Very Short-Term Rainfall Prediction Using Ground Radar Observations and Conditional Generative Adversarial Networks

Weather radars play an important role in in situ rainfall monitoring owing to their ability to measure instantaneous rain rates and rainfall distributions. Currently, the Korea Meteorological Administration (KMA) provides instantaneous radar observation data and predictions based on the McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE) for up to 6 h, for short-term forecasting. This study presents a conditional generative adversarial network (CGAN)-based radar rainfall prediction method for very short-range weather forecasts from 10 min to 4 h. The CGAN-predicted model was trained and tested using KMA’s constant altitude plan position indicator (CAPPI) observation data. The qualitative comparison between the radar observation and the CGAN-predicted rain rates displayed high statistical scores, such as the probability of detection (POD) = 0.8442, false alarm ratio (FAR) = 0.2913, and critical success index (CSI) = 0.6268, in the case of a 1-h prediction for rainfall on September 5, 2019, 15:20 KST. This study demonstrates the capability of the CGAN model for short-term rainfall forecasting. Consequently, the CGAN-generated radar-based rainfall prediction could complement the KMA MAPLE system and be useful in various forecasting applications.

I. Introduction

Weather radars are very useful for estimating rainfall amounts because they can measure instantaneous rain rates and their distribution at fine spatial and temporal resolutions compared with rain gauges [1]. Typical operating temporal and spatial resolutions of weather radars are 1–5 min and 100 m–1 km for the X-band, 5–10 min and 250 m–2 km for the C-band, 10–15 min and 1–4 km for the S-band, respectively [2].