SimCast: Enhancing Precipitation Nowcasting with Short-to-Long Term Knowledge Distillation

2025-10-09

AI Summary

Precipitation nowcasting requires high-precision forecasting of future radar echo sequences within the complex Earth system, playing a critical role in disaster prevention, mitigation, and traffic management. To address this, we propose SimCast—a novel training framework featuring a pioneering short-to-long knowledge distillation strategy that enables efficient transfer of multi-stage predictive knowledge. We further introduce a weighted mean squared error (MSE) loss and a non-autoregressive architecture to enhance spatiotemporal modeling capabilities—particularly for intense precipitation regions. Additionally, we integrate SimCast’s deterministic forecasts into the diffusion-based model CasCast to improve uncertainty quantification. Evaluated on SEVIR, HKO-7, and MeteoNet, our method achieves average Critical Success Index (CSI) scores of 0.452, 0.474, and 0.361, respectively—significantly outperforming state-of-the-art methods without increasing inference overhead.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Diffusion Models for Vision

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Precipitation nowcasting predicts future radar sequences based on current observations, which is a highly challenging task driven by the inherent complexity of the Earth system. Accurate nowcasting is of utmost importance for addressing various societal needs, including disaster management, agriculture, transportation, and energy optimization. As a complementary to existing non-autoregressive nowcasting approaches, we investigate the impact of prediction horizons on nowcasting models and propose SimCast, a novel training pipeline featuring a short-to-long term knowledge distillation technique coupled with a weighted MSE loss to prioritize heavy rainfall regions. Improved nowcasting predictions can be obtained without introducing additional overhead during inference. As SimCast generates deterministic predictions, we further integrate it into a diffusion-based framework named CasCast, leveraging the strengths from probabilistic models to overcome limitations such as blurriness and distribution shift in deterministic outputs. Extensive experimental results on three benchmark datasets validate the effectiveness of the proposed framework, achieving mean CSI scores of 0.452 on SEVIR, 0.474 on HKO-7, and 0.361 on MeteoNet, which outperforms existing approaches by a significant margin.

Problem

• Improving precipitation nowcasting accuracy for disaster management
• Addressing blurriness in deterministic weather prediction models
• Overcoming distribution shift in short-term rainfall forecasts

Innovation

• Short-to-long term knowledge distillation training pipeline
• Weighted MSE loss prioritizes heavy rainfall regions
• Integrates deterministic model into diffusion-based probabilistic framework