[A-AS02] From weather predictability to controllability

[AAS02-07] Uncertainty quantification of sub-grid scale parameterizations in atmospheric models toward robust theory of weather controllability

*Yohei Sawada¹, Duc Le¹, Futo Tomizawa¹, Mai Khanh Hung², Yuki Hirose¹, Kazuo Saito³ (1.Graduate School of Engineering, University of Tokyo, 2.National Center for Hydro-Meteorological Forecasting, Vietnam, 3.Atmosphere and Ocean Research Institute, University of Tokyo)

Keywords:Uncertainty quantification

Simulated strong sensitivity to initial conditions in atmosphere is a promising clue to realize weather control. However, this simulation of sensitivity to initial conditions is greatly affected by parametric uncertainties in sub-grid scale parameterization schemes in atmospheric models. Most of the current operational ensemble prediction systems perform the integration of an atmospheric model with fixed model parameters from different initial conditions and do not explicitly consider the uncertainties of model parameters, indicating that uncertainty quantification for sub-grid scale parameterizations is still in its infancy. Here we present the current advance of our uncertainty quantification of model parameters in a mesoscale atmospheric model. We realize to estimate non-parametric posterior distribution of parameters in sub-grid scale schemes using observation data with a reasonable computational cost. Three case studies will be provided: (1) some applications to an operational system in Vietnam, (2) severe stationary line shaped precipitation system, and (3) rapid intensification of a tropical cyclone.