Effects of Radar Sampling on Single-Doppler Velocity Signatures of Mesocyclones and Tornadoes

147

121

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.

Section: ͑2͒mentioning

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

147

121

“…Most radar data assimilation studies do not consider this beam broadening effect. Wood and Brown (1997) introduced a power-gain weighted average in the radar forward observation operator in their study on the effects of radar sampling on velocity signatures of mesocylones and tornadoes. Sun and Crook (2001) incorporated a similar beam broadening equation in their 4DVAR radar analysis system.…”

Section: Introductionmentioning

confidence: 99%

Impacts of Beam Broadening and Earth Curvature on Storm-Scale 3D Variational Data Assimilation of Radial Velocity with Two Doppler Radars

Gao

Brewster

et al. 2010

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 to properly map 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; however, 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 data assimilation (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. This 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 the earth's curvature is more significant, even for distances beyond 60 km, because it places the data at the wrong vertical location. The impact of refractive index gradient is also tested. It is shown that the variations of refractive index gradient have a very small impact on the wind analysis results.Two time series of 1-h-long data assimilation experiments are further conducted to illustrate the impact of the beam broadening and earth curvature on all retrieved model variables. It is shown that all model variables can be retrieved to some degrees in all data assimilation experiments. Similar to the wind analysis experiments, the impacts of both factors are not obvious when radars are relatively close to the storm. When the radars are far from the storm (especially beyond 150 km), overlooking beam broadening degrades the accuracy of assimilation results slightly, whereas ignoring the earth's curvature leads to significant errors.

“…For simplicity, either the problem is assumed to be two-dimensional, or there is no variation in the vertical direction and the radar beams are horizontal. The same assumptions are made in Wood and Brown (1997), although our method and results can be easily generalized to three dimensions.…”

Section: A Radar Data Sampling and Observation Operatormentioning

confidence: 99%

“…The antenna motion will produce azimuthal smearing so that the effective beamwidth is wider than the physical beamwidth. Moreover, the resolution volumes of individual samples are continuously shifted in azimuth (Doviak and Zrnic 1993;Wood and Brown 1997).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Variational Analysis of Oversampled Dual-Doppler Radial Velocity Data and Application to the Analysis of Tornado Circulations

Xue

Liu

2007

For the detection of severe weather phenomena such as tornados, mesocyclones, and strong wind shear, the azimuthal resolution of radial velocity measurements is more important. The typical azimuthal resolutions of 1°for the Weather Surveillance Radar-1988 Doppler (WSR-88D) radars and of 2°for the planned Center for Collaborative Adaptive Sensing of Atmosphere (CASA) radars are not sufficient for this purpose, especially at far ranges. Oversampling is one strategy that can potentially provide more details about the azimuthal structures of flows, and can be achieved by processing raw data at azimuthal increments smaller than the radar beamwidth. In the presence of dual-Doppler observations, the variational method can be used to effectively recover subbeamwidth structures from these oversampled data, which, combined with the typically higher range resolutions, can provide high-resolution wind analyses that are valuable for, for example, tornado detection. This idea is tested in this paper using simulated data as well as reprocessed level-I data from a research WSR-88D radar, for model-simulated and actually observed tornadoes, respectively. The results confirm that much more detailed, often subbeamwidth, flow structures can indeed be recovered through azimuthal oversampling and a properly configured variational analysis, and the detailed flow analysis is expected to significantly improve one's ability in identifying small-scale features such as tornadoes from radial velocity observations.