Characterizing noise and spurious convection in convective data assimilation

Abstract

The occurrence of noise and spurious convective cells in analyses of convective‐scale data assimilation (DA) is a problem that is not quantified sufficiently to develop reliable set‐ups for the DA of radar observations in an ensemble Kalman filter. This article presents two approaches to quantifying these phenomena using a local ensemble transform Kalman filter testbed, where simulated radar observations are assimilated with varying spatial and temporal DA parameters. Firstly, the dynamical coupling of cold pools and emerging cells of organized deep convection is quantified in terms of spatio‐temporal correlations, so that spurious perturbations can be identified. Secondly, the abundance of gravity‐wave noise is characterized by the variance of the vertical velocity field during DA cycling and the ensemble forecasts. This evaluation is performed separately for subsets of model points inside observed storms, in their vicinity and in the surrounding clear‐air regions. Varying horizontal localization lengths, observation averaging scales and the length of the assimilation cycling interval are compared, with respect to the two diagnostic approaches. It is concluded that the cold‐pool coupling method and the partitioned variance of vertical velocity fields are viable measures to quantify the influence of DA parameters and algorithms on the balance of analysis states that are used to initiate forecasts of convection.

Assimilating Visible and Infrared Radiances in Idealized Simulations of Deep Convection

“…Our OSSE setup largely follows previous studies for radar data assimilation (Lange and Craig 2014;Bachmann et al 2019Bachmann et al , 2020) using the COSMO model, version 5.3. We initialize wind, temperature, and humidity with a radiosonde profile from Payerne, Switzerland, at 1200 UTC 30 July 2007 and add two types of perturbation for each ensemble member to account for the uncertainty on smaller and larger scales (see below).…”

“…To reduce the complexity and focus on a particularly challenging case with randomly located convection, we conduct a more idealized OSSE with homogeneous initial conditions and small random noise to trigger convection following studies for radar data assimilation (Lange and Craig 2014;Bachmann et al 2019Bachmann et al , 2020. In this setup, we neglect orography and land surface heterogeneity.…”

Comparison of Methods Accounting for Subgrid-Scale Model Error in Convective-Scale Data Assimilation

“…Comparatively, the added vertical kinetic energy dissipates rapidly in the forecasts and is therefore unlikely the dominant factor for improvement at longer forecast lead time. Furthermore, the vertical kinetic energy is also considered as an indicator for model imbalance as surface pressure tendency (Lange et al 2017). The SAN is the most effective FIG.…”

“…, 1700, and 1800 UTC. A detailed description of the KENDA system and the implementation of the LETKF is given in Schraff et al (2016), Bick et al (2016), and Lange and Janjić (2016). More details about treatment (e.g., specification of observation error, localization and superobbing) of observations (especially radar reflectivity) are available in Zeng et al (2018).…”

Representation of Model Error in Convective‐Scale Data Assimilation: Additive Noise, Relaxation Methods, and Combinations

“…As a probabilistic approach, the EnKF should be especially useful for the convective‐scale data assimilation because of the stochastic nature of convection. In particular, there have been already a number of studies on performance of the EnKF applied to convective‐scale radar data assimilation (Aksoy et al, ; Bick et al, ; Caya et al, ; Dowell & Wicker, ; Dowell et al, , ; Gao & Xue, ; Lange & Craig, ; Lange et al, ; Snyder & Zhang, ; Sobash & Stensrud, ; Tong & Xue, ; Xue et al, ) and promising results have been gained. However, many meteorological centers still rely on the latent heat nudging (LHN) to indirectly assimilate radar reflectivities for the operational usage (Gustafsson et al, ).…”