A Ku-band wind and rain backscatter model at low-incidence angles using Tropical Rainfall Mapping Mission precipitation radar data. Issue 5 (4th March 2017)

• Record Type: Journal Article
• Authors: Ren, Lin; Yang, Jingsong; Zheng, Gang; Wang, Juan
• Is Part Of: International journal of remote sensing. Volume 38:Issue 5(2017)
• Page Start: 1388
• Page End: 1403
• Publication Date: 2017-03-04
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2017.1285081
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: This article proposes a simple Ku-band wind and rain backscatter model at low-incidence angles for describing sea surfaces where rain is present. The data used consisted of the sea surface backscatter and averaged rain rates from Tropical Rainfall Mapping Mission precipitation radar (TRMM PR) measurements and the collocated 10 m height numerical prediction wind speeds from the European Centre for Medium-Range Weather Forecasts. From the rain effect analysis, it was found that the surface backscatter decreases with increases in the averaged rain rate. The rain-induced backscatter was clearly dependent of the wind speed and was slightly dependent of the incidence angle. On the basis of the analysis, the wind and rain backscatter model was simply defined as the sum of a wind backscatter model and a rain backscatter model. The wind backscatter model was provided with the given Ku-band low-incidence backscatter model. The rain-induced backscatter for specific incidence angles and wind speeds was modelled as a simple quadratic polynomial function of the averaged rain rate by using the collocated datasets. The wind and rain backscatter model was validated by other parts of the collocated datasets. Results showed that the new model-predicted total backscatter had a root mean square error of 1.02 dB and a correlation coefficient ( r ) of 0.73 with the collocated TRMM PR-measured rain-affected surface backscatter. Compared to the single wind backscatter model, the wind and rain backscatter model significantly improved the prediction accuracy for the surface backscatter in rain.