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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 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
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, 2003
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Cited by 14 (4 self)
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See next page for additional authors Follow this and additional works at:
2006b: Variational analysis of over-sampled dual-Doppler radial velocity data and application to the analysis of tornado circulations
- J. Atmos. Ocean Tech., Under review
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Impacts of Beam Broadening and Earth Curvature on Storm-scale 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
Simultaneous retrieval of microphysical parameters and atmospheric state variables with radar data and ensemble Kalman filter method
- PREPRINT, 17TH CONF. NUM. WEA. PRED., WASHINGTON DC, AMER. METEOR. SOC., CDROM P1.30
, 2005
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2007: A Doppler radar emulator with an application to the detectability of tornadic signatures
- J. Atmos. Oceanic Technol
, 1973
"... A Doppler radar emulator was developed to simulate the expected mean returns from scanning radar, including pulse-to-pulse variability associated with changes in viewing angle and atmospheric structure. Based on the user’s configuration, the emulator samples the numerical simulation output to produc ..."
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Cited by 5 (2 self)
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A Doppler radar emulator was developed to simulate the expected mean returns from scanning radar, including pulse-to-pulse variability associated with changes in viewing angle and atmospheric structure. Based on the user’s configuration, the emulator samples the numerical simulation output to produce simulated returned power, equivalent radar reflectivity, Doppler velocity, and Doppler spectrum width. The emulator is used to evaluate the impact of azimuthal over- and undersampling, gate spacing, velocity and range aliasing, antenna beamwidth and sidelobes, nonstandard (anomalous) pulse propagation, and wavelength-dependent Rayleigh attenuation on features of interest. As an example, the emulator is used to evaluate the detection of the circulation associated with a tornado simulated within a supercell thunderstorm by the Advanced Regional Prediction System (ARPS). Several metrics for tornado intensity are examined, including peak Doppler velocity and axisymmetric vorticity, to determine the degradation of the tornadic signature as a function of range and azimuthal sampling intervals. For the case of a 2 ° half-power beamwidth radar, like those deployed in the first integrated project of the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA), the detection of the cyclonic shear associated with this simulated tornado will be difficult beyond the 10-km range, if standard metrics such as
COMPARISON BETWEEN DOW OBSERVED TORNADOES AND PARENT MESOCYCLONES OBSERVED BY WSR-88Ds
"... The typical inter-radar spacing and operational volumetric coverage patterns of about 30 WSR-88Ds across the approximately 1.5 million square km of the United States plains often precludes ..."
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Cited by 3 (2 self)
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The typical inter-radar spacing and operational volumetric coverage patterns of about 30 WSR-88Ds across the approximately 1.5 million square km of the United States plains often precludes
Identifying precursors to strong lowlevel rotation within numerically simulated supercell thunderstorms: A data mining approach
, 2008
"... I am truly grateful to those who have assisted in this research effort and for the incredible support of so many others who have helped bring me to where I am today. I first would like to thank my advisor and committee chair Dr. Kelvin Droegemeier for giving me this opportunity and for the support a ..."
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I am truly grateful to those who have assisted in this research effort and for the incredible support of so many others who have helped bring me to where I am today. I first would like to thank my advisor and committee chair Dr. Kelvin Droegemeier for giving me this opportunity and for the support and encouragement he has provided as well as for being both a mentor and a friend. I would also like to thank my co-committee chair Dr. Amy McGovern for bringing her vast knowledge of computer science and data mining to the project. This was a highly crossdisciplinary, collaborative effort and could not have been a success without her involvement. Thanks also go to Dr. Rodger Brown, a committee member, who provided guidance and advice over every aspect of the project. I also thank committee member Dr. Howard Bluestein for the advice he provided. Additional assistance was provided by students working under Dr. McGovern
An Automated Method for Depicting Mesocyclone Paths and Intensities
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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
, 2005
"... 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 S ..."
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Cited by 1 (0 self)
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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 frequen-cies up to one volume scan per minute on the quality of analysis are examined, using a domain of a sufficient
