Development of precipitation retrieval based on multimode radar remote sensing

Abstract

Rainfall is a key link in the earth’s water cycle and one of the most important inputs to hydrological and land surface models. Thus far, radar-based rainfall information has been widely used in global surface hydrology process simulation, disastrous weather forecast, flood control, and disaster relief because it provides data with a high spatial and temporal resolution that improves rainfall representation. In recent years, precipitation radar has gradually developed in a multiangle, multifrequency, dual-polarization, multiresolution, and multiantenna direction under different meteorological observation requirements and rainfall application scenarios. The radar observes rainfall in the air. Meteorological and hydrological research have different requirements for rainfall observation on the temporal and spatial scales. Thus, a series of conversion processes from radar rainfall observation in the air to the land surface generates considerable uncertainties. Thus, the accuracy of radar precipitation must be improved by systematic deviation corrections and data processing. In this study, a comprehensive review of the process of radar rainfall inversion aims to provide a general picture of the current state of multimode radar technology. First, the development characteristics of multimode radar remote sensing technology were summarized. Afterward, the basic process of converting aerial rainfall observed by radar to the surface of the land surface was sorted out. Furthermore, we reviewed and summarized the major progress and methods of precipitation radar rainfall inversion. The upscaling and downscaling applications in hydrological and land surface models were compared in the literature review. Then, we analyzed the factors causing the air-land surface rainfall deviation in the radar, such as raindrop evaporation, drift, and fragmentation. The calculation method of raindrop evolution deviation was also summarized. Moreover, we summarized the current methods for correcting radar rainfall based on ground-based reference rainfall, such as the rainfall observed by rain gauges. Finally, on the basis of this review, we discussed the existing major challenges and prospects in multimode radar precipitation, including developing multiscale inversion of surface rainfall, multimodel surface rainfall data fusion, surface rainfall inversion considering microphysical deviation of raindrops, and multimode radar data mining based on machine learning.