Observation Error Statistics for Doppler Radar Radial Wind Superobservations Assimilated into the DWD COSMO-KENDA System

Abstract

Currently in operational numerical weather prediction (NWP) the density of high-resolution observations, such as Doppler radar radial winds (DRWs), is severely reduced in part to avoid violating the assumption of uncorrelated observation errors. To improve the quantity of observations used and the impact that they have on the forecast requires an accurate specification of the observation uncertainties. Observation uncertainties can be estimated using a simple diagnostic that utilizes the statistical averages of observation-minus-background and observation-minus-analysis residuals. We are the first to use a modified form of the diagnostic to estimate spatial correlations for observations used in an operational ensemble data assimilation system. The uncertainties for DRW superobservations assimilated into the Deutscher Wetterdienst convection-permitting NWP model are estimated and compared to previous uncertainty estimates for DRWs. The new results show that most diagnosed standard deviations are smaller than those used in the assimilation, hence, it may be feasible to assimilate DRWs using reduced error standard deviations. However, some of the estimated standard deviations are considerably larger than those used in the assimilation; these large errors highlight areas where the observation processing system may be improved. The error correlation length scales are larger than the observation separation distance and influenced by both the superobbing procedure and observation operator. This is supported by comparing these results to our previous study using Met Office data. Our results suggest that DRW error correlations may be reduced by improving the superobbing procedure and observation operator; however, any remaining correlations should be accounted for in the assimilation.

Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project

“…Further tests showed that the long correlations are in part a result of using superobservations and a simplified observation operator. We found similar results in a study of the assimilation of Doppler radar winds into the Deutsche Wetterdienst (DWD) convection-permitting system [59]*.…”

Interpreting estimated Observation Error Statistics of Weather Radar Measurements using the ICON-LAM-KENDA System

“…In the present work, we use the Desroziers method to explore characteristics of the OE for radial wind and reflectivity in the operational ICON-KENDA system of the DWD. A similar study has been done by Waller et al (2019) but for the COSMO-KENDA system and only for the radial wind. To authors's knowledge, it is the first in-depth attempt to investigate the OE statistics (variances and correlations) of reflectivity data.…”

“…For each elevation, standard deviations and horizontal correlations at different heights are calculated as Waller et al (2016cWaller et al ( , 2019. Results are shown for elevations 0.5 • , 1.5 • , 3.5 • , 5.5 • and 8.0 • .…”

Comparing diagnosed observation uncertainties with independent estimates: A case study using aircraft‐based observations and a convection‐permitting data assimilation system

“…Because of these (and possibly other) limitations of the diagnostic, error statistics estimated using this methodology should be interpreted as indicative, rather than necessarily quantitatively exact. Such results have nevertheless proved useful to identify the sources of observation and quality control errors (e.g., Waller et al, 2016aWaller et al, , 2016bWaller et al, , 2019.…”

“…A simple approach for estimating the observation error covariance, known as the Desroziers et al (2005) diagnostic, has become popular due to its ease of use. This method uses samples of observation-model departures routinely output from the data assimilation system and has been applied to a number of observation types, such as Doppler radial winds (Waller et al, 2016a(Waller et al, , 2019, satellite radiances, (e.g., Stewart et al, 2014;Waller et al, 2016b) smoothed aircraft-derived observations (Lange and Janjic, 2016), and surface observations (Tavolato and Isaksen, 2015). It is known that this diagnostic approach relies on unrealistic assumptions, and provides only an approximation to the observation error covariance (Desroziers et al, 2005;Ménard, 2016;Waller et al, 2016;Waller et al, 2017;Bathmann, 2018).…”