期刊
MONTHLY WEATHER REVIEW
卷 142, 期 2, 页码 788-805出版社
AMER METEOROLOGICAL SOC
DOI: 10.1175/MWR-D-13-00063.1
关键词
Data assimilation; Mesoscale models; Model evaluation; performance; Optimization; Reanalysis data
资金
- Bureau of Ocean Energy Management (BOEM) of the U.S. Department of the Interior [M06PC00018]
The Weather Research and Forecasting Model (WRF) and its variational data assimilation system (WRFDA) are applied to the Chukchi-Beaufort Seas and adjacent Arctic Slope region for high-resolution regional atmospheric reanalysis study. To optimize WRFDA performance over the study area, a set of sensitivity experiments are carried out to analyze the model sensitivity to model background errors (BEs) and the assimilation of various observational datasets. Observational data are assimilated every 6 h and the results are verified against unassimilated observations. In the BE sensitivity analyses, the results of assimilating in situ surface observations with a customized, domain-dependent BE are compared to those using the WRF-provided global BE. It is found that the customized BE is necessary in order to achieve positive impacts from WRFDA assimilation for the study area. When seasonal variability is incorporated into the customized BE, the impacts are minor. Sensitivity analyses examining the assimilation of different datasets via WRFDA demonstrate that 1) positive impacts are always seen through the assimilation of in situ surface and radiosonde measurements, 2) assimilating Quick Scatterometer (QuikSCAT) winds improves the simulation of the 10-m wind field over ocean and coastal areas, and 3) selectively assimilating Moderate Resolution Imaging Spectroradiometer (MODIS) retrieved profiles under clear-sky and snow-free conditions is essential to avoid degradation of assimilation performance, while assimilation of Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) retrievals has little impact, most likely due to limited data availability. Based on the sensitivity results, a 1-yr (2009) experimental reanalysis is conducted and consistent improvements are achieved, particularly in capturing mesoscale processes such as mountain barrier and sea-breeze effects.
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