Wu, Wanli, Yubao Liu, Linlin Pan, Yuewei Liu, Jason
Knievel, Rong-Shyang Sheu, National Center for Atmospheric Research/RAL, and
John Pace, Scott Halvorson, Frank Gallagher Jr., U.S. Army Test and Evaluation
Command, Dugway, Utah
Advances in scientific
understanding of weather processes, rapid increases in computing power, and
demands on precision fine-scale severe storms forecasting have promoted
numerical weather prediction (NWP) developments toward to cloud-resolving and
large-eddy simulation (LES) scales. Such ultra high resolution NWP possesses
great potential for many weather-critical applications such as prediction of
hazardous releases, wind power, military operations. Liu et al (2011) has
developed a WRF-based multi-scale simultaneous-nestdown data assimilation and
forecasting system, which employs a three-dimensional LES turbulence model at
100s meters grid spacing, and uses an improved nudging scheme in fine scale
data assimilation. The across scale prediction system has been evaluated in
simulating fine scale weather including tornado-like severe storms and
topographic flows in regions with complex terrain. It has demonstrated
robustness and additional skill at LES scales. The system has recently been
adopted for operational wind power forecasting for an offshore wind farm in
South Korea, with nested model grids spacing from 24.3km to 300 meters; has
also been implemented for real-time experimental forecast at the Army Dugway
Proving Ground, UT. In this talk,
we will review the configuration and the special model settings of the DPG VLES
scale NWP system and its skills based on semi-operational forecasting outputs
with intensive observational data for data assimilation and verification. We will also study the impact of the
LES model forecasting on transport and dispersion modeling at DPG.