Enhanced Precision in Rainfall Forecasting for Mumbai: Utilizing Physics
  Informed ConvLSTM2D Models for Finer Spatial and Temporal Resolution
Ajay Devda Akshay Sunil Murthy R B Deepthi
Forecasting rainfall in tropical areas is challenging due to complex
atmospheric behaviour, elevated humidity levels, and the common presence of
convective rain events. In the Indian context, the difficulty is further
exacerbated because of the monsoon intra seasonal oscillations, which introduce
significant variability in rainfall patterns over short periods. Earlier
investigations into rainfall prediction leveraged numerical weather prediction
methods, along with statistical and deep learning approaches. This study
introduces deep learning spatial model aimed at enhancing rainfall prediction
accuracy on a finer scale. In this study, we hypothesize that integrating
physical understanding improves the precipitation prediction skill of deep
learning models with high precision for finer spatial scales, such as cities.
To test this hypothesis, we introduce a physics informed ConvLSTM2D model to
predict precipitation 6hr and 12hr ahead for Mumbai, India. We utilize ERA5
reanalysis data select predictor variables, across various geopotential levels.
The ConvLSTM2D model was trained on the target variable precipitation for 4
different grids representing different spatial grid locations of Mumbai. Thus,
the use of the ConvLSTM2D model for rainfall prediction, utilizing physics
informed data from specific grids with limited spatial information, reflects
current advancements in meteorological research that emphasize both efficiency
and localized precision.