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16
2003: The Advanced Regional Prediction System (ARPS), stormscale numerical weather prediction and data assimilation
 Meteor. Atmos. Physics
"... Storms (CAPS) was established at the University of Oklahoma as one of the National Science Foundation’s first 11 ..."
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Cited by 168 (98 self)
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Storms (CAPS) was established at the University of Oklahoma as one of the National Science Foundation’s first 11
Ensemble Kalman Filter Assimilation of Doppler Radar Data with a Compressible Nonhydrostatic Model: OSS Experiments
, 2004
"... 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 pur ..."
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Cited by 127 (78 self)
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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 multiclass 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 multiclass 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
2006a: 3DVAR and cloud analysis with WSR88D levelII data for the prediction of Fort Worth tornadic thunderstorms. Part I: Cloud analysis and its
 Mon. Wea. Rev
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Droegemeir, 2002: Retrieval of model initial fields from singleDoppler observations of a supercell thunderstorm. Part I: SingleDoppler velocity retrieval
 Mon. Wea. Rev
"... In this twopart study, a singleDoppler parameter retrieval technique is developed and applied to a realdata case to provide model initial conditions for a shortrange prediction of a supercell thunderstorm. The technique consists of the sequential application of a singleDoppler velocity retrieva ..."
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Cited by 31 (6 self)
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In this twopart study, a singleDoppler parameter retrieval technique is developed and applied to a realdata case to provide model initial conditions for a shortrange prediction of a supercell thunderstorm. The technique consists of the sequential application of a singleDoppler velocity retrieval (SDVR), followed by a variational velocity adjustment, a thermodynamic retrieval, and a moisture specification step. In Part I, the SDVR procedure is described and results from its application to a supercell thunderstorm are presented. In Part II, results from the thermodynamic retrieval and the numerical model prediction for this same case are presented. For comparison, results from parallel sets of experiments using dualDopplerderived winds and winds obtained from the simplified velocity retrieval described in Part I are also shown. Following the SDVR, the retrieved wind fields (available only within the storm volume) are blended with a basestate background field obtained from a proximity sounding. The blended fields are then variationally adjusted to preserve anelastic mass conservation and the observed radial velocity. A GalChen type thermodynamic retrieval procedure is then applied to the adjusted wind fields. For all experiments (full retrieval, simplified retrieval, and dual Doppler), the resultant perturbation pressure and potential temperature fields agree qualitatively with expectations for a deepconvective storm. An analysis of the magnitude of the various terms in the vertical
2005a: Efficient assimilation of radar data at high resolution for shortrange numerical weather prediction
 World Weather Research Program Symposium on Nowcasting and Very ShortRange Forecasting, WSN05, Tolouse, France, WMO, Symposium CD, Paper 3.06
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2006: A comparison of the radar ray path equation and approximations for use in radar data assimilation, adv
 Adv. Atmos. Sci
"... The radar ray path equations are used to determine the physical location of each radar measurement. These equations are necessary for mapping radar data to computational grids for diagnosis, display and numerical weather prediction (NWP). They are also used to determine the forward operators for ass ..."
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Cited by 11 (8 self)
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The radar ray path equations are used to determine the physical location of each radar measurement. These equations are necessary for mapping radar data to computational grids for diagnosis, display and numerical weather prediction (NWP). They are also used to determine the forward operators for assimilation of radar data into forecast models. In this paper, a stepwise ray tracing method is developed. The influence of the atmospheric refractive index on the ray path equations at different locations related to an intense cold front is examined against the ray path derived from the new tracing method. It is shown that the radar ray path is not very sensitive to sharp vertical gradients of refractive index caused by the strong temperature inversion and large moisture gradient in this case. In the paper, the errors caused by using the simplified straight ray path equations are also examined. It is found that there will be significant errors in the physical location of radar measurements if the earth’s curvature is not considered, especially at lower elevation angles. A reduced form of the equation for beam height calculation is derived using Taylor series expansion. It is computationally more efficient and also avoids the need to use double precision variables to mitigate the small difference between two large terms in the original form. The accuracy of this reduced form is found to be sufficient for modeling use. Key words: Doppler radar, ray path equations, refractivity index, data assimilation
Impacts of Beam Broadening and Earth Curvature on Stormscale 3D Variational Data Assimilation of Radial Velocity with Two Doppler Radars
"... The radar ray path and beam broadening equations are important for assimilation of radar data into Numerical Weather Prediction (NWP) models. They can be used to determine the physical location of each radar measurement, and for properly mapping the atmospheric state variables from the model grid to ..."
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Cited by 5 (3 self)
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The radar ray path and beam broadening equations are important for assimilation of radar data into Numerical Weather Prediction (NWP) models. They can be used to determine the physical location of each radar measurement, and for properly mapping the atmospheric state variables from the model grid to the radar measurement space as part of the forward observation operators. Historically, different degrees of approximations have been made with these equations but no systematic evaluation of their impact exists, at least in the context of variational data assimilation. This study examines the effects of simplifying ray path and ray broadening calculations on the radar data assimilation in a 3D variational (3DVAR) system. Several groups of Observational System Simulation Experiments (OSSEs) are performed to test the impact of these equations to radar data assimilation with an idealized tornadic thunderstorm case. Our study shows that the errors caused by simplifications vary with the distance between the analyzed storm and the radar. For single time level wind analysis, as the surface range increases, the impact of beam broadening on analyzed wind field becomes evident and can cause relatively large error for distances beyond 150 km. The impact of earth’s curvature is more significant even
2001: Application of the ZhangGalChen singleDoppler velocity retrieval to a deep convective storm
 J. Atmos. Sci
"... The Zhang–GalChen singleDoppler velocity retrieval (SDVR) technique is applied to a multicell storm observed by three radars near the Orlando, Florida, airport on 9 August 1991. This dataset is unique in that 3min volume scans at very high spatial resolution (200 m) are available during a 24min ..."
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Cited by 4 (3 self)
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The Zhang–GalChen singleDoppler velocity retrieval (SDVR) technique is applied to a multicell storm observed by three radars near the Orlando, Florida, airport on 9 August 1991. This dataset is unique in that 3min volume scans at very high spatial resolution (200 m) are available during a 24min period. The retrieved (unobserved) wind, determined using only the radial wind and reflectivity from one of the radars, is compared to the (observed) winds obtained from a hybrid threedimensional wind synthesis. Error statistics demonstrate that the retrievals perform best when applied in a reference frame moving with the storm; however, the results also show that the specification of this frame is problematic. The findings also indicate that, in an environment where the mean flow has a critical layer, the moving reference frame is best defined as a function of height rather than a volume mean. The benefit of such a reference frame is case dependent and is best realized in regions such as a surface cold pool or upperlevel divergence at storm top. Error statistics demonstrate that the SDVR technique recovers the horizontal wind with greater accuracy than it does the vertical velocity—suggesting that for deep convection, the absence of dynamical constraints is critical. The kinematic and O’Brien techniques and a new variational technique, in which the solution to a secondorder ordinary differential equation for the vertical velocity is expressed in terms of Bessel functions, are tested as possible alternatives to the SDVR vertical velocity. Results indicate that this new technique yields vertical velocities significantly better than those using the other three methods. 1.
A Methodology For Developing High Performance Computing Models: StormScale Weather Prediction
 The Society for Computer Simulation
, 1993
"... A methodology for developing future generations of a stormscale weather prediction model for Massively Parallel Processing is described. The forecast model is the Advanced Regional Prediction System (ARPS), a threedimensional, fully compressible, nonhydrostatic predictive model. In the short term ..."
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Cited by 3 (0 self)
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A methodology for developing future generations of a stormscale weather prediction model for Massively Parallel Processing is described. The forecast model is the Advanced Regional Prediction System (ARPS), a threedimensional, fully compressible, nonhydrostatic predictive model. In the short term, the computational goals include developing a portable, scalable model for distributed memorySIMD and MIMD architectures, while preserving a high degree of modularity to support rapid design and validation, maintainability, educational goals and operational testing. Longer term computational goals include a parallel adaptive mesh refinement scheme. A FortranD/High Performance Fortran version of the ARPS provides portability in the current version of the model, and supports future model research goals. INTRODUCTION We present a survey of issues by the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma, and the Northeast Parallel Architectures Center (NPAC) at S...
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, 2001
"... An objective method of determining and correcting phase or position errors in numerical weather prediction is described and tested in a radar data observing system simulation experiment (OSSE) addressing the forecasting of ongoing thunderstorms. Such phase or position errors are common in numerical ..."
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An objective method of determining and correcting phase or position errors in numerical weather prediction is described and tested in a radar data observing system simulation experiment (OSSE) addressing the forecasting of ongoing thunderstorms. Such phase or position errors are common in numerical forecasts at grid resolutions of 1to20 km (mesoγ scale). It is proposed that the process of correcting a numerical forecast field can be simplified if such errors are addressed directly. An objective method of determining the phase error in the forecast by searching for a field of shift vectors that minimizes a squarederror difference from highresolution observations is described. Three methods of applying a phase error correction to a forecast model are detailed. The first applies the entire correction at the initial time, the second in discrete steps during an assimilation window, and the third applies the correction continuously through the model’s horizontal advection process. It is shown that the phase correction method is effective in producing an analysis field that agrees with the data yet preserves the structure developed by the model. The three methods of assimilating the correction in the forecast are successful, and a longterm positive effect on the thunderstorm simulation is achieved in the simulations, even as the modeled storms go through a cycle of decline and regeneration. 2