Bias estimation of Doppler‐radar radial‐wind observations

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

doi:10.1002/qj.114

Cited by 18 publications

(14 citation statements)

References 25 publications

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“…Because the central processing cannot filter out 100% of the unwanted or contaminated observations, the aim was to use model wind fields in order to remove any observations that had the potential to damage the assimilation process. Biases were monitored using the method described in Salonen et al (2007) and observation − background statistics ( O − B ) from the routine monitoring were used to derive an observation innovation error; see section 3.4. As the Southern UK Fixed‐domain model (SUKF) was not running in real time, the model wind background field used in the monitoring is the operational T + 3 UK forecast.…”

Section: Doppler Radar Radial Windmentioning

confidence: 99%

“…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%

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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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Section: Doppler Radar Radial Windmentioning

confidence: 99%

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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“…Here, the mean OmB difference for vector wind (bias) and the standard deviation of the OmB difference for radial wind component are considered. Bias estimation is done following Salonen et al (2007). Calculating bias simply by summing up radial wind OmB values results in a near‐zero bias, even in presence of systematic wind speed and/or direction differences.…”

Section: Resultsmentioning

confidence: 99%

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

Salonen¹,

Järvinen²,

Haase

et al. 2009

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C T Doppler radar radial wind observations are modelled in numerical weather prediction (NWP) within observation errors which consist of instrumental, modelling and representativeness errors. The systematic and random modelling errors can be reduced through a careful design of the observation operator (Part I). The impact of the random instrumental and representativeness errors can be decreased by optimizing the processing of the so-called super-observations (spatial averages of raw measurements; Part II).The super-observation processing is experimentally optimized in this article by determining the optimal resolution for the super-observations for different NWP model resolutions. A 1-month experiment with the HIRLAM data assimilation and forecasting system is used for radial wind data monitoring and for generating observation minus background (OmB) differences. The OmB statistics indicate that the super-observation processing reduces the standard deviation of the radial wind speed OmB difference, while the mean vector wind OmB difference tends to increase. The optimal parameter settings correspond at a measurement range of 50 km (100 km) to an averaging area of 1.7 km 2 (7.3 km 2 ).In conclusion, an accurate and computationally feasible observation operator for the Doppler radar radial wind observations is developed (Part I) and a super-observation processing system is optimized (Part II).

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“…The conventional way of calculating the radial wind bias by summing up OmB values at different azimuth directions results in a near zero bias even when there are systematic differences in the modelled and observed wind speed and/or direction. A bias estimation method which accounts for this aspect (Salonen et al, 2007) is thus applied. This method provides a bias estimate for vector wind (i.e.…”

Section: Numerical Testsmentioning

confidence: 99%

Doppler radar radial winds in HIRLAM. Part I: observation modelling and validation

Järvinen

Salonen

Lindskog

et al. 2009

Tellus A: Dynamic Meteorology and Oceanography

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An observation operator for Doppler radar radial wind measurements is developed further in this article, based on the earlier work and considerations of the measurement characteristic. The elementary observation operator treats radar observations as point measurements at pre‐processed observation heights. Here, modelling of the radar pulse volume broadening in vertical and the radar pulse path bending due to refraction is included to improve the realism of the observation modelling. The operator is implemented into the High Resolution Limited Area Model (HIRLAM) limited area numerical weather prediction (NWP) system. A data set of circa 133 000 radial wind measurements is passively monitored against the HIRLAM six‐hourly background values in a 1‐month experiment. No data assimilation experiments are performed at this stage. A new finding is that the improved modelling reduces the mean observation minus background (OmB) vector wind difference at ranges below 55 km, and the standard deviation of the radial wind OmB difference at ranges over 25 km. In conclusion, a more accurate and still computationally feasible observation operator is developed. The companion paper (Part II) considers optimal super‐observation processing of Doppler radar radial winds for HIRLAM, with general applicability in NWP.

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Bias estimation of Doppler‐radar radial‐wind observations

Cited by 18 publications

References 25 publications

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

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

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

Doppler radar radial winds in HIRLAM. Part I: observation modelling and validation

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