SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations
<p>Long-term gridded precipitation products are crucial for several applications in hydrology, agriculture and climate sciences. Currently available precipitation products suffer from space and time inconsistency due to the non-uniform density of ground networks and the difficulties in merging...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2019-10-01
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| Series: | Earth System Science Data |
| Online Access: | https://www.earth-syst-sci-data.net/11/1583/2019/essd-11-1583-2019.pdf |
| _version_ | 1819149449869393920 |
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| author | L. Brocca P. Filippucci S. Hahn L. Ciabatta C. Massari S. Camici L. Schüller B. Bojkov W. Wagner |
| author_facet | L. Brocca P. Filippucci S. Hahn L. Ciabatta C. Massari S. Camici L. Schüller B. Bojkov W. Wagner |
| author_sort | L. Brocca |
| collection | DOAJ |
| description | <p>Long-term gridded precipitation products are crucial for several
applications in hydrology, agriculture and climate sciences. Currently
available precipitation products suffer from space and time inconsistency
due to the non-uniform density of ground networks and the difficulties in
merging multiple satellite sensors. The recent “bottom-up” approach that
exploits satellite soil moisture observations for estimating rainfall
through the SM2RAIN (Soil Moisture to Rain) algorithm is suited to build a consistent rainfall data
record as a single polar orbiting satellite sensor is used.</p>
<p>Here we exploit the Advanced SCATterometer (ASCAT) on board three Meteorological Operational (MetOp)
satellites, launched in 2006, 2012, and 2018, as part of the European Organisation for the Exploitation of
Meteorological Satellites (EUMETSAT) Polar
System programme. The continuity of the scatterometer sensor is ensured
until the mid-2040s through the MetOp Second Generation Programme. Therefore, by
applying the SM2RAIN algorithm to ASCAT soil moisture observations, a long-term
rainfall data record will be obtained, starting in 2007 and lasting until the mid-2040s. The
paper describes the recent improvements in data pre-processing, SM2RAIN
algorithm formulation, and data post-processing for obtaining the
SM2RAIN–ASCAT quasi-global (only over land) daily rainfall data record at a
12.5 km spatial sampling from 2007 to 2018. The quality of the SM2RAIN–ASCAT data record
is assessed on a regional scale through comparison with high-quality
ground networks in Europe, the United States, India, and Australia. Moreover, an
assessment on a global scale is provided by using the triple-collocation (TC)
technique allowing us also to compare these data with the latest, fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis
(ERA5), the Early Run version of the Integrated Multi-Satellite Retrievals
for Global Precipitation Measurement (IMERG), and the gauge-based Global
Precipitation Climatology Centre (GPCC) products.</p>
<p>Results show that the SM2RAIN–ASCAT rainfall data record performs relatively
well at both a regional and global scale, mainly in terms of root mean square
error (RMSE) when compared to other products. Specifically, the SM2RAIN–ASCAT data
record provides performance better than IMERG and GPCC in data-scarce
regions of the world, such as Africa and South America. In these areas, we
expect larger benefits in using SM2RAIN–ASCAT for hydrological and
agricultural applications. The limitations of the SM2RAIN–ASCAT data record consist
of the underestimation of peak rainfall events and the presence of
spurious rainfall events due to high-frequency soil moisture fluctuations
that might be corrected in the future with more advanced bias correction
techniques.</p>
<p>The SM2RAIN–ASCAT data record is freely available at
<a href="https://doi.org/10.5281/zenodo.3405563">https://doi.org/10.5281/zenodo.3405563</a> (Brocca et al., 2019) (recently extended to the end of
August 2019).</p> |
| first_indexed | 2024-12-22T14:01:48Z |
| format | Article |
| id | doaj.art-101a0a0b56c848e280c382d4f5e826be |
| institution | Directory Open Access Journal |
| issn | 1866-3508 1866-3516 |
| language | English |
| last_indexed | 2024-12-22T14:01:48Z |
| publishDate | 2019-10-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Science Data |
| spelling | doaj.art-101a0a0b56c848e280c382d4f5e826be2022-12-21T18:23:23ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162019-10-01111583160110.5194/essd-11-1583-2019SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observationsL. Brocca0P. Filippucci1S. Hahn2L. Ciabatta3C. Massari4S. Camici5L. Schüller6B. Bojkov7W. Wagner8Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, ItalyResearch Institute for Geo-Hydrological Protection, National Research Council, Perugia, ItalyDepartment of Geodesy and Geoinformation, TU Wien, Vienna, AustriaResearch Institute for Geo-Hydrological Protection, National Research Council, Perugia, ItalyResearch Institute for Geo-Hydrological Protection, National Research Council, Perugia, ItalyResearch Institute for Geo-Hydrological Protection, National Research Council, Perugia, ItalyEuropean Organisation for the Exploitation of Meteorological Satellites, Darmstadt, GermanyEuropean Organisation for the Exploitation of Meteorological Satellites, Darmstadt, GermanyDepartment of Geodesy and Geoinformation, TU Wien, Vienna, Austria<p>Long-term gridded precipitation products are crucial for several applications in hydrology, agriculture and climate sciences. Currently available precipitation products suffer from space and time inconsistency due to the non-uniform density of ground networks and the difficulties in merging multiple satellite sensors. The recent “bottom-up” approach that exploits satellite soil moisture observations for estimating rainfall through the SM2RAIN (Soil Moisture to Rain) algorithm is suited to build a consistent rainfall data record as a single polar orbiting satellite sensor is used.</p> <p>Here we exploit the Advanced SCATterometer (ASCAT) on board three Meteorological Operational (MetOp) satellites, launched in 2006, 2012, and 2018, as part of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Polar System programme. The continuity of the scatterometer sensor is ensured until the mid-2040s through the MetOp Second Generation Programme. Therefore, by applying the SM2RAIN algorithm to ASCAT soil moisture observations, a long-term rainfall data record will be obtained, starting in 2007 and lasting until the mid-2040s. The paper describes the recent improvements in data pre-processing, SM2RAIN algorithm formulation, and data post-processing for obtaining the SM2RAIN–ASCAT quasi-global (only over land) daily rainfall data record at a 12.5 km spatial sampling from 2007 to 2018. The quality of the SM2RAIN–ASCAT data record is assessed on a regional scale through comparison with high-quality ground networks in Europe, the United States, India, and Australia. Moreover, an assessment on a global scale is provided by using the triple-collocation (TC) technique allowing us also to compare these data with the latest, fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5), the Early Run version of the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), and the gauge-based Global Precipitation Climatology Centre (GPCC) products.</p> <p>Results show that the SM2RAIN–ASCAT rainfall data record performs relatively well at both a regional and global scale, mainly in terms of root mean square error (RMSE) when compared to other products. Specifically, the SM2RAIN–ASCAT data record provides performance better than IMERG and GPCC in data-scarce regions of the world, such as Africa and South America. In these areas, we expect larger benefits in using SM2RAIN–ASCAT for hydrological and agricultural applications. The limitations of the SM2RAIN–ASCAT data record consist of the underestimation of peak rainfall events and the presence of spurious rainfall events due to high-frequency soil moisture fluctuations that might be corrected in the future with more advanced bias correction techniques.</p> <p>The SM2RAIN–ASCAT data record is freely available at <a href="https://doi.org/10.5281/zenodo.3405563">https://doi.org/10.5281/zenodo.3405563</a> (Brocca et al., 2019) (recently extended to the end of August 2019).</p>https://www.earth-syst-sci-data.net/11/1583/2019/essd-11-1583-2019.pdf |
| spellingShingle | L. Brocca P. Filippucci S. Hahn L. Ciabatta C. Massari S. Camici L. Schüller B. Bojkov W. Wagner SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations Earth System Science Data |
| title | SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations |
| title_full | SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations |
| title_fullStr | SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations |
| title_full_unstemmed | SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations |
| title_short | SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations |
| title_sort | sm2rain ascat 2007 2018 global daily satellite rainfall data from ascat soil moisture observations |
| url | https://www.earth-syst-sci-data.net/11/1583/2019/essd-11-1583-2019.pdf |
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