Towards Bayesian estimator selection for QuikSCAT wind and rain estimation

Abstract

The QuikSCAT scatterometer infers wind vectors over the ocean using measurements of the surface backscatter. During rain events the QuikSCAT observations are subject to rain contamination. Three separate estimators have been developed: wind-only, simultaneous wind and rain, and rain-only, which account for rain contamination in varying degrees. This paper introduces a Bayes estimator selection technique to adaptively choose a best estimator from among the three types of estimators at each measurement location. Bayes estimator selection is introduced from a general perspective after which it is applied specifically to QuikSCAT wind and rain estimation. Bayes estimator selection is demonstrated in a case study to illustrate improvements in wind and rain estimation which can be obtained.

A Study of Sea Surface Rain Identification Based on HY-2A Scatterometer

“…It is found that there is a good correlation between the quality control flag and rain flag, which has a certain reference value in judging whether there is rain [8][9][10]. The third strategy obtains information data by matching external data and then extends rain as an independent variable to the geophysical model function (GMF) so that the new GMF can also consider the influence of rain [11][12][13][14][15][16][17]. The fourth approach is to study the transmission process and scattering characteristics of microwaves on rainy days and then construct the microwave radiation transmission model under rainy conditions, which can correct the influence of rain on the wind measurement [18][19][20][21].…”