# CasCast: Skillful High-resolution Precipitation Nowcasting via Cascaded Modelling

arXiv: https://arxiv.org/abs/2402.04290 · HTML: https://mlsanity.com/paper/2402.04290/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 35 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Builds on (most cited first, 23 of 23)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Imagen Video: High Definition Video Generation with Diffusion Models](https://mlsanity.com/paper/2210.02303/) (2022)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Simple diffusion: End-to-end diffusion for high resolution images](https://mlsanity.com/paper/2301.11093/) (2023)
- [ClimaX: A foundation model for weather and climate](https://mlsanity.com/paper/2301.10343/) (2023)
- [Skillful Precipitation Nowcasting using Deep Generative Models of Radar](https://mlsanity.com/paper/2104.00954/) (2021)
- [Photorealistic Video Generation with Diffusion Models](https://mlsanity.com/paper/2312.06662/) (2023)
- [Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction](https://mlsanity.com/paper/2003.01460/) (2020)
- [On the Importance of Noise Scheduling for Diffusion Models](https://mlsanity.com/paper/2301.10972/) (2023)
- [SimVP: Simpler yet Better Video Prediction](https://mlsanity.com/paper/2206.05099/) (2022)
- [Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model](https://mlsanity.com/paper/1706.03458/) (2017)
- [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)
- [Vision Transformers with Hierarchical Attention](https://mlsanity.com/paper/2106.03180/) (2021)
- [FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecasting](https://mlsanity.com/paper/2402.00059/) (2024)
- [Diffusion Model-based Probabilistic Downscaling for 180-year East Asian Climate Reconstruction](https://mlsanity.com/paper/2402.06646/) (2024)
- [Hierarchical Diffusion Autoencoders and Disentangled Image Manipulation](https://mlsanity.com/paper/2304.11829/) (2023)
- [A Knowledge-enhanced Two-stage Generative Framework for Medical Dialogue Information Extraction](https://mlsanity.com/paper/2307.16200/) (2023)

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

- [A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers](https://mlsanity.com/paper/2508.21148/) (2025)
- [OneForecast: A Universal Framework for Global and Regional Weather Forecasting](https://mlsanity.com/paper/2502.00338/) (2025)
- [Scientists' First Exam: Probing Cognitive Abilities of MLLM via Perception, Understanding, and Reasoning](https://mlsanity.com/paper/2506.10521/) (2025)
- [Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling](https://mlsanity.com/paper/2405.13796/) (2024)
- [Deep Learning and Foundation Models for Weather Prediction: A Survey](https://mlsanity.com/paper/2501.06907/) (2025)
- [Deep learning for precipitation nowcasting: A survey from the perspective of time series forecasting](https://mlsanity.com/paper/2406.04867/) (2024)
- [OmniEarth-Bench: Towards Holistic Evaluation of Earth's Six Spheres and Cross-Spheres Interactions with Multimodal Observational Earth Data](https://mlsanity.com/paper/2505.23522/) (2025)
- [WeatherGFM: Learning A Weather Generalist Foundation Model via In-context Learning](https://mlsanity.com/paper/2411.05420/) (2024)
- [PostCast: Generalizable Postprocessing for Precipitation Nowcasting via Unsupervised Blurriness Modeling](https://mlsanity.com/paper/2410.05805/) (2024)
- [RadarQA: Multi-modal Quality Analysis of Weather Radar Forecasts](https://mlsanity.com/paper/2508.12291/) (2025)
- [FlowCast: Advancing Precipitation Nowcasting with Conditional Flow Matching](https://mlsanity.com/paper/2511.09731/) (2025)
- [Generative emulation of chaotic dynamics with coherent prior](https://mlsanity.com/paper/2504.14264/) (2025)
- [PredBench: Benchmarking Spatio-Temporal Prediction across Diverse Disciplines](https://mlsanity.com/paper/2407.08418/) (2024)
- [WaveC2R: Wavelet-Driven Coarse-to-Refined Hierarchical Learning for Radar Retrieval](https://mlsanity.com/paper/2511.17558/) (2025)
- [Extreme Weather Nowcasting via Local Precipitation Pattern Prediction](https://mlsanity.com/paper/2602.05204/) (2026)
- [DuoCast: Duo-Probabilistic Diffusion for Precipitation Nowcasting](https://mlsanity.com/paper/2412.01091/) (2024)
- [Transforming Weather Data from Pixel to Latent Space](https://mlsanity.com/paper/2503.06623/) (2025)
- [Learning to Refine: Spectral-Decoupled Iterative Refinement Framework for Precipitation Nowcasting](https://mlsanity.com/paper/2606.02661/) (2026)
- [Evidential Deep Learning for Probabilistic Modelling of Extreme Storm Events](https://mlsanity.com/paper/2412.14048/) (2024)
- [RainPro-8: An Efficient Deep Learning Model to Estimate Rainfall Probabilities Over 8 Hours](https://mlsanity.com/paper/2505.10271/) (2025)
- [Reframing Generative Models for Physical Systems using Stochastic Interpolants](https://mlsanity.com/paper/2509.26282/) (2025)
- [SimCast: Enhancing Precipitation Nowcasting with Short-to-Long Term Knowledge Distillation](https://mlsanity.com/paper/2510.07953/) (2025)
- [RainDiff: End-to-end Precipitation Nowcasting Via Token-wise Attention Diffusion](https://mlsanity.com/paper/2510.14962/) (2025)
- [SynCast: Synergizing Contradictions in Precipitation Nowcasting via Diffusion Sequential Preference Optimization](https://mlsanity.com/paper/2510.21847/) (2025)
- [Omni-Weather: A Unified Multimodal Model for Weather Radar Understanding and Generation](https://mlsanity.com/paper/2512.21643/) (2025)
- [Stable Attention Response for Reliable Precipitation Nowcasting](https://mlsanity.com/paper/2605.13181/) (2026)
- [McCast: Memory-Guided Latent Drift Correction for Long-Horizon Precipitation Nowcasting](https://mlsanity.com/paper/2605.13197/) (2026)
- [Probabilistic Precipitation Nowcasting with Rectified Flow Transformers](https://mlsanity.com/paper/2605.31204/) (2026)
- [RainODE: Continuous-Time Precipitation Forecasting with Latent Neural ODEs](https://mlsanity.com/paper/2606.29855/) (2026)
- [DAWP: A framework for global observation forecasting via Data Assimilation and Weather Prediction in satellite observation space](https://mlsanity.com/paper/2510.15978/) (2025)

## Abstract (arXiv)

Precipitation nowcasting based on radar data plays a crucial role in extreme weather prediction and has broad implications for disaster management. Despite progresses have been made based on deep learning, two key challenges of precipitation nowcasting are not well-solved: (i) the modeling of complex precipitation system evolutions with different scales, and (ii) accurate forecasts for extreme precipitation. In this work, we propose CasCast, a cascaded framework composed of a deterministic and a probabilistic part to decouple the predictions for mesoscale precipitation distributions and small-scale patterns. Then, we explore training the cascaded framework at the high resolution and conducting the probabilistic modeling in a low dimensional latent space with a frame-wise-guided diffusion transformer for enhancing the optimization of extreme events while reducing computational costs. Extensive experiments on three benchmark radar precipitation datasets show that CasCast achieves competitive performance. Especially, CasCast significantly surpasses the baseline (up to +91.8%) for regional extreme-precipitation nowcasting.