84th AMS Annual Meeting

Monday, 12 January 2004
Assimilation of ground-based GPS PWV with 3DVAR system for a IHOP case
Room 4AB
Y.-R. Guo, NCAR, Boulder, CO; and H. Kusaka, D. M. Barker, Y. -. H. Kuo, and A. Crook
Poster PDF (84.3 kB)
In the United States, the ground-based GPS PWV (Precipitable Water Vapor) measurements are now available hourly in real-time (about 50-min after the observation time) from three networks: SOUMInet, FSLnet, and CPRSnet. Meanwhile the MM5 3DVAR system is ready to be used to assimilate the conventional and non-conventional observations. How to assimilate the frequent measurements of GPS PWV with 3DVAR in improving the mesoscale convection forecast is a challenging problem.

A convective case (12 June 2002) during IHOP IOP was chosen in this study. A series of experiments are conducted to identify the impact of GPS PWV on the NWP forecast, and to get an optimal assimilation strategy with the 3DVAR system. The "cold-start" assimilation and the 3-h 3DVAR cycling-run experiments were conducted. But the 3DVAR "cold-star" assimilation with an default interpolated background error statistics (BES) from a gloabl 210-km BES file did not show a positive impact on the convection forecast in compared with the control run initialized from Eta analysis. Then, we proposed a approach to re-construct a new BES. With the new BES, the "cold-start" 3DVAR assimilation gave the improved forecast skills, and assimilation of the GPS PWV shows the positive impact on the convection forecast in this study.

To exploit the information from the high frequent GPS PWV data, the 3DVAR Cycling-run experiments are also performed. Because there is limited observations available in NCAR archive, and maybe the forecast model errors existed, the 3-h 3DVAR Cycling-run experiments did not show the better forecast skills than the cold-start runs. However, assimilation of the GPS PWV also demonstrated the positive impact on the convection forest. More 3DVAR cycling-run experiments with more observations and the next-generation mesoscale model (WRF) may be needed in future.

Supplementary URL: http://