A CVAE-GAN U-Net stochastic generator trained on CloudSat-CALIPSO data halves RMSE in cloud histograms and cuts global shortwave cloud radiative effect bias by a factor of three versus the Räisänen method.
Rows: cloud fraction (a-e), SW TOA CRE (f-j), LW TOA CRE (k-o)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
physics.ao-ph 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Assessment of cloud and associated radiation fields from a GAN stochastic cloud subcolumn generator
A CVAE-GAN U-Net stochastic generator trained on CloudSat-CALIPSO data halves RMSE in cloud histograms and cuts global shortwave cloud radiative effect bias by a factor of three versus the Räisänen method.