Poster Session P1.44 The Issue of Data Density and Frequency with EnKF Radar Data Assimilation in a Compressible Nonhydrostatic NWP Model

Monday, 25 June 2007
Summit C (The Yarrow Resort Hotel and Conference Center)
Jidong Gao, CAPS/Univ. of Oklahoma, Norman, Oklahoma; and M. Xue

Handout (452.4 kB)

Data assimilation methods in the context of large-scale hydrostatic flows are reaching a considerable state of maturity. Such techniques sometimes cannot be directly extended to nonhydrostatic flows on the meso- and convective scales because of the differences in the most important data types, in the suitable balance constraints that can be used and differences in the nature of background error covariance. At the latter scales, and especially for intense buoyant convection that is both highly nonhydrostatic and intermittent, the WSR-88D Doppler Radar is the only operational instrument capable of providing high spatial and temporal resolution observations. One of the challenges facing realtime NWP is that the WSR-88D radar network provides a huge amount of data every several minutes across the country. To successfully assimilate these data into numerical models operating at practical resolutions and over large enough domains, data thinning is needed. In this work, we study several strategies for thinning the radar data when assimilating them using the ensemble Kalman filter method through OSSEs with a nonhydrostatic NWP model - Advanced Regional Prediction System (ARPS). The radar data thinning problem is investigated for the cases where the model resolution is higher than, lower than or similar to radar data resolution. In addition, temporal thinning is considered by examining the impact of different frequencies of data usage within given assimilation windows. The general tradeoffs between accuracy and cost associated with different data density/frequency ratios and their impact on the quality of assimilation and subsequent forecasts are assessed.
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner