Reconstructing and Nowcasting the Rainfall Field by a CML Network

Abstract

Currently, the opportunistic method to estimate rainfall using commercial microwave links (CMLs) has been shown as an efficient way to complement traditional instruments in terms of spatial‐temporal resolution and coverage. In this paper, we collected data from 26 CMLs in Jiangyin City, Jiangsu Province, and conducted experiments on rainfall field reconstruction and nowcasting. First, the raw CML data were processed to invert the path‐averaged rainfall intensity. Second, the algorithms of inverse distance weighting (IDW) and ordinary kriging (OK) interpolation were employed to reconstruct the rainfall field. Then a 10‐min prediction of the rainfall field was achieved using a nowcasting model based on the long short‐term memory neural network and a setup window was introduced to improve the prediction performance of the first few minutes. The reconstruction results show that the average correlation coefficient (ACC) and the average root mean square error (ARMSE) between the IDW‐based results and daily cumulative rainfall from rain gauges (RGs) are 0.89 and 8.69 mm, respectively, while the ACC and ARMSE between the OK‐based results and RG data are 0.89 and 9.13 mm, respectively. The nowcasting results show that the ACC between the prediction results with a 5‐min setup window and the IDW‐retrieved rainfall fields can reach 0.91 at the first minute and gradually decrease to 0.20 within 10 min. Furthermore, the model has better nowcasting performance for stratiform precipitation and mixed precipitation compared to convective precipitation.

Precipitation Monitoring Using Commercial Microwave Links: Current Status, Challenges and Prospectives

“…Imhoff et al [185] constructed nowcasts for the first time in the Netherlands using country-wide rainfall maps based on CMLs and found that CMLs gave better results than radar in general. Recently, Zhang et al [184] also carried out rainfall field nowcasting experiments based on CMLs in Jiangyin City, China, and found that the LSTM-based nowcasting algorithm has better performance for stratiform and mixed precipitation compared to convective precipitation. In addition, based on the assumption that atmospheric variables related to rainfall change prior to a rainfall event affect CMLs, CMLs can also directly predict whether and how much rain will fall in the future without the need for rainfall mapping [197].…”