# Skillful Twelve Hour Precipitation Forecasts using Large Context Neural Networks

arXiv: https://arxiv.org/abs/2111.07470 · HTML: https://mlsanity.com/paper/2111.07470/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 14 Nov 2021.

## Builds on (most cited first, 14 of 14)

- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Skillful Precipitation Nowcasting using Deep Generative Models of Radar](https://mlsanity.com/paper/2104.00954/) (2021)
- [MetNet: A Neural Weather Model for Precipitation Forecasting](https://mlsanity.com/paper/2003.12140/) (2020)
- [GSPMD: General and Scalable Parallelization for ML Computation Graphs](https://mlsanity.com/paper/2105.04663/) (2021)
- [Neural networks for post-processing ensemble weather forecasts](https://mlsanity.com/paper/1805.09091/) (2018)
- [SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet Architecture](https://mlsanity.com/paper/2007.04417/) (2020)
- [Neural Machine Translation in Linear Time](https://mlsanity.com/paper/1610.10099/) (2016)
- [Machine Learning for Precipitation Nowcasting from Radar Images](https://mlsanity.com/paper/1912.12132/) (2019)
- [A review of radar-based nowcasting of precipitation and applicable machine learning techniques](https://mlsanity.com/paper/2005.04988/) (2020)
- [Wind speed prediction using multidimensional convolutional neural networks](https://mlsanity.com/paper/2007.12567/) (2020)
- [PlumeNet: Large-Scale Air Quality Forecasting Using A Convolutional LSTM Network](https://mlsanity.com/paper/2006.09204/) (2020)

## Built on by (most cited first, 20 of 20)

- [Forecasting Global Weather with Graph Neural Networks](https://mlsanity.com/paper/2202.07575/) (2022)
- [Earthformer: Exploring Space-Time Transformers for Earth System Forecasting](https://mlsanity.com/paper/2207.05833/) (2022)
- [PreDiff: Precipitation Nowcasting with Latent Diffusion Models](https://mlsanity.com/paper/2307.10422/) (2023)
- [A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts](https://mlsanity.com/paper/2204.02028/) (2022)
- [SimPer: Simple Self-Supervised Learning of Periodic Targets](https://mlsanity.com/paper/2210.03115/) (2022)
- [Robust Hybrid Learning With Expert Augmentation](https://mlsanity.com/paper/2202.03881/) (2022)
- [WeatherBench Probability: A benchmark dataset for probabilistic medium-range weather forecasting along with deep learning baseline models](https://mlsanity.com/paper/2205.00865/) (2022)
- [Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes](https://mlsanity.com/paper/2203.17070/) (2022)
- [Spatial Mixture-of-Experts](https://mlsanity.com/paper/2211.13491/) (2022)
- [Global Extreme Heat Forecasting Using Neural Weather Models](https://mlsanity.com/paper/2205.10972/) (2022)
- [ENS-10: A Dataset For Post-Processing Ensemble Weather Forecasts](https://mlsanity.com/paper/2206.14786/) (2022)
- [Benchmark Dataset for Precipitation Forecasting by Post-Processing the Numerical Weather Prediction](https://mlsanity.com/paper/2206.15241/) (2022)
- [Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data](https://mlsanity.com/paper/2202.04964/) (2022)
- [Deep-Learning-Based Precipitation Nowcasting with Ground Weather Station Data and Radar Data](https://mlsanity.com/paper/2210.12853/) (2022)
- [Inductive biases in deep learning models for weather prediction](https://mlsanity.com/paper/2304.04664/) (2023)
- [Evidential Deep Learning for Probabilistic Modelling of Extreme Storm Events](https://mlsanity.com/paper/2412.14048/) (2024)
- [RainAI -- Precipitation Nowcasting from Satellite Data](https://mlsanity.com/paper/2311.18398/) (2023)
- [Tools for Extracting Spatio-Temporal Patterns in Meteorological Image Sequences: From Feature Engineering to Attention-Based Neural Networks](https://mlsanity.com/paper/2210.12310/) (2022)
- [Multi-Source Temporal Attention Network for Precipitation Nowcasting](https://mlsanity.com/paper/2410.08641/) (2024)
- [TSSM: Triaxial State Space Model for Global Station Weather Forecasting with Temporal-Variable-Historical Modeling](https://mlsanity.com/paper/2607.13101/) (2026)

## Abstract (arXiv)

The problem of forecasting weather has been scientifically studied for centuries due to its high impact on human lives, transportation, food production and energy management, among others. Current operational forecasting models are based on physics and use supercomputers to simulate the atmosphere to make forecasts hours and days in advance. Better physics-based forecasts require improvements in the models themselves, which can be a substantial scientific challenge, as well as improvements in the underlying resolution, which can be computationally prohibitive. An emerging class of weather models based on neural networks represents a paradigm shift in weather forecasting: the models learn the required transformations from data instead of relying on hand-coded physics and are computationally efficient. For neural models, however, each additional hour of lead time poses a substantial challenge as it requires capturing ever larger spatial contexts and increases the uncertainty of the prediction. In this work, we present a neural network that is capable of large-scale precipitation forecasting up to twelve hours ahead and, starting from the same atmospheric state, the model achieves greater skill than the state-of-the-art physics-based models HRRR and HREF that currently operate in the Continental United States. Interpretability analyses reinforce the observation that the model learns to emulate advanced physics principles. These results represent a substantial step towards establishing a new paradigm of efficient forecasting with neural networks.