# Extreme Precipitation Nowcasting using Transformer-based Generative Models

arXiv: https://arxiv.org/abs/2403.03929 · HTML: https://mlsanity.com/paper/2403.03929/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 6 Mar 2024.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [MaskGIT: Masked Generative Image Transformer](https://mlsanity.com/paper/2202.04200/) (2022)
- [VideoGPT: Video Generation using VQ-VAE and Transformers](https://mlsanity.com/paper/2104.10157/) (2021)
- [Skillful Precipitation Nowcasting using Deep Generative Models of Radar](https://mlsanity.com/paper/2104.00954/) (2021)
- [N"UWA: Visual Synthesis Pre-training for Neural visUal World creAtion](https://mlsanity.com/paper/2111.12417/) (2021)
- [SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet Architecture](https://mlsanity.com/paper/2007.04417/) (2020)
- [Temporally Consistent Transformers for Video Generation](https://mlsanity.com/paper/2210.02396/) (2022)
- [AA-TransUNet: Attention Augmented TransUNet For Nowcasting Tasks](https://mlsanity.com/paper/2202.04996/) (2022)
- [A review of radar-based nowcasting of precipitation and applicable machine learning techniques](https://mlsanity.com/paper/2005.04988/) (2020)
- [MS-nowcasting: Operational Precipitation Nowcasting with Convolutional LSTMs at Microsoft Weather](https://mlsanity.com/paper/2111.09954/) (2021)

## Built on by (most cited first, 6 of 6)

- [Deep Learning and Foundation Models for Weather Prediction: A Survey](https://mlsanity.com/paper/2501.06907/) (2025)
- [Precipitation Nowcasting Using Physics Informed Discriminator Generative Models](https://mlsanity.com/paper/2406.10108/) (2024)
- [Masked Generative Priors Improve World Models Sequence Modelling Capabilities](https://mlsanity.com/paper/2410.07836/) (2024)
- [BlockGPT: Spatio-Temporal Modelling of Rainfall via Frame-Level Autoregression](https://mlsanity.com/paper/2510.06293/) (2025)
- [PixelFlowCast: Latent-Free Precipitation Nowcasting via Pixel Mean Flows](https://mlsanity.com/paper/2605.10046/) (2026)
- [Machine learning in an expectation-maximisation framework for nowcasting](https://mlsanity.com/paper/2512.07335/) (2025)

## Abstract (arXiv)

This paper presents an innovative approach to extreme precipitation nowcasting by employing Transformer-based generative models, namely NowcastingGPT with Extreme Value Loss (EVL) regularization. Leveraging a comprehensive dataset from the Royal Netherlands Meteorological Institute (KNMI), our study focuses on predicting short-term precipitation with high accuracy. We introduce a novel method for computing EVL without assuming fixed extreme representations, addressing the limitations of current models in capturing extreme weather events. We present both qualitative and quantitative analyses, demonstrating the superior performance of the proposed NowcastingGPT-EVL in generating accurate precipitation forecasts, especially when dealing with extreme precipitation events. The code is available at urlhttps://github.com/Cmeo97/NowcastingGPT.