Hou, Tuanjie, Fanyou Kong and Xunlai Chen, University
of Oklahoma
To improve the accuracy
of short-term (0-12h) forecasts of severe weather in Southern China, a realtime
storm-scale forecasting system based on the WRF-ARW model and the ARPS
3DVAR/Cloud Analysis system has been developed collaboratively by the Center
for Analysis and Prediction of Storms (CAPS) in University of Oklahoma,
Shenzhen Meteorological Bureau (SZMB), and the Shenzhen Institute of Advanced
Technology (SIAT), Chinese Academy of Sciences, and in operation since March
2010. The forecast system consists of an outer domain with 12-km horizontal
grid spacing and a one-way nested high-resolution domain at 4-km grid spacing.
Initialized from the fine-resolution ECMWF data, the forecast system was
initially characterized by assimilating radar reflectivity and radial wind from
local WSR-98 radars every hour in realtime. To provide more realistic synoptic
settings for the storm-scale forecasts, surface (AWS) and rawinsonde
observations including temperature, dew point, pressure and wind profiles were
assimilated in two heavy rainfall cases in Southern China., A series of
experiments including assimilation of radar data only, both radar and AWS data,
and both radar and rawinsonde data were conducted. The impact of various
observation data assimilation on quantitative precipitation forecasting was
examined. The equitable threat scores (ETS) of 1-, 3- 6- and 12-h accumulated
precipitation from the 4-km domain were computed against rain gauge measurement
from over 1000 stations to evaluate forecast skills.