Performance Comparison between Deep Learning and Optical Flow-Based Techniques for Nowcast Precipitation from Radar Images

Forecasting

卷:2

Luca Massidda¹  Marino Marrocu¹ 

[1] CRS4, Center for Advanced Studies, Research and Development in Sardinia, loc. Piscina Manna ed. 1, 09050 Pula, Italy;

关键词: nowcast;    meteorological radar data;    optical flow;    deep learning;   

DOI  :  10.3390/forecast2020011

来源: DOAJ

【 摘 要 】

In this article, a nowcasting technique for meteorological radar images based on a generative neural network is presented. This technique’s performance is compared with state-of-the-art optical flow procedures. Both methods have been validated using a public domain data set of radar images, covering an area of about 10 ${}^4$ km ${}^2$ over Japan, and a period of five years with a sampling frequency of five minutes. The performance of the neural network, trained with three of the five years of data, forecasts with a time horizon of up to one hour, evaluated over one year of the data, proved to be significantly better than those obtained with the techniques currently in use.

【 授权许可】

Unknown