Interpreting estimated observation error statistics of weather radar measurements using the ICON-LAM-KENDA system

Yuefei Zeng,Jinzhong Min,Yuxuan Feng,Elisabeth Bauernschubert,Tijana Janjic,Klaus Stephan,Alberto De Lozar,Ulrich Blahak

doi:10.5194/amt-14-5735-2021

Abstract

Abstract. Assimilation of weather radar measurements including radar reflectivity and radial wind data has been operational at the Deutscher Wetterdienst, with a diagonal observation error (OE) covariance matrix. For an implementation of a full OE covariance matrix, the statistics of the OE have to be a priori estimated, for which the Desroziers method has been often used. However, the resulted statistics consists of contributions from different error sources and are difficult to interpret. In this work, we use an approach that is based on samples for truncation error in radar observation space to approximate the representation error due to unresolved scales and processes (RE) and compare its statistics with the OE statistics estimated by the Desroziers method. It is found that the statistics of the RE help the understanding of several important features in the variances and correlation length scales of the OE for both reflectivity and radial wind data and the other error sources from the microphysical scheme, radar observation operator and the superobbing technique may also contribute, for instance, to differences among different elevations and observation types. The statistics presented here can serve as a guideline for selecting which observations are assimilated and for assignment of the OE covariance matrix that can be diagonal or full and correlated.

Highlights

Nowadays, assimilation of weather radar measurements has been widely adopted in many weather services for convective-scale numerical weather prediction (NWP) models (Gustafsson et al, 2018)
To see the errors decoupled from observed values, we divide standard deviations by simulated data from the high-resolution model run that represents observations; this is equal to the inverse of signal-to-noise ratio (e.g., Russ, 2006, hereafter ISNR)
Only reflectivity data ≥ 5 dBZ are evaluated in Fig. 9d, in which standard deviations of all elevations increase until about 3 km and decrease until about 5 km before increasing until around 7 km and decreasing again to the top

Summary

Introduction

Assimilation of weather radar measurements has been widely adopted in many weather services for convective-scale numerical weather prediction (NWP) models (Gustafsson et al, 2018). At the Met Office, volume scans of radar reflectivity data are directly assimilated (Hawkness-Smith and Simonin, 2021) by the hourly cycling 4D-Var (Milan et al, 2020). At the Deutscher Wetterdienst (DWD), the Kilometre-scale ENsemble Data Assimilation (KENDA) system (Schraff et al, 2016) has been developed for the COSMO (COnsortium for Small-scale MOdelling, Baldlauf et al, 2011) and the ICON (ICOsahedral Nonhydrostatic, Zängl et al, 2015) models. Since June 2020, the radial wind and reflectivity data have been assimilated via the local ensemble transform Kalman filter (LETKF, Hunt et al, 2007) combined with the latent heat nudging (Stephan et al, 2008) for the COSMO model in the operational suite; the ICON-LAM (ICON – Limited Area Model) is the limited area version of the ICON model and is to replace the COSMO model in the operational forecasting system. The ICON-D2 (D: Deutschland (Germany); 2: 2 km) is an ICON-LAM setting at approximately 2 km grid spacing, which is restricted to Germany and the neighboring coun-

Methods

Results

Discussion

Conclusion