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New ML model CloudCast v2 improves 12-hour cloud-cover forecasts

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CloudCast v2, a new machine-learning model designed for 12-hour cloud-cover forecasting. This model is trained using the Copernicus European Regional Reanalysis and adapted with conditional flow matching to utilize satellite-derived cloud fields. CloudCast v2 demonstrates a 10% reduction in mean absolute error compared to its predecessor, CloudCast v1, and shows improved spatial agreement in forecasts beyond the typical nowcasting range. AI

IMPACT Enhances the accuracy and range of cloud-cover forecasts, potentially benefiting solar power operations and weather prediction.

RANK_REASON The cluster describes a new machine learning model presented in an arXiv paper, detailing its methodology and performance improvements over a previous version. [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 →

New ML model CloudCast v2 improves 12-hour cloud-cover forecasts

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The cluster describes a new machine learning model presented in an arXiv paper, detailing its methodology and performance improvements over a previous version. [lever_c_demoted from research: ic=1 …
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Mikko Partio, Leila Hieta, Ossi Laine ·

    From Nowcasting to Forecasting: Adapting a Reanalysis-Trained

    arXiv:2609.03763v1 Announce Type: new Abstract: Accurate cloud-cover forecasts are important for temperature prediction, radiation forecasting, and solar-power operations. Short-range forecasting methods can preserve observed cloud placement during the first forecast hours, but t…

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