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.
Machine learning methods for postprocessing ensemble forecasts of wind gusts: A systematic comparison.Monthly Weather Review, 150(1):235–257, January 2022
2 Pith papers cite this work. Polarity classification is still indexing.
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A post-processing pipeline applied to ECMWF subseasonal ensembles produces calibrated daily wind power forecasts for France that improve on climatology by 5-15% in CRPS up to 16 days ahead.
citing papers explorer
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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.
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Achieving Skilled and Reliable Daily Probabilistic Forecasts of Wind Power at Subseasonal-to-Seasonal Timescales over France
A post-processing pipeline applied to ECMWF subseasonal ensembles produces calibrated daily wind power forecasts for France that improve on climatology by 5-15% in CRPS up to 16 days ahead.