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.
Quarterly Journal of the Royal Meteorological Society , volume =
2 Pith papers cite this work. Polarity classification is still indexing.
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Evaluates standard CP, normalized CP, and conformalized quantile regression against ensemble spread and standard deviation for uncertainty in idealized data assimilation, and tests CP perturbations in the assimilation cycle.
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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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Uncertainty quantification via conformal prediction in data assimilation
Evaluates standard CP, normalized CP, and conformalized quantile regression against ensemble spread and standard deviation for uncertainty in idealized data assimilation, and tests CP perturbations in the assimilation cycle.