A distributional regression network acts as a backward operator to produce uncertainty-quantified, multivariate Gaussian retrievals of cloud properties from six solar channels for data assimilation.
Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods
2 Pith papers cite this work. Polarity classification is still indexing.
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Raw IFS forecasts outperform raw AIFS for wind speed at all horizons, but post-processing with EMOS or QR reduces the gap, leaving IFS ahead mainly at short leads.
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Using Distributional Regression Networks to Retrieve Cloud Properties from Solar Satellite Channels for Data Assimilation
A distributional regression network acts as a backward operator to produce uncertainty-quantified, multivariate Gaussian retrievals of cloud properties from six solar channels for data assimilation.