An Approach for Radar Quantitative Precipitation Estimation Based on Spatiotemporal Network

Abstract

Radar quantitative precipitation estimation (QPE) is a key and challenging task for many designs and applications with meteorological purposes. Since the Z-R relation between radar and rain has a number of parameters on different areas, and the rainfall varies with seasons, the traditional methods are incapable of achieving high spatial and temporal resolution and thus difficult to obtain a refined rainfall estimation. This paper proposes a radar quantitative precipitation estimation algorithm based on the spatiotemporal network model (ST-QPE), which designs a convolutional time-series network QPE-Net8 and a multi-scale feature fusion time-series network QPE-Net22 to address these limitations. We report on our investigation into contrast reversal experiments with radar echo and rainfall data collected by the Hunan Meteorological Observatory. Experimental results are verified and analyzed by using statistical and meteorological methods, and show that the ST-QPE model can inverse the rainfall information corresponding to the radar echo at a given moment, which provides practical guidance for accurate short-range precipitation nowcasting to prevent and mitigate disasters efficiently.