Enhancing Hyperlocal 3-Hourly Rainfall Forecasting for Mumbai Using a Hybrid CNN-LSTM Model.

The increasing frequency and severity of extreme weather events, such as heavy rainfall and flooding, emphasize the urgent need for advanced early warning systems. Short-duration rainfall extremes, exacerbated by climate change, significantly increase flood risks, particularly in urban coastal cities like Mumbai. Mumbai's vulnerability arises from rapid urbanization, its coastal location, and variable topography, which contribute to significant spatial variability in rainfall. We have used Global Forecast System (GFS) data to identify key predictors for high-resolution, 3-hour rainfall forecasts for Mumbai. The GFS variables were selected using a correlation matrix. We have used past 3-hour observed rainfall data from Automatic Weather Stations (AWS) across 15 locations in Mumbai (2015–2023) along with selected GFS variables, which include Precipitable Water, Precipitation Rate, Relative Humidity, and Total Cloud Cover, to forecast rainfall for one timestep ahead. The dataset was divided into 80% for training and 20% for testing. We employed a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model to enhance forecast accuracy. The CNN captures spatial features, while the LSTM models temporal dependencies, effectively addressing the challenges of hyperlocal rainfall forecasting. Further, we incorporated a weighted Mean Squared Error (MSE) loss function to prioritize extreme rainfall events (≥95^th percentile). The results indicate that using CNN-LSTM models reduced the Root Mean Square Error (RMSE) by 9.41% -12.38% and increased the Correlation Coefficient (CC) by 70.4%-113% compared to GFS models. At the 95^th percentile, the Hit Rate (HR) improved by 233% -483.3%, while the False Alarm Rate (FAR) decreased by 7%-16.2%. Using weighted MSE also enhanced performance, increasing the HR by 255.5%-583.3% at the 95^th percentile and reducing the FAR by 7% -13.2%. Implementing weighted MSE as a loss function resulted in a reduction in RMSE by 9.94% -12.86% and an increase in CC by 85.2%-126%. This study highlights that the hybrid CNN-LSTM model, combined with a weighted MSE loss function, demonstrates superior capability in accurately forecasting 3-hourly extreme rainfall events in Mumbai, providing critical advancements for early warning systems and flood risk mitigation.