气候变化领域集成服务门户(CCPortal): Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF-Chem/DART

CCPortal

DOI	10.1029/2019JD031465
	Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF-Chem/DART
	Ma C.; Wang T.; Jiang Z.; Wu H.; Zhao M.; Zhuang B.; Li S.; Xie M.; Li M.; Liu J.; Wu R.
发表日期	2020
ISSN	2169897X
卷号	125 期号:1
英文摘要	Three types of observations, aerosol optical depth from the Moderate Resolution Imaging Spectroradiometer, surface particulate matter with diameters less than 2.5 (PM2.5) and 10 μm (PM10), and aerosol extinction coefficient (AEXT) profiles from ground-based lidars, were separately and simultaneously assimilated using the Weather Research and Forecasting Model with the Chemistry/Data Assimilation Research Testbed (WRF-Chem/DART). Two cases in June and November 2018 were selected over middle and eastern China. Experiments assimilating single-type and multiple observations were evaluated by cross validating their analysis and forecast against the three observation types. Compared to the experiment without data assimilation (DA), DA of single-type observations is always closer to the type of observations assimilated. However, DA of aerosol optical depth or AEXT sometimes significantly degraded the error performance for PM2.5. This problem is caused by the inconsistency of bias tendencies when modeling aerosol optical properties and surface aerosol mass. It is found that WRF-Chem tends to predict dryer air within the boundary layer over eastern China, which may have played a role in the underestimation of AEXT even when PM2.5 was overestimated. After applying a simple bias correction (BC), the problem was alleviated. DA of multiple observations with BC gives the best overall error performance when validated against all types of observations and even performs better than any DA of single-type observations experiments in reproducing AEXT profiles. The results illustrate that BC is important in DA of multiple observations and that the simultaneous DA of aerosol observations with different vertical information can work synergistically to improve aerosol forecasts. ©2019. American Geophysical Union. All Rights Reserved.
英文关键词	bias correction; lidar aerosol extinction profile; MODIS aerosol optical depth; multiple observations data assimilation; near-surface particulate matter
语种	英语
来源期刊	Journal of Geophysical Research: Atmospheres
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/186240
作者单位	School of Atmospheric Sciences, Nanjing University, Nanjing, China; Jiangsu Environmental Monitoring Center, Nanjing, China; International Institute for Earth System Science, Nanjing University, Nanjing, China; Department of Geography and Planning, University of Toronto, Toronto, Canada
推荐引用方式 GB/T 7714	Ma C.,Wang T.,Jiang Z.,et al. Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF-Chem/DART[J],2020,125(1).
APA	Ma C..,Wang T..,Jiang Z..,Wu H..,Zhao M..,...&Wu R..(2020).Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF-Chem/DART.Journal of Geophysical Research: Atmospheres,125(1).
MLA	Ma C.,et al."Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF-Chem/DART".Journal of Geophysical Research: Atmospheres 125.1(2020).