Journal cover Journal topic
Earth System Science Data The data publishing journal
Journal topic

Journal metrics

Journal metrics

  • IF value: 10.951 IF 10.951
  • IF 5-year value: 9.899 IF 5-year
  • CiteScore value: 9.74 CiteScore
  • SNIP value: 3.111 SNIP 3.111
  • IPP value: 8.99 IPP 8.99
  • SJR value: 5.229 SJR 5.229
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 38 Scimago H
    index 38
  • h5-index value: 33 h5-index 33
Discussion papers
© 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.

Data description paper 23 Apr 2019

Data description paper | 23 Apr 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Earth System Science Data (ESSD).

SM2RAIN-ASCAT (2007–2018): global daily satellite rainfall from ASCAT soil moisture

Luca Brocca1, Paolo Filippucci1, Sebastian Hahn2, Luca Ciabatta1, Christian Massari1, Stefania Camici1, Lothar Schüller3, Bojan Bojkov3, and Wolfgang Wagner2 Luca Brocca et al.
  • 1Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy
  • 2Department of Geodesy and Geoinformation, Vienna University of Technology, Vienna, Austria
  • 3European Organisation for the Exploitation of Meteorological Satellites, Darmstadt, Germany

Abstract. Long-term gridded precipitation products are crucial for several applications in hydrology, agriculture and climate sciences. Currently available precipitation products obtained from rain gauges, remote sensing and meteorological modelling suffer from space and time inconsistency due to non-uniform density of ground networks and the difficulties in merging multiple satellite sensors. The recent bottom up approach that uses satellite soil moisture observations for estimating rainfall through the SM2RAIN algorithm is suited to build long-term and consistent rainfall data record as a single polar orbiting satellite sensor is used.

We exploit here the Advanced SCATterometer (ASCAT) on board three Metop satellites, launched in 2006, 2012 and 2018. The continuity of the scatterometer sensor on European operational weather satellites is ensured until mid-2040s through the Metop Second Generation Programme. By applying SM2RAIN algorithm to ASCAT soil moisture observations a long-term rainfall data record can be obtained, also operationally available in near real time. The paper describes the recent improvements in data pre-processing, SM2RAIN algorithm formulation, and data post-processing for obtaining the SM2RAIN-ASCAT global daily rainfall dataset at 12.5 km sampling (2007–2018). The quality of SM2RAIN-ASCAT dataset is assessed on a regional scale through the comparison with high-quality ground networks in Europe, United States, India and Australia. Moreover, an assessment on a global scale is provided by using the Triple Collocation technique allowing us also the comparison with other global products such as the latest European Centre for Medium-Range Weather Forecasts reanalysis (ERA5), the Global Precipitation Measurement (GPM) mission, and the gauge-based Global Precipitation Climatology Centre (GPCC) product.

Results show that the SM2RAIN-ASCAT rainfall dataset performs relatively well both at regional and global scale, mainly in terms of root mean square error when compared to other datasets. Specifically, SM2RAIN-ASCAT dataset provides better performance better than GPM and GPCC in the data scarce regions of the world, such as Africa and South America. In these areas we expect the larger benefits in using SM2RAIN-ASCAT for hydrological and agricultural applications.

The SM2RAIN-ASCAT dataset is freely available at

Luca Brocca 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
[Login for Authors/Topical Editors] [Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement
Luca Brocca et al.
Data sets

SM2RAIN-ASCAT (2007-2018): global daily satellite rainfall from ASCAT soil moisture L. Brocca, P. Filippucci, S. Hahn, L. Ciabatta, C. Massari, S. Camici, L. Schüller, B. Bojkov, and W. Wagner

SM2RAIN test dataset with ASCAT satellite soil moisture L. Brocca

Luca Brocca et al.
Total article views: 1,128 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
927 199 2 1,128 3 5
  • HTML: 927
  • PDF: 199
  • XML: 2
  • Total: 1,128
  • BibTeX: 3
  • EndNote: 5
Views and downloads (calculated since 23 Apr 2019)
Cumulative views and downloads (calculated since 23 Apr 2019)
Viewed (geographical distribution)  
Total article views: 795 (including HTML, PDF, and XML) Thereof 788 with geography defined and 7 with unknown origin.
Country # Views %
  • 1
No saved metrics found.
No discussed metrics found.
Latest update: 17 Jul 2019
Short summary
A new global scale rainfall dataset, 12-year long (2007-2018), has been obtained by applying the SM2RAIN algorithm to ASCAT soil moisture data: SM2RAIN-ASCAT. The dataset has a spatial/temporal sampling of 12.5 km/1 day. Results show that the new dataset performs particularly good in Africa and South America, i.e., in the continents in which ground observations are scarce and the need of satellite rainfall data is high. SM2RAIN-ASCAT is available at:
A new global scale rainfall dataset, 12-year long (2007-2018), has been obtained by applying the...