Basic Convective and Mesoscale Research
NOAA HPCC/NCEP/Other Agency – Doppler Radar Data Quality Control, Analyses, and Assimilation
Funding Type: Office of Naval Research, FAA, NSSL Director’s Discretional Research Fund
Advance knowledge and skill in storm-scale data assimilation; develop state-of-the-art technologies and software for real-time applications of remotely sensed high-resolution measurements, especially those from Doppler radars, to improve numerical nowcasts and forecasts of severe storms and hazardous weather conditions..
Estimating radar wind observation error and NCEP WRF background wind error covariances from radar radial-velocity innovations. By using the non-isotropic form of error covariance function derived for radialvelocity fields on conical surfaces of low-elevation radar scans, a statistical method was developed based on least-square fitting to estimate Doppler radar radial-velocity observation error covariance and background vector wind error covariance from time series of radar radial-velocity innovation (observation minus independent background) fields. The method is applied to radar radial-velocity innovation data collected from six radars (KINX, KLZK, KSGF, KSRX, KTLX, and KVNX) from 11:00 to 23:50 UTC on 21 May 2005 under a clear but windy weather condition. The background wind fields were provided by NCEP WRF 3-hour forecasts (on a 321x161x61 E-grid with a sigma vertical coordinate and 8 km horizontal resolution) over the central U.S. The results show that radar radial-velocity observation errors are correlated between neighboring range gates and between neighboring beams. The radial-velocity observation error correlations can be quite consistently estimated along with the observation error variances and background wind biases and covariances by the method. The detailed results were presented at the 18th Conference on Numerical Weather Prediction (1B.3 by Xu et al. 2007, available online from AMS site http://ams.confex.com/ams/pdfpapers/123419.pdf).
Time-expanded sampling for ensemble-based filters. A time-expanded sampling approach was proposed for ensemble-based filters with covariance localization in data assimilation. This approach samples a series of perturbed state vectors from each prediction run within a subsynoptic-scale time window in the vicinity of the analysis time. As all the sampled state vectors are used to construct the ensemble and compute the localized covariance, the number of required prediction runs can be much smaller than the ensemble size, and this can reduce the computational cost significantly. The conventional approach, however, requires the number of prediction runs to be as large as the ensemble size, so the ensemble size can be severely limited by the computational cost for an intended operational application. By properly setting the sampling time interval, the proposed approach can improve the ensemble spread and ensemble representation of the forecast probability distribution and thus improve the filter performance even thought the number of prediction runs is greatly reduced. The potential merits of the proposed timeexpanded sampling are demonstrated by assimilation experiments. The detailed results were presented at the 18th Conference on Numerical Weather Prediction (6B.1A by Xu et al. 2007, see http://ams.confex.com/ams/pdfpapers/123409.pdf).
These projects are ongoing.
Liu, S., M. Xue, and Q. Xu, 2007: Using wavelet analysis to detect tornadoes from Doppler radar radial-velocity observations. J. Atmos. Oceanic Technol., 24, 344-359.
Qiu, C., A. Shao, Q. Xu, and L. Wei, 2007: Fitting model fields to observations by using singular value decomposition – An ensemble-based 4DVar approach. J. Geophys. Res., 112, No. D11, D11105, doi:10.1029/2006JD007994.
Xu, Q., K. Nai, and L. Wei, 2007: An innovation method for estimating radar radial-velocity observation error and background wind error covariances. Quart. J. Roy. Meteor. Soc., 133, 407-415.
Xu, Q., S. Liu, and M. Xue, 2006: Background error covariance functions for vector wind analyses using Doppler radar radialvelocity observations. Quart. J. Roy. Meteor. Soc., 132, 2887-2904.
Pinto, J., C. Kessinger, B. Hendrickson, D. Megenhardt, P. Harasti, Q. Xu, P. Zhang, Q. Zhao, M. Frost, J. Cook, and S. Potts, 2007: Storm characterization and short term forecasting potential using a phase array radar. 33rd Conf. on Radar Meteorology. 6–10 August 2007, Cairns, Australia. Amer. Meteor. Soc., P5.18.
Qiu, C., A. Shao, Q. Xu,, Li Wei, 2007: An Ensemble-Based 4DVar Approach Based on SVD Technique.. 18th Conf. on Numerical Weather Prediction. 25-29 June 2007, Park City, UT. Amer. Meteor. Soc., P2.2.
Xu, Q., L. Wei, H. Lu, K. Nai, and Q. Zhao, 2006: Phased-array radar data assimilation at the National Weather Radar Testbed – Theoretical issues and practical solutions. Fourth European Conf. on Radar Meteorology, 18-22 September 2006, Barcelona, Spain, CD-ROM 9.3.
Xu, Q., K. Nai, L. Wei, H. Lu, P. Zhang, S. Liu, D. Parrish, 2007: Estimating radar wind observation error and NCEP WRF background wind error covariances from radar radial-velocity innovations. 18th Conf. on Numerical Weather Prediction. 25-29 June 2007, Park City, UT. Amer. Meteor. Soc., 1B.3.
Xu, Q., L. Wei, H. Lu, Q. Zhao, and C. Qiu, 2007: Time-expanded sampling for ensemble-based filter with covariance localization: assimilation experiments with a shallow-water equation model. 18th Conf. on Numerical Weather Prediction. 25-29 June 2007, Park City, UT. Amer. Meteor. Soc., 6B.1A.
Xu, Q., H. Lu, L. Wei, and Q. Zhao, 2007: Studies of phased-array scan strategies for radar data assimilation. 33rd Conf. on Radar Meteorology. 6–10 August 2007, Cairns, Australia. Amer. Meteor. Soc., 4A.3.