Challenges and
progress in radar data quality control and assimilation
Qin Xu
NOAA/NSSL
11 am May 27 in Room 2155
Abstract:
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.