Utilizing deep learning for near real-time rainfall forecasting based on Radar data

Abstract

Accurate near real time precipitation forecasting has several benefits, including water resource management, dam discharge and flash flood management. In this regard, deep-learning offers good value in precipitation now-casting, particularly when supplemented with reliable observational records, e.g. Radar images. This study employs deep learning (DL) models for precipitation now-casting utilizing Radar precipitation data over Bhopal city located in central India and tests its efficacy during the monsoon (JJAS) 2021 season, with a 20-min temporal resolution. Out of the three methods tested for forecasting, the DL model ConvLSTM outperforms ConvGRU model, and persistence baseline method, in terms of spatial and temporal correlation, skill score, and RMSE, and is thus chosen for further investigations. The ConvLSTM model provides an accuracy of up to 75% for the 1st lead time step forecast and gradually decreases for further time steps going down to approximately 35% at the 5th lead time step forecast. Moreover, while comparing directly from ground truth, the model is able to capture the temporal (sequential) linkage in data. The findings show that deep-learning-based models have the potential to improve precipitation now-casting.