# Machine Learning for Precipitation Nowcasting from Radar Images

arXiv: https://arxiv.org/abs/1912.12132 · HTML: https://mlsanity.com/paper/1912.12132/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2019.

## Built on by (most cited first, 30 of 56)

- [Skillful Precipitation Nowcasting using Deep Generative Models of Radar](https://mlsanity.com/paper/2104.00954/) (2021)
- [Forecasting Global Weather with Graph Neural Networks](https://mlsanity.com/paper/2202.07575/) (2022)
- [Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast](https://mlsanity.com/paper/2211.02556/) (2022)
- [A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts](https://mlsanity.com/paper/2204.02028/) (2022)
- [Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification](https://mlsanity.com/paper/2304.12891/) (2023)
- [Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science](https://mlsanity.com/paper/2303.00652/) (2023)
- [Broad-UNet: Multi-scale feature learning for nowcasting tasks](https://mlsanity.com/paper/2102.06442/) (2021)
- [Conformal Contextual Robust Optimization](https://mlsanity.com/paper/2310.10003/) (2023)
- [Generative Diffusion-based Downscaling for Climate](https://mlsanity.com/paper/2404.17752/) (2024)
- [Precipitation nowcasting with generative diffusion models](https://mlsanity.com/paper/2308.06733/) (2023)
- [AA-TransUNet: Attention Augmented TransUNet For Nowcasting Tasks](https://mlsanity.com/paper/2202.04996/) (2022)
- [Scaling Spherical CNNs](https://mlsanity.com/paper/2306.05420/) (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)
- [Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes](https://mlsanity.com/paper/2203.17070/) (2022)
- [Deep Probability Estimation](https://mlsanity.com/paper/2111.10734/) (2021)
- [Spatial Mixture-of-Experts](https://mlsanity.com/paper/2211.13491/) (2022)
- [TempEE: Temporal-Spatial Parallel Transformer for Radar Echo Extrapolation Beyond Auto-Regression](https://mlsanity.com/paper/2304.14131/) (2023)
- [Effective Training Strategies for Deep-learning-based Precipitation Nowcasting and Estimation](https://mlsanity.com/paper/2202.10555/) (2022)
- [DiffESM: Conditional Emulation of Earth System Models with Diffusion Models](https://mlsanity.com/paper/2304.11699/) (2023)
- [GPTCast: a weather language model for precipitation nowcasting](https://mlsanity.com/paper/2407.02089/) (2024)
- [Graph Dual-stream Convolutional Attention Fusion for Precipitation Nowcasting](https://mlsanity.com/paper/2401.07958/) (2024)
- [Enhanced Route Planning with Calibrated Uncertainty Set](https://mlsanity.com/paper/2503.10088/) (2025)
- [Interpretable Machine Learning for Weather and Climate Prediction: A Survey](https://mlsanity.com/paper/2403.18864/) (2024)
- [Personalized Federated Learning for improving radar based precipitation nowcasting on heterogeneous areas](https://mlsanity.com/paper/2408.05761/) (2024)
- [Observation-guided Interpolation Using Graph Neural Networks for High-Resolution Nowcasting in Switzerland](https://mlsanity.com/paper/2509.00017/) (2025)
- [Numerical Weather Forecasting using Convolutional-LSTM with Attention and Context Matcher Mechanisms](https://mlsanity.com/paper/2102.00696/) (2021)
- [Multi Scale Graph Wavenet for Wind Speed Forecasting](https://mlsanity.com/paper/2109.15239/) (2021)
- [Short-term precipitation prediction using deep learning](https://mlsanity.com/paper/2110.01843/) (2021)
- [Deep-Learning-Based Precipitation Nowcasting with Ground Weather Station Data and Radar Data](https://mlsanity.com/paper/2210.12853/) (2022)

## Abstract (arXiv)

High-resolution nowcasting is an essential tool needed for effective adaptation to climate change, particularly for extreme weather. As Deep Learning (DL) techniques have shown dramatic promise in many domains, including the geosciences, we present an application of DL to the problem of precipitation nowcasting, i.e., high-resolution (1 km x 1 km) short-term (1 hour) predictions of precipitation. We treat forecasting as an image-to-image translation problem and leverage the power of the ubiquitous UNET convolutional neural network. We find this performs favorably when compared to three commonly used models: optical flow, persistence and NOAA's numerical one-hour HRRR nowcasting prediction.