# Prediction of severe thunderstorm events with ensemble deep learning and radar data

arXiv: https://arxiv.org/abs/2109.09791 · HTML: https://mlsanity.com/paper/2109.09791/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 66 references that are in the map.
- First posted on arXiv 20 Sep 2021.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://mlsanity.com/paper/1411.4389/) (2014)
- [Skillful Precipitation Nowcasting using Deep Generative Models of Radar](https://mlsanity.com/paper/2104.00954/) (2021)
- [Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model](https://mlsanity.com/paper/1706.03458/) (2017)
- [Score-oriented loss (SOL) functions](https://mlsanity.com/paper/2103.15522/) (2021)
- [Distributed Deep Learning for Precipitation Nowcasting](https://mlsanity.com/paper/1908.10964/) (2019)
- [Bad and good errors: value-weighted skill scores in deep ensemble learning](https://mlsanity.com/paper/2103.02881/) (2021)
- [Machine learning as a flaring storm warning machine: Was a warning machine for the September 2017 solar flaring storm possible?](https://mlsanity.com/paper/2007.02425/) (2020)
- [Rank-Consistency Deep Hashing for Scalable Multi-Label Image Search](https://mlsanity.com/paper/2102.01486/) (2021)

## Built on by (most cited first, 8 of 8)

- [ClimateIQA: A New Dataset and Benchmark to Advance Vision-Language Models in Meteorology Anomalies Analysis](https://mlsanity.com/paper/2406.09838/) (2024)
- [Graph Dual-stream Convolutional Attention Fusion for Precipitation Nowcasting](https://mlsanity.com/paper/2401.07958/) (2024)
- [Operational solar flare forecasting via video-based deep learning](https://mlsanity.com/paper/2209.05128/) (2022)
- [Deep Learning Techniques in Extreme Weather Events: A Review](https://mlsanity.com/paper/2308.10995/) (2023)
- [A comprehensive theoretical framework for the optimization of neural networks classification performance with respect to weighted metrics](https://mlsanity.com/paper/2305.13472/) (2023)
- [Forecasting Geoffective Events from Solar Wind Data and Evaluating the Most Predictive Features through Machine Learning Approaches](https://mlsanity.com/paper/2403.09847/) (2024)
- [UniExtreme: A Universal Foundation Model for Extreme Weather Forecasting](https://mlsanity.com/paper/2508.01426/) (2025)
- [Weighted Score-Oriented Losses for Temporally Localized Event Prediction](https://mlsanity.com/paper/2606.23145/) (2026)

## Abstract (arXiv)

The problem of nowcasting extreme weather events can be addressed by applying either numerical methods for the solution of dynamic model equations or data-driven artificial intelligence algorithms. Within this latter framework, the present paper illustrates how a deep learning method, exploiting videos of radar reflectivity frames as input, can be used to realize a warning machine able to sound timely alarms of possible severe thunderstorm events. From a technical viewpoint, the computational core of this approach is the use of a value-weighted skill score for both transforming the probabilistic outcomes of the deep neural network into binary classification and assessing the forecasting performances. The warning machine has been validated against weather radar data recorded in the Liguria region, in Italy,