2019
DOI: 10.1175/mwr-d-19-0104.1
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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 o… Show more

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Cited by 21 publications
(47 citation statements)
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“…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]*.…”
Section: Observation Uncertainty In Data Assimilationsupporting
confidence: 77%
“…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]*.…”
Section: Observation Uncertainty In Data Assimilationsupporting
confidence: 77%
“…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.…”
Section: Introductionmentioning
confidence: 77%
“…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 • .…”
Section: Experimental Setup and Resultsmentioning
confidence: 99%
“…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.…”
Section: Desroziers Et Al's Diagnosed Estimatementioning
confidence: 99%
“…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;MĂŠnard, 2016;Waller et al, 2016;Waller et al, 2017;Bathmann, 2018).…”
Section: Introductionmentioning
confidence: 99%