Challenges and progress in radar data quality control and assimilation

Qin Xu
11 am May 27 in Room 2155

Radar observations have been very useful and often critical for forecasters to issue timely warnings and outlooks of severe weathers. Assimilating radar observations into a numerical weather prediction system can be more useful and critical for forecasting severe weathers, but the involved tasks are very challenging and require rigorous data quality controls (QCs). Toward this goal, dedicated efforts have been undertaken at NSSL to develop high-standard QCs for radar data assimilation at EMC. In particular, a suite of velocity dealiasing techniques has been developed adaptively for various scan modes applied to different weathers. In these techniques, each reference velocity field is produced by an alias-robust analysis in which the global minimization problem for analyzing aliased velocities is formulated in terms of Bayesian estimation by folding the domain of the non-aliased velocity probability density function into the Nyquist interval. The successively improved performances of these dealiasing techniques will be highlighted with discussions on remaining and newly encountered difficulties. Along with the aforementioned efforts, a radar wind analysis system has been also developed to process radar data, detect data quality problems, test radar data QC and assimilation techniques, and produce high-resolution vector winds for nowcast applications. On-going improvements to this system on tornadic mesocyclone wind analysis, multi-scale/multistep variational analysis, and variational-ensemble approach with time-expanded sampling will be discussed along with challenging issues in storm-scale data assimilation.