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Volume 10, issue 1
Earth Syst. Sci. Data, 10, 267-280, 2018
https://doi.org/10.5194/essd-10-267-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Earth Syst. Sci. Data, 10, 267-280, 2018
https://doi.org/10.5194/essd-10-267-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Peer-reviewed comment 08 Feb 2018

Peer-reviewed comment | 08 Feb 2018

SM2RAIN-CCI: a new global long-term rainfall data set derived from ESA CCI soil moisture

Luca Ciabatta1,4, Christian Massari1, Luca Brocca1, Alexander Gruber2,5, Christoph Reimer3, Sebastian Hahn2, Christoph Paulik2,6, Wouter Dorigo2, Richard Kidd3, and Wolfgang Wagner2 Luca Ciabatta et al.
  • 1Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy
  • 2Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria
  • 3Earth Observation Data Centre, Vienna, Austria
  • 4Department of Civil and Environmental Engineering, University of Perugia, Perugia, Italy
  • 5Department of Earth and Environmental Sciences, KU Leuven, Heverlee, 3001, Belgium
  • 6Vandersat B.V., Haarlem, the Netherlands

Abstract. Accurate and long-term rainfall estimates are the main inputs for several applications, from crop modeling to climate analysis. In this study, we present a new rainfall data set (SM2RAIN-CCI) obtained from the inversion of the satellite soil moisture (SM) observations derived from the ESA Climate Change Initiative (CCI) via SM2RAIN (Brocca et al., 2014). Daily rainfall estimates are generated for an 18-year long period (1998–2015), with a spatial sampling of 0.25° on a global scale, and are based on the integration of the ACTIVE and the PASSIVE ESA CCI SM data sets.

The quality of the SM2RAIN-CCI rainfall data set is evaluated by comparing it with two state-of-the-art rainfall satellite products, i.e. the Tropical Measurement Mission Multi-satellite Precipitation Analysis 3B42 real-time product (TMPA 3B42RT) and the Climate Prediction Center Morphing Technique (CMORPH), and one modeled data set (ERA-Interim). A quality check is carried out on a global scale at 1° of spatial sampling and 5 days of temporal sampling by comparing these products with the gauge-based Global Precipitation Climatology Centre Full Data Daily (GPCC-FDD) product. SM2RAIN-CCI shows relatively good results in terms of correlation coefficient (median value  > 0.56), root mean square difference (RMSD, median value  < 10.34mm over 5 days) and bias (median value  < −14.44%) during the evaluation period. The validation has been carried out at original resolution (0.25°) over Europe, Australia and five other areas worldwide to test the capabilities of the data set to correctly identify rainfall events under different climate and precipitation regimes.

The SM2RAIN-CCI rainfall data set is freely available at https://doi.org/10.5281/zenodo.846259.

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In this study, rainfall is estimated starting from satellite soil moisture observation on a global scale, using the ESA CCI soil moisture datasets. The new obtained rainfall product has proven to correctly identify rainfall events, showing performance sometimes higher than those obtained by using classical rainfall estimation approaches.
In this study, rainfall is estimated starting from satellite soil moisture observation on a...
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