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Earth System Science Data The Data Publishing Journal
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Peer-reviewed comment
21 Sep 2017
Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Earth System Science Data (ESSD).
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, Christoph Reimer3, Sebastian Hahn2, Christoph Paulik2, Wouter Dorigo2, Richard Kidd3, and Wolfgang Wagner2 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, Italy
Abstract. Accurate and long-term rainfall estimates are the main inputs for several applications, spanning 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 stateof-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 modelled data set (ERA-Interim). The assessment is carried out on a global scale at 1° of spatial sampling and 5-day 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 < 10.34 mm) and BIAS (median value < −14.44 %) during the evaluation period. The validation has been also carried out at original resolution (0.25°) over Europe, Australia and other 5 areas worldwide to test the capabilities of the data set to correctly identify rainfall events under different climate and precipitation regimes.

The CCI-SM derived rainfall data set is freely available at at

Citation: 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 Discuss.,, in review, 2017.
Luca Ciabatta et al.
Luca Ciabatta et al.

Data sets

SM2RAIN-CCI (1 Jan 1998–31 December 2015) global daily rainfall dataset
L. Ciabatta, C. Massari, L. Brocca, A. Gruber, C. Reimer, S. Hahn, C. Paulik, W. Dorigo, R. Kidd, and W. Wagner
Luca Ciabatta et al.


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Short summary
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...