🤖 AI Summary
This study addresses the limitations of traditional monsoon forecasting, which typically provides coarse, regionally averaged seasonal point estimates that are insufficient for fine-scale management. For the first time, Indian summer monsoon prediction is formulated as a spatiotemporal video prediction task. A deep learning model based on convolutional neural networks is developed, using multivariate atmospheric and oceanic fields from January to May as multi-channel image sequences. Leveraging ERA5 reanalysis and India Meteorological Department (IMD) observational data, the model establishes a high-resolution gridded mapping from the precursor period to the monsoon season (June–September). This approach overcomes conventional constraints by enabling high-spatial-resolution forecasts of both monthly and total seasonal rainfall, thereby supporting refined climate outlooks at both intraseasonal and seasonal timescales.
📝 Abstract
The Indian Summer Monsoon (ISM) is a critical climate phenomenon, fundamentally impacting the agriculture, economy, and water security of over a billion people. Traditional long-range forecasting, whether statistical or dynamical, has predominantly focused on predicting a single, spatially-averaged seasonal value, lacking the spatial detail essential for regional-level resource management. To address this gap, we introduce a novel deep learning framework that reframes gridded monsoon prediction as a spatio-temporal computer vision task. We treat multi-variable, pre-monsoon atmospheric and oceanic fields as a sequence of multi-channel images, effectively creating a video-like input tensor. Using 85 years of ERA5 reanalysis data for predictors and IMD rainfall data for targets, we employ a Convolutional Neural Network (CNN)-based architecture to learn the complex mapping from the five-month pre-monsoon period (January-May) to a high-resolution gridded rainfall pattern for the subsequent monsoon season. Our framework successfully produces distinct forecasts for each of the four monsoon months (June-September) as well as the total seasonal average, demonstrating its utility for both intra-seasonal and seasonal outlooks.