# MS-nowcasting: Operational Precipitation Nowcasting with Convolutional LSTMs at Microsoft Weather

arXiv: https://arxiv.org/abs/2111.09954 · HTML: https://mlsanity.com/paper/2111.09954/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 18 references that are in the map.
- First posted on arXiv 18 Nov 2021.

## Builds on (most cited first, 9 of 9)

- [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)
- [Averaging Weights Leads to Wider Optima and Better Generalization](https://mlsanity.com/paper/1803.05407/) (2018)
- [Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs](https://mlsanity.com/paper/1802.10026/) (2018)
- [Axial Attention in Multidimensional Transformers](https://mlsanity.com/paper/1912.12180/) (2019)
- [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)
- [MetNet: A Neural Weather Model for Precipitation Forecasting](https://mlsanity.com/paper/2003.12140/) (2020)
- [Precipitation Nowcasting with Satellite Imagery](https://mlsanity.com/paper/1905.09932/) (2019)

## Built on by (most cited first, 12 of 12)

- [Forecasting Global Weather with Graph Neural Networks](https://mlsanity.com/paper/2202.07575/) (2022)
- [A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts](https://mlsanity.com/paper/2204.02028/) (2022)
- [DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies](https://mlsanity.com/paper/2310.04610/) (2023)
- [TempEE: Temporal-Spatial Parallel Transformer for Radar Echo Extrapolation Beyond Auto-Regression](https://mlsanity.com/paper/2304.14131/) (2023)
- [Domain Generalization Strategy to Train Classifiers Robust to Spatial-Temporal Shift](https://mlsanity.com/paper/2212.02968/) (2022)
- [Extreme Precipitation Nowcasting using Transformer-based Generative Models](https://mlsanity.com/paper/2403.03929/) (2024)
- [Generative Modeling of High-resolution Global Precipitation Forecasts](https://mlsanity.com/paper/2210.12504/) (2022)
- [RainUNet for Super-Resolution Rain Movie Prediction under Spatio-temporal Shifts](https://mlsanity.com/paper/2212.04005/) (2022)
- [Inductive biases in deep learning models for weather prediction](https://mlsanity.com/paper/2304.04664/) (2023)
- [Learning Robust Precipitation Forecaster by Temporal Frame Interpolation](https://mlsanity.com/paper/2311.18341/) (2023)
- [An ensemble of data-driven weather prediction models for operational sub-seasonal forecasting](https://mlsanity.com/paper/2403.15598/) (2024)
- [BlockGPT: Spatio-Temporal Modelling of Rainfall via Frame-Level Autoregression](https://mlsanity.com/paper/2510.06293/) (2025)

## Abstract (arXiv)

We present the encoder-forecaster convolutional long short-term memory (LSTM) deep-learning model that powers Microsoft Weather's operational precipitation nowcasting product. This model takes as input a sequence of weather radar mosaics and deterministically predicts future radar reflectivity at lead times up to 6 hours. By stacking a large input receptive field along the feature dimension and conditioning the model's forecaster with predictions from the physics-based High Resolution Rapid Refresh (HRRR) model, we are able to outperform optical flow and HRRR baselines by 20-25% on multiple metrics averaged over all lead times.