# A review of radar-based nowcasting of precipitation and applicable machine learning techniques

arXiv: https://arxiv.org/abs/2005.04988 · HTML: https://mlsanity.com/paper/2005.04988/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2020.

## Built on by (most cited first, 19 of 19)

- [Skillful Precipitation Nowcasting using Deep Generative Models of Radar](https://mlsanity.com/paper/2104.00954/) (2021)
- [ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling](https://mlsanity.com/paper/2307.01909/) (2023)
- [AI Foundation Models for Weather and Climate: Applications, Design, and Implementation](https://mlsanity.com/paper/2309.10808/) (2023)
- [Skillful Twelve Hour Precipitation Forecasts using Large Context Neural Networks](https://mlsanity.com/paper/2111.07470/) (2021)
- [Deep learning for precipitation nowcasting: A survey from the perspective of time series forecasting](https://mlsanity.com/paper/2406.04867/) (2024)
- [FlowCast: Advancing Precipitation Nowcasting with Conditional Flow Matching](https://mlsanity.com/paper/2511.09731/) (2025)
- [Precipitation Nowcasting Using Physics Informed Discriminator Generative Models](https://mlsanity.com/paper/2406.10108/) (2024)
- [Extreme Precipitation Nowcasting using Transformer-based Generative Models](https://mlsanity.com/paper/2403.03929/) (2024)
- [BlockGPT: Spatio-Temporal Modelling of Rainfall via Frame-Level Autoregression](https://mlsanity.com/paper/2510.06293/) (2025)
- [Deep Learning for Rain Fade Prediction in Satellite Communications](https://mlsanity.com/paper/2110.00695/) (2021)
- [Improving deep learning precipitation nowcasting by using prior knowledge](https://mlsanity.com/paper/2301.11707/) (2023)
- [Analogue Forecast System for Daily Precipitation Prediction Using Autoencoder Feature Extraction: Application in Hong Kong](https://mlsanity.com/paper/2501.02814/) (2025)
- [Physical Scales Matter: The Role of Receptive Fields and Advection in Satellite-Based Thunderstorm Nowcasting with Convolutional Neural Networks](https://mlsanity.com/paper/2504.09994/) (2025)
- [FuXi-Nowcast: Environment-conditioned deep learning for severe convection nowcasting](https://mlsanity.com/paper/2512.08974/) (2025)
- [REE-TTT: Highly Adaptive Radar Echo Extrapolation Based on Test-Time Training](https://mlsanity.com/paper/2601.01605/) (2026)
- [Accurate Precipitation Forecast by Efficiently Learning from Massive Atmospheric Variables and Unbalanced Distribution](https://mlsanity.com/paper/2603.26108/) (2026)
- [Temporal Context Conditioning for Seasonality-Aware Precipitation Nowcasting of High-Intensity Rainfall](https://mlsanity.com/paper/2606.09959/) (2026)
- [QWRF-Net: A Quantum-Wavelet Framework with Rectified Flow for Short-Term Precipitation Nowcasting](https://mlsanity.com/paper/2608.01626/) (2026)
- [From Nowcasting to Forecasting: Adapting a Reanalysis-Trained](https://mlsanity.com/paper/2609.03763/) (2026)

## Abstract (arXiv)

A 'nowcast' is a type of weather forecast which makes predictions in the very short term, typically less than two hours - a period in which traditional numerical weather prediction can be limited. This type of weather prediction has important applications for commercial aviation; public and outdoor events; and the construction industry, power utilities, and ground transportation services that conduct much of their work outdoors. Importantly, one of the key needs for nowcasting systems is in the provision of accurate warnings of adverse weather events, such as heavy rain and flooding, for the protection of life and property in such situations. Typical nowcasting approaches are based on simple extrapolation models applied to observations, primarily rainfall radar. In this paper we review existing techniques to radar-based nowcasting from environmental sciences, as well as the statistical approaches that are applicable from the field of machine learning. Nowcasting continues to be an important component of operational systems and we believe new advances are possible with new partnerships between the environmental science and machine learning communities.