Using Ranked Probability Skill Score (RPSS) as Nonlocal Root-Mean-Square Errors (RMSEs) for Mitigating Wet Bias of Soil Moisture Ocean Salinity (SMOS) Soil Moisture

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 (wos) 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 (Emus) 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 &was 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.