Comparison of data-driven and numerical weather prediction models for rainfall nowcasting in major cities of Bangladesh

Flood-prone Bangladesh requires accurate short-term rainfall prediction, yet conventional numerical weather prediction (NWP) models remain computationally intensive and regionally limited in skill. This study evaluates and compares four deep learning (DL) architectures — Simple Long Short-Term Memory (LSTM), Stacked LSTM, Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU) — against the physics-based Weather Research and Forecasting (WRF) model for one-hour-ahead rainfall forecasting across six climatologically diverse stations in Bangladesh. Each model was trained independently per station using hourly ERA5 reanalysis data spanning 2018–2024, and performance was assessed using both regression and binary detection metrics. Across the full test period, Simple LSTM achieved the strongest overall performance, explaining approximately 88% of ERA5 rainfall variance with the lowest prediction errors, while GRU produced nearly comparable results. Performance was consistently stronger at wetter stations such as Chittagong and Sylhet than at drier northwestern stations such as Rajshahi and Rangpur, where class imbalance and rainfall intermittency limited model skill. Event-category analysis across 10 WRF simulation events — spanning extreme, moderate, and weak rainfall regimes — showed that DL models demonstrated closest agreement with ERA5 reference values during extreme events, while skill declined notably during weak events due to near-zero rainfall conditions. WRF exhibited systematic overestimation, temporal displacement of peak rainfall, and strongly negative R² values across all event categories and stations. A Friedman test confirmed statistically significant differences in RMSE ranks among the four DL architectures (χ² = 10.60, p = 0.014), with a Nemenyi post-hoc test showing Simple LSTM significantly outperformed Stacked LSTM (p = 0.0095).