Doppler radar radial winds in HIRLAM. Part II: optimizing the super-observation processing

Salonen, Kirsti; Järvinen, Heikki; Haase, Günther; Niemelä, Sami; Eresmaa, Reima

doi:10.1111/j.1600-0870.2008.00381.x

Cited by 20 publications

(14 citation statements)

References 17 publications

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“…To mitigate against the large number of high density observations, several radar gates are combined into superobservations. We note that, in general, there are two main approaches to creating superobservations; one adds average innovation values to the background value at the superobservation location (e.g., Daley (1991); Simonin et al (2014)); the other, used at DWD, simply averages observations (e.g., as in Alpert and Kumar (2007); Salonen et al (2009);Bick et al (2016)).…”

Section: B Doppler Radar Radial Windsmentioning

confidence: 99%

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

Waller

Bauernschubert

Dance

et al. 2019

Monthly Weather Review

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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.

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Section: B Doppler Radar Radial Windsmentioning

confidence: 99%

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

Waller

Bauernschubert

Dance

et al. 2019

Monthly Weather Review

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“…Already, many meteorological services are actively working on the assimilation of Doppler radial wind from weather radar. Some have concentrated their efforts on the best way to use these new observations and/or deriving an appropriate observation error (Lindskog et al, 2001;Salonen et al, 2007;Rihan et al, 2008;Salonen et al, 2009). For example, Salonen et al (2009) present a super-observation technique tested under the High-Resolution Limited-Area Model (HIRLAM) framework and Rihan et al (2008) derived an observation error and tested it in the Met Office system.…”

Section: Introductionmentioning

confidence: 99%

Doppler radar radial wind assimilation using an hourly cycling 3D‐Var with a 1.5 km resolution version of the Met Office Unified Model for nowcasting

Simonin

Ballard

2014

Quart J Royal Meteoro Soc

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This article describes the development and trials at the Met Office of the assimilation of radar Doppler radial winds in a pre‐operational framework. It uses a high‐resolution 1.5 km convective‐scale version of the Unified Model for nowcasting, on a small domain covering southern England and Wales. The assimilation was performed using a version of the Met Office 3D‐variational data assimilation system at 1.5 km resolution. The Doppler radial wind observations were provided by the original four Dopplerized C‐band single‐polarization radars in the UK radar network, all situated in the south of England. This article provides a description of the Doppler radial wind observation operator, pre‐processing, observation errors and data assimilation configuration. The system is tested on and verified for four case studies using continuous hourly cycling with 7 h forecasts. A significant positive impact for rain and wind is found in the first half of the forecasts (nowcasting time‐scale).

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“…These innovations are then averaged and added to the model observation closest to the center of the superobbing cell to provide the super-observation. Optimization of the superobbing processing from dense raw data is the compromise between the above two factors including saving finer than average size scales and obtaining more stable estimates for the remaining scales by reducing stochastic errors [12]. Additionally, the problem of representativeness is necessary to be accounted for while comparing and assimilating data from sources with different spatial resolutions.…”

Section: Introductionmentioning

confidence: 99%

Mesoscale Resolution Radar Data Assimilation Experiments with the Harmonie Model

2018

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This study presents a pre-processing approach adopted for the radar reflectivity data assimilation and results of simulations with the Harmonie numerical weather prediction model. The proposed method creates a 3D regular grid in which a horizontal size of meshes coincides with the horizontal model resolution. This minimizes the representative error associated with the discrepancy between resolutions of informational sources. After such preprocessing, horizontal structure functions and their gradients for radar reflectivity maintain the sizes and shapes of precipitation patterns similar to those of the original data. The method shows an improvement of precipitation prediction within the radar location area in both the rain rates and spatial pattern presentation. It redistributes precipitable water with smoothed values over the common domain since the control runs show, among several sub-domains with increased and decreased values, correspondingly. It also reproduces the mesoscale belts and cell patterns of sizes from a few to ten kilometers in precipitation fields. With the assimilation of radar data, the model simulates larger water content in the middle troposphere within the layer from 1 km to 6 km with major variations at 2.5 km to 3 km. It also reproduces the mesoscale belt and cell patterns of precipitation fields.

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Doppler radar radial winds in HIRLAM. Part II: optimizing the super-observation processing

Cited by 20 publications

References 17 publications

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

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

Doppler radar radial wind assimilation using an hourly cycling 3D‐Var with a 1.5 km resolution version of the Met Office Unified Model for nowcasting

Mesoscale Resolution Radar Data Assimilation Experiments with the Harmonie Model

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