A Simple Adjoint Method of Wind Analysis for Single-Doppler Data

Qiu, Chongjian; Xu, Qin

doi:10.1175/1520-0426(1992)009<0588:asamow>2.0.co;2

Cited by 90 publications

(46 citation statements)

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“…The mean winds determined from successive volume scans based on the principle of tracking quasi-conserved quantities (e.g., Qiu and Xu 1992;Shapiro et al 1995) can definitely benefit from higherfrequency data. Shapiro et al (2003) find that with a SDVR scheme based on a Lagrangian form of the radial component of the equation of motion, the wind retrieval error statistics are substantially improved because the volume scan intervals decrease from 5 min down to 1 min, using real Doppler-on-Wheels mobile radar data.…”

Section: Impact Of Volume Scan Intervalmentioning

confidence: 99%

An OSSE Framework Based on the Ensemble Square Root Kalman Filter for Evaluating the Impact of Data from Radar Networks on Thunderstorm Analysis and Forecasting

Xue

Tong

Droegemeier

2006

Journal of Atmospheric and Oceanic Technology

147

121

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A framework for Observing System Simulation Experiments (OSSEs) based on the ensemble square root Kalman filter (EnSRF) technique for assimilating data from more than one radar network is described. The system is tested by assimilating simulated radial velocity and reflectivity data from a Weather Surveillance Radar-1988 Doppler (WSR-88D) radar and a network of four low-cost radars planned for the Oklahoma test bed by the new National Science Foundation (NSF) Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA). Such networks are meant to adaptively probe the lower atmosphere that is often missed by the existing WSR-88D radar network, so as to improve the detection of low-level hazardous weather events and to provide more complete data for the initialization of numerical weather prediction models.Different from earlier OSSE work with ensemble Kalman filters, the radar data are sampled on the radar elevation levels and a more realistic forward operator based on the Gaussian power-gain function is used. A stretched vertical grid with high vertical resolution near the ground allows for a better examination of the impact of low-level data. Furthermore, the impacts of storm propagation and higher-volume scan frequencies up to one volume scan per minute on the quality of analysis are examined, using a domain of a sufficient size. The generally good analysis compared to earlier work indicates that the filter can effectively handle the non-uniform-resolution data on the radar elevation levels.The assimilation of additional data from a well-positioned (relative to the storm) CASA radar improves the analysis of a supercell storm system that uses data from one WSR-88D radar alone; and the improvement is most significant at the low levels. When data from a single CASA radar are assimilated and when the radar does not provide full coverage of the storm system, significant errors develop in the analysis that cannot be effectively corrected. The combination of three CASA radars produces analyses of similar quality as the combination of one WSR-88D radar and one well-positioned CASA radar.The most significant effect of storm propagation speed appears to be on the data coverage, which in turn affects the analysis quality. It is generally true that the more observations, the better the analysis. The results of the EnSRF assimilation are not very sensitive to the propagation speed. The quality of analysis can be improved by employing faster volume scans. The sensitivity of the EnSRF analysis to the volume scan interval is however much less than that of traditional velocity and thermodynamic retrieval schemes, suggesting the superiority of the EnSRF method compared to traditional methods. The very frequent update of the model state by the filter, even at 1-min intervals, does not show any negative effect, indicating that the analyzed fields are well balanced.

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Section: Impact Of Volume Scan Intervalmentioning

confidence: 99%

An OSSE Framework Based on the Ensemble Square Root Kalman Filter for Evaluating the Impact of Data from Radar Networks on Thunderstorm Analysis and Forecasting

Xue

Tong

Droegemeier

2006

Journal of Atmospheric and Oceanic Technology

147

121

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“…These methods range from purely kinematic to sophisticated 4D variational method (4DVAR) that employs a nonhydrostatic prediction model and its adjoint (e.g., Gal-Chen 1978;Sun et al 1991;Qiu and Xu 1992;Shapiro et al 1995;Sun and Crook 1997;Gao et al 1999;Wu et al 2000;Weygandt et al 2002). Most of the work deals with retrieval and assimilation of radial velocity and/or reflectivity data from single Doppler radar because dual or multiple-Doppler coverage is not generally available.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Kalman Filter Assimilation of Doppler Radar Data with a Compressible Nonhydrostatic Model: OSS Experiments

Tong

Xue

2005

Monthly Weather Review

370

312

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A Doppler radar data assimilation system is developed based on ensemble Kalman filter (EnKF) method and tested with simulated radar data from a supercell storm. As a first implementation, we assume the forward models are perfect and radar data are sampled at the analysis grid points. A general purpose nonhydrostatic compressible model is used with the inclusion of complex multi-class ice microphysics. New aspects compared to previous studies include the demonstration of the ability of EnKF method in retrieving multiple microphysical species associated with a multi-class ice microphysics scheme, and in accurately retrieving the wind and thermodynamic variables. Also new are the inclusion of reflectivity observations and the determination of the relative role of radial velocity and reflectivity data as well as their spatial coverage in recovering the full flow and cloud fields. In general, the system is able to reestablish the model storm extremely well after a number of assimilation cycles, and best results are obtained when both radial velocity and reflectivity data, including reflectivity information outside precipitation regions, are used. Significant positive impact of the reflectivity assimilation is found even though the observation operator involved is nonlinear. The results also show that a compressible model that contains acoustic modes hence the associated error growth performs at least as well as an anelastic model used in previous EnKF studies at the cloud scale.Flow-dependent and dynamically consistent background error covariances estimated from the forecast ensemble play a critical role in successful assimilation and retrieval. When the assimilation cycles start from random initial perturbations, better results are obtained when one withholds during the first few cycles the updating of the fields that are not directly related to radar reflectivity. In fact, during the first few cycles, updating of variables indirectly related to reflectivity hurts the analysis. This is so because the estimated background covariances are unreliable at this stage of data assimilation, which is related to the way the forecast ensemble is initialized. Forecasts of supercell storms starting from the best assimilated initial conditions are shown to remain very good for at least two hours.1

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“…The availability in real-time full- volume, full-resolution (level II) radar data (Crum et al 2003) and the important improvements in data assimilation techniques have also paved the way for radar data assimilation. Research efforts have been made in the last decade with an attempt to extract and assimilate meteorological information from Doppler radar observations into NWP models to improve the model capability of forecasting severe weather events (Shapiro et al 1995(Shapiro et al , 2003Qiu and Xu 1996;Sun 2004Sun , 2005Sun andCrook 1997,1998;Xu et al 1995Xu et al , 2001aGao et al 1999;Wu et al 2000;Weygandt et al 2002a,b;Snyder and Zhang 2003;Xiao et al 2005). While these studies were done mostly for isolated thunderstorms and mesoscale convective systems, it should be feasible and practical to apply these techniques to TC structure and intensity analyses and predictions.…”

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confidence: 99%