A convolutional LSTM model with high accuracy to predict extreme precipitation space-time fields

Precipitation forecast models based on meteorological radar data using machine learning architectures accurately predict spatio-temporal progress of precipitation. However, these data-driven forecasting models tend to underestimate magnitude of extreme precipitation events because the training of them is based on the observed precipitation data in which the normal precipitation events are included significantly more than the rare extreme events. This study proposes a ConvLSTM-based precipitation nowcasting model that can accurately predict space-time field of extreme precipitation. First, precipitation events were classified into 5 subsets using the k-means clustering algorithm based their statistical properties such as mean, standard deviation, skewness, duration, and the calendar month at which the precipitation event occurred. Then, a ConvLSTM-based neural network was trained based on the subset containing extreme precipitation events (events with large mean, variance, and duration occurred in summer months). The model was trained and tested based on the 4km-10minute resolution radar-gauge composite precipitation field of central part of South Korea (200km x 200km) for the period of 2009-2015 and 2016-2020, respectively. The NSE of the model that was trained based on the whole precipitation data was 0.55 while the one trained based on the subset of extreme precipitation was 0.78 showing a significant improvement.

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2021R1A2C2003471).