An Algorithm for Retrieving the 2-D Distribution of Moderate Rain by X-SAR

Abstract

Synthetic aperture radar (SAR) can detect ground information with high precision, which provides another opportunity for the retrieval of rain. Rainfall intensities in East Asia are mainly moderate. The current retrieval algorithms have high accuracy in rainstorms, but they overestimate the rainfall intensity greatly in moderate rain. Therefore, it is very important to reduce the retrieval error of SAR in moderate rain. After analyzing the scattering model of precipitation, this paper proposes an algorithm for retrieving 2-D moderate rain distribution (MRA). Since the 2-D distribution of rain is related to the vertical and horizontal distributions, MRA combines the empirical regression equation with the directional model of rain rates at different levels to retrieve the vertical distribution of precipitation. Compared with the model-oriented statistical (MOS) algorithm, MRA reduces the root mean square error when retrieving the surface rain rate from 2.6 to 0.1. In addition, based on the high-precision rain parameters retrieved by MRA, the horizontal distribution is retrieved through the likelihood distance. This horizontal distribution retrieval method not only has less amount of calculation but also avoids the difficulties of mathematical analysis.

Retrieval of Hurricane Rain Rate from SAR Images Based on Artificial Neural Network

“…ATELLITE remote sensing technology serves as a crucial mean in the study of precipitation, which can overcome the limitations imposed by geographical position and has the characteristics of wide coverage [1]. Synthetic Aperture Radar (SAR) has all-day, all-weather, highresolution ocean monitoring capabilities, and provides highquality data for the study of hurricanes.…”