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MTG satellite data enhances CNN-based solar irradiance retrieval

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new convolutional neural network (CNN) architecture that integrates data from the Meteosat Third Generation (MTG) satellite constellation with existing Meteosat Second Generation (MSG) imagery to improve the retrieval of Surface Solar Irradiance (SSI). This hybrid model demonstrated significant improvements in accuracy under overcast and cloudy conditions over Northern Europe, reducing the Root Mean Square Error (RMSE) by up to 8.2 W m$^{-2}$ compared to MSG-only models. While the higher resolution of MTG imagery enhances performance in variable cloud cover, the study indicates that it does not fully address limitations in clear-sky irradiance retrieval, where physics-based models still perform better. AI

IMPACT Improves accuracy for photovoltaic energy monitoring and forecasting by enhancing satellite data processing.

RANK_REASON Academic paper detailing a new methodology for satellite imagery analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

MTG satellite data enhances CNN-based solar irradiance retrieval

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Academic paper detailing a new methodology for satellite imagery analysis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Gordei Prib\~otkin, Piia Post, Velle Toll ·

    Meteosat Third Generation imagery improves CNN-based SSI retrieval

    arXiv:2607.28093v1 Announce Type: cross Abstract: Accurate Surface Solar Irradiance (SSI) estimation is increasingly important for photovoltaic energy monitoring and forecasting. The recently introduced Meteosat Third Generation (MTG) satellite constellation provides imaging data…

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