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HoST: integrating Heuristic knowledge with attention-based LSTM networks for Thunderstorm prediction
Kalyan Chatterjee1, Mudassir Khan2, Bhoomeshwar Bala1
1Computer Science & Engineering, Nalla Malla Reddy Engineering College, Hyderabad, Telangana, 500088, India.
Scientific Reports
|July 7, 2026
Summary
This study introduces HoST, a novel framework for thunderstorm prediction. It combines heuristic knowledge with AI to improve forecasting accuracy and reliability for severe weather events.
Area of Science:
- Meteorology and Atmospheric Science
- Artificial Intelligence in Weather Forecasting
- Computational Science
Background:
- Accurate severe convective event forecasting is crucial for meteorology and early warning systems.
- Existing models often struggle with the complex nonlinear dynamics of atmospheric patterns.
- There is a need for computationally efficient and meteorologically consistent prediction frameworks.
Purpose of the Study:
- To develop a probabilistic thunderstorm prediction framework called HoST.
- To integrate heuristic knowledge with attention-based LSTM networks for enhanced meteorological consistency.
- To evaluate the framework's performance in real-world and synthetic scenarios.
Main Methods:
- Developed HoST, integrating attention-enhanced recurrent modeling with heuristic constraints.
- Utilized a real-world observational dataset for model evaluation.
- Conducted controlled experiments with synthetic atmospheric scenarios.
Main Results:
- HoST demonstrated strong predictive capability in capturing spatiotemporal convective patterns.
- The framework exhibited low-latency inference and computational efficiency in quasi-operational settings.
- HoST outperformed several established models including Random Forest, SVM, MetNet, FourCastNet, GraphCast, HRRR, and AROME.
Conclusions:
- Integrating heuristic knowledge with data-driven learning is effective for thunderstorm prediction.
- HoST shows potential as a physically consistent and operationally viable framework for short-term forecasting.
- The approach offers improved classification stability and probabilistic calibration.
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