Deployment of 3D-Conv-LSTM for Precipitation Nowcast via Satellite Data
Vedanti Patel, Sheshang Degadwala
Abstract
The utilization of 3D-Convolutional Long Short-Term Memory (3D-Conv-LSTM) networks for precipitation nowcasting through satellite data integration has emerged as a significant advancement in meteorological forecasting. This approach harnesses the power of 3D convolutions to extract spatiotemporal features, enabling the model to capture intricate patterns and dynamics in precipitation systems. By leveraging satellite data, which provides a comprehensive view of atmospheric conditions, the model gains valuable insights crucial for accurate and timely weather predictions. The experimental evaluation of this methodology has demonstrated notable improvements in nowcasting accuracy compared to conventional methods. The incorporation of 3D convolutions allows the model to consider both spatial and temporal dependencies, leading to enhanced predictive performance. These advancements have far-reaching implications across various sectors, including agriculture, disaster management, and renewable energy, where precise precipitation forecasts are essential for informed decision-making processes.