Performance Comparison between Deep Learning and Optical Flow-Based Techniques for Nowcast Precipitation from Radar Images
In one sentence. A generative neural network nowcasts radar precipitation up to one hour ahead and beats optical-flow methods on five years of Japanese radar data.
Abstract
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 104 km2 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.
Labels
- Topic
- Nowcasting
- Type
- New model or method
- Method
- GAN or VAE
- Time scale
- 0–6 h
- Domain
- Regional
- Data
- Radar
- Status
- Research
- Identifiers
- DOI:10.3390/forecast2020011 OpenAlex:W3037627778
- Added
- 2026-10-22
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