Abstract
To mitigate instantaneously evolving biases in satellite retrievals, a stochastic approach is applied over West Africa. This stochastic approach independently self-corrects Soil Moisture Ocean Salinity (<small>SMOS</small>) wet biases, unlike the cumulative density function
(<small>CDF</small>) matching that rescales satellite retrievals with respect to several years of reference data. Ranked probability skill score (<small>RPSS</small>) is used as nonlocal root-mean-square errors (<small>RMSEs</small>) to assess stochastic
retrievals. Stochastic method successfully decreases <small>RMSEs</small> from 0.146 m3/m3 to 0.056 m3/m3 in the Republic of Benin and from 0.080 m3/m3 to 0.038 m3/m3 in Niger, while the <small>CDF</small>
matching method exacerbates the original <small>SMOS</small> biases up to 0.141 m3/m3 in Niger, and 0.120 m3/m3 in Benin. Unlike the <small>CDF</small> matching or European Centre for Medium-Range Weather Forecasts (<small>ECMWF</small>)
Re-Analysis (<small>ERA</small>))–interim soil moisture, only a stochastic retrieval responds to Tropical Rainfall Measuring Mission rainfall. Based on the effects of bias correction, RPSS is suggested as a nonlocal verification without needing local measurements.
Publisher
American Society for Photogrammetry and Remote Sensing
Subject
Computers in Earth Sciences
Cited by
2 articles.
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