# Integrating Weather Foundation Model and Satellite to Enable Fine-Grained Solar Irradiance Forecasting

arXiv: https://arxiv.org/abs/2603.14845 · HTML: https://mlsanity.com/paper/2603.14845/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 16 Mar 2026.

## Builds on (most cited first, 11 of 11)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [FuXi: A cascade machine learning forecasting system for 15-day global weather forecast](https://mlsanity.com/paper/2306.12873/) (2023)
- [FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators](https://mlsanity.com/paper/2208.05419/) (2022)
- [The Road Less Scheduled](https://mlsanity.com/paper/2405.15682/) (2024)
- [Improving day-ahead Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context](https://mlsanity.com/paper/2306.01112/) (2023)
- [FuXi-2.0: Advancing machine learning weather forecasting model for practical applications](https://mlsanity.com/paper/2409.07188/) (2024)
- [Utilizing Strategic Pre-training to Reduce Overfitting: Baguan -- A Pre-trained Weather Forecasting Model](https://mlsanity.com/paper/2505.13873/) (2025)
- [FusionSF: Fuse Heterogeneous Modalities in a Vector Quantized Framework for Robust Solar Power Forecasting](https://mlsanity.com/paper/2402.05823/) (2024)
- [Data-driven Surface Solar Irradiance Estimation using Neural Operators at Global Scale](https://mlsanity.com/paper/2411.08843/) (2024)
- [SolarCrossFormer: Improving day-ahead Solar Irradiance Forecasting by Integrating Satellite Imagery and Ground Sensors](https://mlsanity.com/paper/2509.15827/) (2025)
- [Solarcast-ML: Per Node GraphCast Extension for Solar Energy Production](https://mlsanity.com/paper/2406.13559/) (2024)

## Abstract (arXiv)

Accurate day-ahead solar irradiance forecasting is essential for integrating solar energy into the power grid. However, it remains challenging due to the pronounced diurnal cycle and inherently complex cloud dynamics. Current methods either lack fine-scale resolution (e.g., numerical weather prediction, weather foundation models) or degrade at longer lead times (e.g., satellite extrapolation). We propose Baguan-solar, a two-stage multimodal framework that fuses forecasts from Baguan, a global weather foundation model, with high-resolution geostationary satellite imagery to produce 24-hour irradiance forecasts at kilometer scale. Its decoupled two-stage design first forecasts day-night continuous intermediates (e.g., cloud cover) and then infers irradiance, while its modality fusion jointly preserves fine-scale cloud structures from satellite and large-scale constraints from Baguan forecasts. Evaluated over East Asia using CLDAS as ground truth, Baguan-solar outperforms strong baselines (including ECMWF IFS, vanilla Baguan, and SolarSeer), reducing RMSE by 16.08% and better resolving cloud-induced transients. An operational deployment of Baguan-solar has supported solar power forecasting in an eastern province in China, since July 2025. Our code is accessible at https://github.com/DAMO-DI-ML/Baguan-solar.git.