# Kilometer-Scale Convection Allowing Model Emulation using Generative Diffusion Modeling

arXiv: https://arxiv.org/abs/2408.10958 · HTML: https://mlsanity.com/paper/2408.10958/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Placed in Flow matching, time series and science, one of 14 directions in Diffusion models.
- Builds on 25 of its 90 references that are in the map.
- First posted on arXiv 20 Aug 2024.

## Placed in

- Diffusion models / [Flow matching, time series and science](https://mlsanity.com/diffusion/flow-matching-time-series-and-science/)

## Builds on (most cited first, 25 of 25)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022)
- [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022)
- [FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators](https://mlsanity.com/paper/2202.11214/) (2022)
- [Skillful Precipitation Nowcasting using Deep Generative Models of Radar](https://mlsanity.com/paper/2104.00954/) (2021)
- [Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere](https://mlsanity.com/paper/2306.03838/) (2023)
- [GenCast: Diffusion-based ensemble forecasting for medium-range weather](https://mlsanity.com/paper/2312.15796/) (2023)
- [MetNet: A Neural Weather Model for Precipitation Forecasting](https://mlsanity.com/paper/2003.12140/) (2020)
- [Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling](https://mlsanity.com/paper/2309.15214/) (2023)
- [Deep Learning for Day Forecasts from Sparse Observations](https://mlsanity.com/paper/2306.06079/) (2023)
- [Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network](https://mlsanity.com/paper/2005.10374/) (2020)
- [DiffDA: a Diffusion Model for Weather-scale Data Assimilation](https://mlsanity.com/paper/2401.05932/) (2024)
- [Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification](https://mlsanity.com/paper/2304.12891/) (2023)
- [Deep Generative Data Assimilation in Multimodal Setting](https://mlsanity.com/paper/2404.06665/) (2024)
- [Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales](https://mlsanity.com/paper/2406.16947/) (2024)
- [Graph-based Neural Weather Prediction for Limited Area Modeling](https://mlsanity.com/paper/2309.17370/) (2023)
- [Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operators](https://mlsanity.com/paper/2408.03100/) (2024)
- [Machine learning emulation of precipitation from km-scale UK regional climate simulations using a diffusion model](https://mlsanity.com/paper/2407.14158/) (2024)
- [Huge Ensembles Part II: Properties of a Huge Ensemble of Hindcasts Generated with Spherical Fourier Neural Operators](https://mlsanity.com/paper/2408.01581/) (2024)
- [CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences -- Version 1](https://mlsanity.com/paper/2106.09757/) (2021)

## Built on by (most cited first, 30 of 32)

- [Heavy-Tailed Diffusion Models](https://mlsanity.com/paper/2410.14171/) (2024)
- [Continuous Ensemble Weather Forecasting with Diffusion models](https://mlsanity.com/paper/2410.05431/) (2024)
- [Elucidated Rolling Diffusion Models for Probabilistic Forecasting of Complex Dynamics](https://mlsanity.com/paper/2506.20024/) (2025)
- [Elucidating the Design Choice of Probability Paths in Flow Matching for Forecasting](https://mlsanity.com/paper/2410.03229/) (2024)
- [Diffusion-LAM: Probabilistic Limited Area Weather Forecasting with Diffusion](https://mlsanity.com/paper/2502.07532/) (2025)
- [Local Off-Grid Weather Forecasting with Multi-Modal Earth Observation Data](https://mlsanity.com/paper/2410.12938/) (2024)
- [Dynamical Diffusion: Learning Temporal Dynamics with Diffusion Models](https://mlsanity.com/paper/2503.00951/) (2025)
- [FLEX: A Backbone for Diffusion-Based Modeling of Spatio-temporal Physical Systems](https://mlsanity.com/paper/2505.17351/) (2025)
- [HRRRCast: a data-driven emulator for regional weather forecasting at convection allowing scales](https://mlsanity.com/paper/2507.05658/) (2025)
- [Universal Diffusion-Based Probabilistic Downscaling](https://mlsanity.com/paper/2602.11893/) (2026)
- [How to use score-based diffusion in earth system science: A satellite nowcasting example](https://mlsanity.com/paper/2505.10432/) (2025)
- [Constructing Extreme Heatwave Storylines with Differentiable Climate Models](https://mlsanity.com/paper/2506.10660/) (2025)
- [Towards accurate extreme event likelihoods from diffusion model climate emulators](https://mlsanity.com/paper/2605.03802/) (2026)
- [Probabilistic Precipitation Nowcasting with Rectified Flow Transformers](https://mlsanity.com/paper/2605.31204/) (2026)
- [Bridging short- and medium-range weather forecasting with machine learning](https://mlsanity.com/paper/2608.26822/) (2026)
- [IndiaWeatherBench: A Dataset and Benchmark for Data-Driven Regional Weather Forecasting over India](https://mlsanity.com/paper/2509.00653/) (2025)
- [Control-Augmented Autoregressive Diffusion for Data Assimilation](https://mlsanity.com/paper/2510.06637/) (2025)
- [Learning vertical coordinates via automatic differentiation of a dynamical core](https://mlsanity.com/paper/2512.17877/) (2025)
- [SwAIther-Precip: Lead-Time-Aware Bias Correction Enables Kilometer-Scale Downscaling of Global AI Precipitation Forecasts over Switzerland](https://mlsanity.com/paper/2605.16163/) (2026)
- [Does Your Wildfire Prediction Model Actually Work, or Just Score Well?](https://mlsanity.com/paper/2605.18911/) (2026)
- [Data-driven Mesoscale Weather Forecasting Combining Swin-Unet and Diffusion Models](https://mlsanity.com/paper/2503.19354/) (2025)
- [FuXi-Nowcast: Environment-conditioned deep learning for severe convection nowcasting](https://mlsanity.com/paper/2512.08974/) (2025)
- [Hierarchical Testing of a Hybrid Machine Learning-Physics Global Atmosphere Model](https://mlsanity.com/paper/2602.11313/) (2026)
- [Skillful Kilometer-Scale Regional Weather Forecasting via Global and Regional Coupling](https://mlsanity.com/paper/2603.28173/) (2026)
- [Conditional Flow Matching for Probabilistic Downscaling of Maximum 3-day Snowfall in Alaska](https://mlsanity.com/paper/2604.25172/) (2026)
- [RealBench: Benchmarking Data-Driven Numerical Weather Forecasting Under Operational Conditions and Extreme Event Challenges](https://mlsanity.com/paper/2605.24945/) (2026)
- [Scaling Storm-Resolving Atmospheric AI Simulation to the Entire Planet](https://mlsanity.com/paper/2606.31248/) (2026)
- [From Global to Local: Efficient Regional Weather Downscaling with Global Weather Foundation Model](https://mlsanity.com/paper/2607.03279/) (2026)
- [Explainable quantum-compressed machine learning for complex fluid flows](https://mlsanity.com/paper/2607.21688/) (2026)
- [An adaptive and evolvable deep reinforcement learning framework for weather prediction](https://mlsanity.com/paper/2608.09948/) (2026)

## Abstract (arXiv)

Storm-scale convection-allowing models (CAMs) are an important tool for predicting the evolution of thunderstorms and mesoscale convective systems that result in damaging extreme weather. By explicitly resolving convective dynamics within the atmosphere they afford meteorologists the nuance needed to provide outlook on hazard. Deep learning models have thus far not proven skilful at km-scale atmospheric simulation, despite being competitive at coarser resolution with state-of-the-art global, medium-range weather forecasting. We present a generative diffusion model called StormCast, which emulates the high-resolution rapid refresh (HRRR) model-NOAA's state-of-the-art 3km operational CAM. StormCast autoregressively predicts 99 state variables at km scale using a 1-hour time step, with dense vertical resolution in the atmospheric boundary layer, conditioned on 26 synoptic variables. We present evidence of successfully learnt km-scale dynamics including competitive 1-6 hour forecast skill for composite radar reflectivity alongside physically realistic convective cluster evolution, moist updrafts, and cold pool morphology. StormCast predictions maintain realistic power spectra for multiple predicted variables across multi-hour forecasts. Together, these results establish the potential for autoregressive ML to emulate CAMs -- opening up new km-scale frontiers for regional ML weather prediction and future climate hazard dynamical downscaling.