Highlights of My Research Achievements

1. My collaborative research with several colleagues published in Mon. Wea. Rev. (1999) has been cited by major international meteorological journals over 50 times since it was published. The method proposed in this paper was implemented by the Hurricane Research Division, Atlantic Oceanographic and Meteorological Laboratory (AOML)/NOAA in its real-time Airborne Doppler wind analysis system. Its product has been delivered in real-time to the Environmental Modeling Center and National Hurricane Center since 2008. See official government website: http://www.aoml.noaa.gov/hrd/project2007/doppler.html. In its website, it is stated that the system...has been modified in such a way as to be nearly identical to the method described by Gao et al. 1999. The major finding of the paper is that using the mass continuity equation as a weak instead of strong constraint avoids the error accumulation associated with the explicit vertical integration of the mass continuity equation. Because of this, the updraft in a supercell can be more accurately analyzed and is no longer sensitive to low, and upper boundary conditions.

2. Research work published in J. of Appl. Meteorol. (Gao and Droegemeier, 2004) about de-aliasing Doppler radial velocity data using variational method has been applied to the above AOML/NOAA's system by Dr. Gamache (personal communication, 2008). The key to the proposed method is that, by operating on gradients of velocity rather than on the velocity itself, aliasing ambiguities are readily identified and eliminated. However, most studies in Doppler velocity dealiasing during the past 30 yr have used radial velocity directly and various background and spatial continuity checking along the radial and azimuthal directions. These methods generally suffer shortcomings in extreme weather conditions and come with increased complexity.

3. I have been awarded several NSF grants as Principal Investigator (PI) or Co-PI. Among them, a grant about optimally assimilating WSR-88D radar data into high-resolution NWP model led by myself (awarded in 2003, I was the PI) resulted in some great achievements. The 3DVAR system development (Gao et al. 2004, J. Tech.) supported by this grant has been used for providing initial conditions for very high resolution (1km) WRF model runs using WSR-88D radar data over US continental domain during spring real-time storm-scale forecast experiment at CAPS for NOAA Hazardous Weather Testbed since 2008 (http://forecast.caps.ou.edu). The 3DVAR system has also been used in real-time wind analysis by Center for Collaborative Adaptive Sensing of Atmosphere (CASA, established by the NSF as a Science and Technology Center) for hazardous weather warning purpose using CASA radar network since 2008 (http://www.caps.ou.edu/wx/casa).

4. I proposed An efficient Dual-Resolution (DR) EnKF system for assimilation of radar data to NWP model in early 2006 (published in 2008 Mon. Wea. Rev. with Ming Xue). The key finding is that the spatial scale of background error covariance is typically smoother and larger than that of the analysis increment. This allows us to use an ensemble of forecasts at a lower resolution (LR) to provide the background error covariance estimation for both an ensemble of LR analyses and a single Higher Resolution (HR) analysis. The DR EnKF approach may significantly accelerate the application of EnKF operationally in the future. It has been implemented by Japan Meteorological Agency recently in its LETKF system in a pre-operational environment (Fujita, 4^th EnKF workshop, 2010).

5. A preliminary weather-adaptive 3DVAR analysis system has been developed recently (Gao et al. 2009, AMS conf. on radar Meteorology). In it, a storm positioning program is implemented based on the NSSL's WDSS-II two-dimensional composite reflectivity product. The system has the ability to detect, and analyze severe local hazardous weathers by identifying mesocyclones at high spatial resolution (1km horizontal resolution) and high time frequency (every 5 minutes) using data from the national WSR-88D radar network, and NCEP's mesoscale NAM model product. The analysis can also be performed with realtime on-demand capability with which end-users (or forecasters) can easily set up the location of the analysis domain in realtime based on the current severe weather situation. Although it is still in its early development stage, the system has performed very well during the last one and half month. Many severe weather events, such as Mississippi tornadoes on April 24th, Arkansas Tornadoes on April 26th, and Oklahoma/Kansas tornadoes on May 10^th were all successfully captured and analyzed (http://www.nssl.noaa.gov/users/jgao/public_html/analysis).

References:

Fujita, T. 2010: Development of an Ensemble Data Assimilation System for Mesoscale Ensemble Prediction, The 4^th EnKF Workshop, April 6-9, Rensselaerville, New York.

Gao, J., M. Xue, A. Shapiro, and K. K. Droegemeier 1999: A variational analysis for the retrieval of three-dimensional mesoscale wind fields from two Doppler radars, Mon. Wea. Rev., 127, 2128-2142.

Gao, J., K. K. Droegemeier 2004: A variational technique for dealiasing Doppler radial velocity data, J. Appl. Meteor. 43, 934-940.

Gao, J., M. Xue, K. Brewster, and K. K. Droegemeier 2004: A three-dimensional variational data assimilation method with recursive filter for single-Doppler radar, J. Tech. 21, 457-469.

Gao, J. and M. Xue, 2008: An efficient dual-resolution approach for ensemble data assimilation and tests with assimilated Doppler radar data. Mon. Wea. Rev. 136, 945-963.

Gao, J., D. J. Stensrud, and M. Xue 2009: Three-dimensional Analyses of Several Thunderstorms observed during VORTEX2 field operations. 34th Conference on Radar Meteorology, Willimsburg, VA., Online publication.