A Novel Convective Storm Location Prediction Model Based on Machine Learning Methods
In one sentence. Machine learning models trained on temporal properties of radar convective storms predict their future locations, showing usable nowcasts with slight error.
Abstract
A weather radar is a frequently used device in remote sensing to identify meteorological phenomena using electromagnetic waves. It can observe atmospheric conditions in a wide area with a remarkably high spatiotemporal resolution, and its observation results are useful to meteorological research and services. Recent research on data analysis using radar data has concentrated on applying machine learning techniques to solve complicated problems, including quality control, quantitative precipitation estimation, and convective storm prediction. Convective storms, which consist of heavy rains and winds, are closely related to real-life and cause significant loss of life and property. This paper proposes a novel approach utilizing the given convective storms’ temporal properties based on machine learning models to predict future locations. The experimental results showed that the machine learning-based prediction models are capable of nowcasting future locations of convective storms with a slight difference.
Labels
- Topic
- Nowcasting
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 0–6 h
- Domain
- Regional
- Data
- Radar
- Status
- Research
- Identifiers
- DOI:10.3390/atmos12030343 OpenAlex:W3135377703
- Added
- 2026-10-22
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