, and Diego Fernandez Prieto6 Christian Massari et al.
  • 1Via Madonna Alta 126, Perugia, Italy
  • 2Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble F-38000, France
  • 3ARC Centre of Excellence for Climate Extremes, UNSW Sydney
  • 4George Mason University, Fairfax, VA, USA
  • 5Centre d'Etudes Spatiales de la BIOsphère (CESBIO), Université Toulouse 3 CNES CNRS IRD, Toulouse, France
  • 6European Space Agency (ESA), Frascati, Italy

Abstract. Rain gauges are unevenly spaced around the world with extremely low gauge density over developing countries. For instance, in some regions in Africa the gauge density is often less than one station per 10 000 km2. The availability of rainfall data provided by gauges is also not always guaranteed in near real time or with a timeliness suited for agricultural and water resource management applications as gauges are also subject to malfunctions and regulations imposed by national authorities. A potential alternative are satellite-based rainfall estimates, yet comparisons with in-situ data suggest they're often not optimal.

In this study, we developed a short-latency (i.e., 2–3 days) rainfall product derived from the combination of the Integrated Multi-Satellite Retrievals for GPM early run (IMERG-ER) with multiple satellite soil moisture-based rainfall products derived from ASCAT, SMOS and SMAP L3 satellite soil moisture (SM) retrievals. We tested the performance of this product over four regions characterized by high quality ground-based rainfall datasets (India, Conterminous United States, Australia and Europe) and over data scarce regions in Africa and South America by using Triple Collocation analysis (TC). We found the integration of satellite SM observations with in-situ rainfall observations is very beneficial with improvements of IMERG-ER up to 20 % and 40 % in terms of correlation and error, respectively, and a generalized enhancement in terms of categorical scores with the integrated product often outperforming reanalysis and ground-based long latency datasets. Given the importance of a reliable and readily available rainfall product for water resource management and agricultural applications over data scarce regions, the developed product can provide a valuable and unique source of rainfall information for these regions.

Christian Massari et al.

Interactive discussion

Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Christian Massari et al.

Data sets

GPM+SM2RAIN (2015-2018): quasi-global 25km/daily rainfall product from the integration of GPM and SM2RAIN-based rainfall products C. Massari

Christian Massari et al.


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Publications Copernicus
Short summary
Rain gauges are unevenly spaced around the world with extremely low gauge density over developing countries like Africa and South America, regions, where water related problems like floods, drought and famine are particularly severe and able to cause fatalities, migrations and diseases. We have developed a dataset that exploits the synergies between rainfall and soil moisture to provide accurate rainfall observations to face these problems.
Rain gauges are unevenly spaced around the world with extremely low gauge density over...