Blended parameterization in an atmospheric model: Improving severestorm ensemble prediction by considering uncertainties in model physics

Abstract

Physics parameterizations are often needed for numerical weather prediction (NWP) of precipitation forecast. This is mainly because the resolutions of most computational atmospheric models are not fine enough to explicitly resolve sub-grid scale processes associated with precipitation systems. The various options in each physical parameterization scheme introduce model physics uncertainty, leading to variations in simulated precipitation due to differing representations of physical processes. We aim to quantify and reduce uncertainties in severe storm prediction arising from selecting physics parameterization schemes. In this study, we introduced a method called "blended parameterization" in an atmospheric model. This method parameterizes the selection of physical parameterization schemes using weighting parameters. This approach reduces the model selection problem to the optimization of these weighting parameters. The Markov Chain Monte Carlo (MCMC) method was used to estimate the posterior probability distribution of the weighting parameters. The large computational cost of MCMC was resolved by a surrogate model that efficiently mimics the relationship between weighting parameters and likelihood. Subsequently, the weighting parameters were sampled from the posterior distribution, followed by conducting an ensemble simulation to predict rainfall in Vietnam. Our optimized "blended parameterized" ensemble prediction system outperformed a conventional physical ensemble prediction system similar to that operationally used in Vietnam.