The use of surface observations to retrieve atmospheric boundary layer structure with ensemble Kalman filter

Surface observations are believed to be a very useful source for reconstructing atmospheric boundary layer (ABL) structure in numerical weather prediction (NWP) due to its accuracy and abundance. Many attempts have been made to incorporate these surface observations (e.g., 2-mter temperature and 10-meter winds) into various numerical models in the past. Both progresses and difficulties are documented from previous studies. Specifically, assimilation of 2-meter temperature is a challenging problem in NWP, thus, the 2-meter temperature data has not yet been assimilated in operational NWP models.

The objective of this study is to evaluate the ability of modern ensemble Kalman filter technique in assimilating surface observations. With the weather research and forecasting (WRF) model and an ensemble Kalman filter data assimilation system from NCAR Data Assimilation Research Testbed (DART), a series of data assimilation experiments is performed to examine the impact of surface observations on accurate representation of the ABL structure. Various components of surface observations (2-meter temperature or 10-meter winds) are assimilated and their influence on reconstructing the ABL structural features is evaluated. Different approaches to extend the information from surface to the atmosphere aloft are also explored. Results and issues raised from the experiments are discussed.