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IntegratingWeather Foundation Model and Satellite to Enable Fine-Grained Solar Irradiance Forecasting

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Canonical route: /signal-canvas/integratingweather-foundation-model-and-satellite-to-enable-fine-grained-solar-irradiance-forecasting

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Proof freshness: stale
Proof status: partial
Display score: 9/10
Last proof check: 2026-03-19
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 33%

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IntegratingWeather Foundation Model and Satellite to Enable Fine-Grained Solar Irradiance Forecasting

Canonical ID integratingweather-foundation-model-and-satellite-to-enable-fine-grained-solar-irradiance-forecasting | Route /signal-canvas/integratingweather-foundation-model-and-satellite-to-enable-fine-grained-solar-irradiance-forecasting

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MCP example

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Paper mode· single-doc scopescope: integratingweather-foundation-model-and-satellite-to-enable-fine-grained-solar-irradiance-forecasting

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Claim map

Strong 12Mixed 0Weak 0

Evidencepartial
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
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Evidencepartial
to produce 24-hour irradiance forecasts at kilometer scale
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Evidencepartial
Its decoupled two-stage design first forecasts day-night continuous intermediates (e.g., cloud cover) and then infers irradiance
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Evidencepartial
its modality fusion jointly preserves fine-scale cloud structures from satellite and large-scale constraints from Baguan forecasts
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Evidencepartial
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%
Implicationmissing
Implication not extracted yet.
Verificationpartial
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Evidencepartial
better resolving cloud-induced transients
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Evidencepartial
An operational deployment of Baguan-solar has supported solar power forecasting in an eastern province in China, since July 2025
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Evidencepartial
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)
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
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.
Implicationpartial
This is a direct description of the proposed method in the abstract.
Verificationpartial
partial
Evidencepartial
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.
Implicationpartial
This is a direct statement of the output and resolution of the proposed method in the abstract.
Verificationpartial
partial
Evidencepartial
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.
Implicationpartial
This is a specific quantitative result presented in the abstract.
Verificationpartial
partial
Evidencepartial
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.
Implicationpartial
This is a direct comparison of the proposed method against specific baselines, stated in the abstract.
Verificationpartial
partial