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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

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  • 1 School of Meteorology and Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma
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Abstract

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.

Corresponding author address: Dr. Ming Xue, School of Meteorology, University of Oklahoma, SEC 1310, 100 E. Boyd, Norman, OK 73019. Email: mxue@ou.edu

Abstract

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.

Corresponding author address: Dr. Ming Xue, School of Meteorology, University of Oklahoma, SEC 1310, 100 E. Boyd, Norman, OK 73019. Email: mxue@ou.edu

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