Multimodal LSTM Weather Forecasting: An Intelligent Prediction System Fusing Image and Numerical Data

Project Basic Information

• Original Author/Maintainer: Kri311
• Source Platform: GitHub
• Original Title: Multimodal-Weather-Forecasting-LSTM
• Original Link: https://github.com/Kri311/Multimodal-Weather-Forecasting-LSTM
• Release Time: 2026-06-12

Core Points

This project fuses satellite images and numerical weather data, using the LSTM deep learning model to achieve accurate time-series weather forecasting, providing a multimodal intelligent solution for weather prediction.

Accurate weather forecasting is crucial for agriculture, aviation, disaster early warning, and other fields. However, the weather system is a complex nonlinear dynamic system affected by multiple factors. Traditional Numerical Weather Prediction (NWP) has limitations in computational efficiency and extreme weather prediction, while machine learning—especially deep learning—brings new possibilities to weather forecasting.

Multimodal Data Fusion Idea

Fuse numerical data (discrete values such as temperature and air pressure) with image data (satellite cloud images, radar echo maps). Numerical data provides precise measurements, while image data contains spatial pattern information—they complement each other.

LSTM Model Application

LSTM captures long-term dependencies in time series through its memory mechanism, and its gating mechanism selectively retains or forgets information, making it suitable for weather time-series modeling.

Image Processing Architecture

Use CNN to extract high-level visual features (cloud distribution, precipitation patterns, etc.) from satellite cloud images and radar images, then convert them into numerical-compatible representations for input to the LSTM.

Numerical Feature Engineering

Process elements like surface temperature and humidity, design time-series windows and feature combinations, and learn physical laws to improve prediction consistency.

Fusion Strategy

Explore early (input layer concatenation), late (weighted combination), and middle (hidden layer interaction) fusion. An appropriate strategy can significantly improve accuracy.

The project was validated on real datasets with metrics including RMSE, MAE, and extreme weather detection accuracy. Results show: Multimodal LSTM performs excellently in short/medium-term forecasting, has potential in extreme weather early warning, can complement traditional NWP, and provides a faster and more flexible prediction solution.

Application Prospects

• Agriculture: Precise precipitation forecasting guides irrigation;
• Energy: Wind speed/sunshine prediction aids renewable energy scheduling;
• Transportation: Severe weather warning ensures safety.

Expansion Directions

Introduce more data sources (social media, IoT sensors), explore advanced models like Transformer and GNN, and build a more intelligent weather system.