Recent Research and
Development of Ensemble-Variational (EnVar) Hybrid Data Assimilation
for Global, Hurricane and Convective-Scale Severe Weather Prediction
Xuguang Wang
University of Oklahoma
2 Oct, Noon, in 2155
Abstract:
The 3D and 4D ensemble-variational (EnVar) hybrid data assimilation
(DA) system was operationally implemented at NCEP in 2012 and 2016
respectively to improve US NWS global numerical weather prediction
(NWP). This seminar will discuss our most recent research and
development of the EnVar hybrid DA system targeted on improving
operational numerical prediction of a wide range of scales, including
global NWP, convection-allowing hurricane prediction and CONUS
convective-scale severe weather prediction. Such efforts are in close
collaboration with NOAA centers.
In the first part, we discuss two new developments of the global
4DEnVar hybrid DA system including the valid time shifting method to
increase ensemble size and the multi-resolution ensemble 4DEnVar
approach. Experiments over an extended period suggest both methods can
provide a cost-effective way to further improve the global 4DEnVar
analysis and the subsequent global forecasts. Detailed diagnostics will
be shared.
Research and development have also been made to further develop the
hybrid EnVar DA system for convection-allowing regional modeling
systems. In the second part of the seminar, the hybrid DA system is
extended with the operational convection allowing Hurricane Weather
Research and Forecast (HWRF) modeling system to improve high-resolution
tropical cyclone prediction. Experiments and diagnostics have
demonstrated the self-consistent HWRF hybrid DA system can improve the
hurricane intensity forecasts. In addition, recent research using the
HWRF hybrid DA system to identify and diagnose model errors, and to
best assimilate inner core data will be discussed.
In the third part of the seminar, the hybrid DA system is also extended
for convective scale severe weather (e.g. tornadic supercell, MCS,
etc.) prediction over the CONUS. In particular, issues associated with
the direct assimilation of radar reflectivity observations in EnVar are
revealed. A method that allows direct assimilation of radar
reflectivity in EnVar is proposed and implemented. Experiments for a
variety of convective scale weather phenomena including tornadic
supercell and Mesoscale Convective Systems (MCS) in HRRR, NAMRR, WoF
(Warn on Forecast) like contexts are conducted. Experiments revealed
that the new direct radar DA method for EnVar improved the tornadic
supercell prediction, and also improved precipitation forecasts
compared to the current operational method of assimilating the
reflectivity, “the cloud analysis”. If time permits, recent efforts to
assimilate GOES-R radiances for convective scale weather prediction, to
optimally design CAM (convection allowing model) ensemble, and to
design new object based verification for CAM will also be briefly
discussed.