Parameter and state estimation with ensemble Kalman filter based algorithms for convective‐scale applications. (28th August 2018)

• Record Type: Journal Article
• Authors: Ruckstuhl, Y. M.; Janjić, T.
• Is Part Of: Quarterly journal of the Royal Meteorological Society. Volume 144:Number 712(2018)
• Page Start: 826
• Page End: 841
• Publication Date: 2018-08-28
• Subjects: convective‐scale data assimilation -- EnKF -- nonlinearity -- non‐Gaussian statistics -- parameter estimation -- QPEns -- quadratic filter; Meteorology -- Periodicals; 551.5
• DOI: 10.1002/qj.3257
• Languages: English
• ISSNs: 0035-9009

Abstract

Abstract : Representation of clouds in convection‐permitting models is sensitive to numerical weather prediction (NWP) model parameters that are often very crudely known (for example roughness length). Our goal is to allow for uncertainty in these parameters and estimate them from data using the ensemble Kalman filter (EnKF) approach. However, to deal with difficulties associated with convective‐scale applications, such as non‐Gaussianity and constraints on state and parameter values, modifications to classical EnKF are necessary. In this article, we evaluate and extend several recently developed EnKF‐based algorithms that either incorporate constraints such as mass conservation and positivity of precipitation explicitly or introduce higher order moments on the joint state and parameter estimation problem. We compare their results with the localized EnKF for a common idealized test case. The test case uses perfect model experiments with the one‐dimensional modified shallow‐water model, which was designed to mimic important properties of convection. We use a stochastic dynamical model for parameters, in order to prevent underdispersion in parameter space. To deal with localization for estimation of parameters, we introduce a method called global updating, which is a computationally cheap modification of spatial updating and was proven successful in this context. The sensitivity of the results to the number of ensemble members and localization, as well as observation coverage and frequency, is shown. Although all algorithms are capable of reducing the initial state and parameter errors, it is concluded that mass conservation is important when the localization radius is small and/or the observations are sparse. In addition, accounting for higher order moments in the joint space and parameter estimation problem is beneficial when the ensemble size is large enough or when applied to parameter estimation only. Abstract : All algorithms are capable of reducing the initial state and parameter biases. Mass conservation (QPEns) is important when the localization radius is small and/or the observations are sparse. Accounting for skewness (QF) is beneficial when the ensemble size is large or when applied to parameter estimation only.