http://www.cnr.it/ontology/cnr/individuo/prodotto/ID280979

Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data (Articolo in rivista)

Type

Prodotto della ricerca (Classe)
Articolo in rivista (Classe)

Label

Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data (Articolo in rivista) (literal)

Anno

2014-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

10.1002/ 2014JD021489 (literal)

Alternative label

L. Brocca, L. Ciabatta, C. Massari, T. Moramarco, S. Hahn, S. Hasenauer, R. Kidd, W. Dorigo, W. Wagner, V. Levizzani (2014)
Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data
in Journal of geophysical research
(literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

L. Brocca, L. Ciabatta, C. Massari, T. Moramarco, S. Hahn, S. Hasenauer, R. Kidd, W. Dorigo, W. Wagner, V. Levizzani (literal)

Pagina inizio

5128 (literal)

Pagina fine

5141 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

http://onlinelibrary.wiley.com/doi/10.1002/2014JD021489/abstract;jsessionid=ED8AF33A7E756867C2CB6DBC5D7F1618.f03t01 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

119 (literal)

Rivista

Journal of geophysical research (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo

9 (literal)

Note

ISI Web of Science (WOS) (literal)
Scopus (literal)
Google Scholar (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

CNR-IRPI, Perugia Technische Universitaet, Wien CNR-ISAC, Bologna (literal)

Titolo

Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data (literal)

Abstract

Measuring precipitation intensity is not straightforward; and overmany areas, ground observations are lacking and satellite observations are used to fill this gap. The most common way of retrieving rainfall is by addressing the problem \"top-down\" by inverting the atmospheric signals reflected or radiated by atmospheric hydrometeors. However, most applications are interested in how much water reaches the ground, a problem that is notoriously difficult to solve froma top-down perspective. In this study, a novel \"bottom-up\" approach is proposed that, by doing \"hydrology backward,\" uses variations in soil moisture (SM) sensed by microwave satellite sensors to infer preceding rainfall amounts. In other words, the soil is used as a natural rain gauge. Three different satellite SM data sets from the Advanced SCATterometer (ASCAT), the Advanced Microwave Scanning Radiometer (AMSR-E), and the Microwave Imaging Radiometer with Aperture Synthesis are used to obtain three new daily global rainfall products. The \"First Guess Daily\" product of the Global Precipitation Climatology Centre (GPCC) is employed asmain benchmark in the validation period 2010-2011 for determining the continuous and categorical performance of the SM-derived rainfall products by considering the 5 day accumulated values. The real-time version of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis product, i.e., the TRMM-3B42RT, is adopted as a state-of-the-art satellite rainfall product. The SM-derived rainfall products show good Pearson correlation values (R) with the GPCC data set, mainly in areas where SM retrievals are found to be accurate. The global median R values (in the latitude band ±50°) are equal to 0.54, 0.28, and 0.31 for ASCAT-, AMSR-E-, and SMOS-derived products, respectively. For comparison, the median R for the TRMM-3B42RT product is equal to 0.53. Interestingly, the SM-derived products are found to outperform TRMM-3B42RT in terms of average global root-mean-square error statistics and in terms of detection of rainfall events. The regions for which the SM-derived products perform very well are Australia, Spain, South and North Africa, India, China, the Eastern part of South America, and the central part of the United States. The SM-derived products are found to estimate accurately the rainfall accumulated over a 5 day period, an aspect particularly important for their use for hydrological applications, and that address the difficulties of estimating light rainfall from TRMM-3B42RT. (literal)

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