Open AccessUsing Ranked Probability Skill Score (RPSS) as Nonlocal Root-Mean-Square Errors (RMSEs) for Mitigating Wet Bias of Soil Moisture Ocean Salinity (SMOS) Soil Moisture
Author(s) -
Ju Hyoung Lee
Publication year - 2020
Publication title -
photogrammetric engineering and remote sensing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.483
H-Index - 127
eISSN - 2374-8079
pISSN - 0099-1112
DOI - 10.14358/pers.86.2.91
Subject(s) - environmental science , satellite , mean squared error , water content , mathematics , physics , statistics , geology , geotechnical engineering , astronomy
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 ( SMOS ) wet biases, unlike the cumulative density function ( CDF ) matching that rescales satellite retrievals with respect to several years of reference data. Ranked probability skill score ( RPSS ) is used as nonlocal root-mean-square errors ( RMSEs ) to assess stochastic retrievals. Stochastic method successfully decreases RMSEs from 0.146 m 3 /m 3 to 0.056 m 3 /m 3 in the Republic of Benin and from 0.080 m 3 /m 3 to 0.038 m 3 /m 3 in Niger, while the CDF matching method exacerbates the original SMOS biases up to 0.141 m 3 /m 3 in Niger, and 0.120 m 3 /m 3 in Benin. Unlike the CDF matching or European Centre for Medium-Range Weather Forecasts ( ECMWF ) Re-Analysis ( ERA ))–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.