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figure shows the typical behavior of these bias correction methods. There is a significant bias between the ERA5 and HadGEM3 datasets, with HadGEM3 showing much more light drizzle ( Takahashi et al. 2021 ) with precipitation values below 0.1 mm day −1 . Since this is a marginal distribution, QM is expected to perform well. If the datasets were infinitely large, then by definition, QM would perform perfectly here ( Maraun et al. 2017 ). The same is true of the combined UNIT+QM method. The UNIT
figure shows the typical behavior of these bias correction methods. There is a significant bias between the ERA5 and HadGEM3 datasets, with HadGEM3 showing much more light drizzle ( Takahashi et al. 2021 ) with precipitation values below 0.1 mm day −1 . Since this is a marginal distribution, QM is expected to perform well. If the datasets were infinitely large, then by definition, QM would perform perfectly here ( Maraun et al. 2017 ). The same is true of the combined UNIT+QM method. The UNIT
data, two other critical demands are yet to be addressed. First, meteorologists often demand the models to predict high rain rates accurately for disaster prevention while simultaneously predicting reasonable rain rates in drizzle (low rainfall) regions. Current deep learning models achieve decent performance on high-rainfall regions by reweighting in the objective function but output a larger raining area as a side effect. Specifically, this kind of prediction contains many locations with small
data, two other critical demands are yet to be addressed. First, meteorologists often demand the models to predict high rain rates accurately for disaster prevention while simultaneously predicting reasonable rain rates in drizzle (low rainfall) regions. Current deep learning models achieve decent performance on high-rainfall regions by reweighting in the objective function but output a larger raining area as a side effect. Specifically, this kind of prediction contains many locations with small
convection in marine stratocumulus. Part I: Drizzling conditions . J. Atmos. Sci. , 75 , 257 – 274 , https://doi.org/10.1175/JAS-D-17-0070.1 .
convection in marine stratocumulus. Part I: Drizzling conditions . J. Atmos. Sci. , 75 , 257 – 274 , https://doi.org/10.1175/JAS-D-17-0070.1 .
al. 2007 ; Price 2019 ). At a certain point, the air is no longer saturated and can therefore absorb more water vapor, leading to the evaporation of fog droplets. However, a decrease in supersaturation can also be achieved by the entrainment of dry air or drizzle formation and deposition. Either way, if the air is undersaturated, evaporation begins, and the fog event ends. Thus, despite many years of research it is still difficult to provide an accurate forecast ( Pérez-Díaz et al. 2017 ; Bergot and
al. 2007 ; Price 2019 ). At a certain point, the air is no longer saturated and can therefore absorb more water vapor, leading to the evaporation of fog droplets. However, a decrease in supersaturation can also be achieved by the entrainment of dry air or drizzle formation and deposition. Either way, if the air is undersaturated, evaporation begins, and the fog event ends. Thus, despite many years of research it is still difficult to provide an accurate forecast ( Pérez-Díaz et al. 2017 ; Bergot and
