A Spatio-Temporal Deep Learning Framework for Integrating NWP Products to Improve Short-Range Monsoon Rainfall Forecasts over India

Under changing climate conditions, it has been observed that the frequency of extreme events has increased significantly worldwide. India has also experienced numerous flash flood events over the past two decades, leading to substantial socio-economic losses. India receives 70-80% of its annual rainfall during the southwest monsoon, which affects almost all parts of the country except the southeastern coast of Tamil Nadu. Therefore, it is crucial to improve early warning systems, especially for short-term precipitation forecasts. Various national and international organizations publish forecasts for different weather parameters, such as precipitation, temperature, wind speed, etc., derived from Numerical Weather Prediction Models (NWP); these datasets often show significant spatial and temporal biases at different lead times. In this study, the goal has been to identify the spatial and temporal biases in forecast data from NCMRWF, ECMWF, and NCEP for the years 2018 to 2023, using IMD gridded rainfall data and CMORPH-NOAA satellite data as ground truth for the southwest monsoon (June to September). For each grid, the spatial correlation has been evaluated across eight neighbouring grids and the central grid, while temporal cross-correlation has been assessed over 12-hour, 24-hour, and 48-hour lead and lag periods to determine the temporal accuracy of each NWP product for 24-hour lead times, using 00:00 UTC as the reference for both ground truth accumulation and forecasts.

This study introduces a spatio-temporal deep learning–based integration framework that combines three separate NWP rainfall forecasts into a single, skill-enhanced 24-hour prediction by explicitly considering directional spatial dependence and temporal lead–lag relationships, with particular relevance for extreme rainfall detection during the monsoon season. The methodology employs a spatio-temporal deep learning framework in which three NWP precipitation forecasts are encoded separately using direction-aware neighbourhood information and lag–lead temporal context, allowing the model to learn model-specific spatial and temporal error characteristics. These encoded features are dynamically combined through an attention-based integration mechanism to produce an optimized 24-hour rainfall forecast. The combined forecast is evaluated solely at a 24-hour lead time during the South-West Monsoon season using high-resolution rainfall observations. Results indicate that the proposed directional–temporal integration consistently outperforms all individual NWP forecasts, showing significant improvements (20-50% across various parts) in various standard error metrics, including RMSE and correlation coefficient values.

 The study is expected to effectively reduce the local bias in short-term rainfall forecasts over India, ultimately leading to the development of more efficient weather forecasting technologies. Additionally, the future scope of the study aims to introduce a novel approach that combines both physics-based and AI-based predictions, with the goal of establishing a benchmark for improving India's weather forecast system.