Algorithmic Differentiation for Sensitivity Analysis in Cloud Microphysics
نویسندگان
چکیده
Abstract The role of clouds for radiative transfer, precipitation formation, and their interaction with atmospheric dynamics depends strongly on cloud microphysics. parameterization microphysical processes in weather climate models is a well‐known source uncertainties. Hence, robust quantification this uncertainty mandatory. Sensitivity analysis to date has typically investigated only few model parameters. We propose algorithmic differentiation (AD) as tool detect the magnitude timing at which state variable sensitive any hundreds uncertain parameters microphysics parameterization. AD increases computational cost by roughly third our simulations. explore methodology example warm conveyor belt trajectories, that is, air parcels rising rapidly from planetary boundary layer upper troposphere vicinity an extratropical cyclone. Based information derivatives respect parameters, ten contributing most are selected. These mostly related representation hydrometeor diameter fall velocity, activation condensation nuclei, heterogeneous freezing. demonstrate meaningfulness AD‐estimated sensitivities comparing results ensemble simulations spawned different points along where parameter settings used various members. ranking important these consistent AD. Thus, helpful selecting uncertainty.
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ژورنال
عنوان ژورنال: Journal of Advances in Modeling Earth Systems
سال: 2022
ISSN: ['1942-2466']
DOI: https://doi.org/10.1029/2021ms002849