Ensemble prediction and parameter estimation system: the concept

Abstract

Abstract This paper discusses algorithmic estimation of numerical weather prediction (NWP) model closure parameters which appear in physical parametrizations of sub‐grid scale processes. Currently, these parameters are specified manually. Our hypothesis is that the NWP model forecast skill is sensitive to the specified parameter values. Therefore some parameter value combinations score better than others, but it is very demanding to manually specify the optimal combination. First, we approximate the number of test forecasts that would be needed to estimate the posterior probability densities of a limited number of closure parameters, in a spirit of the Bayesian parameter estimation approach. Second, we discuss various options to accommodate the related huge computational task; we end up proposing that operational ensemble prediction might be a potential host. Third, we analyse the ensemble prediction system characteristics from the parameter estimation point of view. We rule out various standard parameter estimation methods; instead we suggest an estimation concept that can be on‐line coupled with existing ensemble prediction systems to utilize the operational ensemble simulations. Thus practically no additional computations are introduced. Such a method, the ensemble prediction and parameter estimation system (EPPES), is developed here and demonstrated in low‐order numerical tests in the companion paper (Laine M, Solonen A, Haario H, Järvinen H. 2011. Q. J. R. Meteorol. Soc. DOI: 10.1002/qj.922). Copyright © 2011 Royal Meteorological Society