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Received：30 November 2021，
Published：07 July 2023
移动端阅览
降雨是地球系统水循环的核心组成要素，也是水文模型和陆面模式最重要的输入项之一。基于雷达遥感的降雨观测作为区域降雨监测的有效手段，为陆表过程模拟、灾害性天气探测和防洪减灾决策提供了良好的数据支撑。近年来，在不同观测需求和降雨应用场景下，雷达所获取的信息逐渐向双偏振、多角度、多频率、多分辨率与多天线等多模式方向发展。由于雷达观测的间接性，以及气象和水文研究对降雨观测时空尺度需求的多样性，从雷达观测的空中水汽到陆表降雨需要经历一系列转换过程，其中产生的大量不确定性，需要进行系统的偏差校正与数据处理，从而提高雷达陆表降雨反演的精度。基于此，本文总结了当下国内外多模式雷达遥感观测技术特点，对多模式雷达陆表降雨反演过程及方法进行了系统梳理，对此过程中的误差因子和校正方法进行了对比分析，最后展望了多模式雷达遥感在降雨观测领域的未来研究趋势。
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.
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