P28 Impact of four-dimensional data assimilation (FDDA) on regional and local scale climate downscaling

Pan, Linlin, Yubao Liu, Yuewei Liu, National Center for Atmospheric Research, Lei Li, Shenzhen Meteorology Bureau, Yin Jiang, Shenzhen National Climate Observatory, Will Y. Y. Cheng, Yongxin Zhang, and Gregory Roux, National Center for Atmospheric Research

WRF based FDDA (four dimensional data assimilation) climate analysis is an innovative micro-climate downscaling and analysis system with FDDA technology. A climate FDDA system with four nested domains with grid sizes of 27, 9, 3 and 1 km has been running for Shenzhen area locating at the southern part of China. We did two experiments, one without FDDA and the other with FDDA, to study the impact of data assimilation on the climate downscaling. Impact of data assimilation on the climate analysis and climate dynamical downscaling is investigated with observed stations data in terms of traditional statistics metrics, such as bias, root mean square error (RMSE), and mean absolute error (MAE), both for the domain averaged and for individual stations. The results indicate that the analysis surface fields from FDDA consistently outperform the results from the experiment without FDDA for all the cycles examined. The analysis of high-resolution simulations implies that the significant advantage of the new system in reproducing the micro-climate processes, such as land-sea breezes, complex terrain flows, and local severe convection systems.