Abstract
Aerosol optical depth (AOD) is the most basic parameter that describes the optical properties of atmospheric aerosols, and it can be used to indicate aerosol content. In this study, we assimilated AOD data from the Fengyun-3A (FY-3A) and MODIS meteorological satellite using the Gridpoint Statistical Interpolation three-dimensional variational data assimilation system. Experiments were conducted for a dust storm over East Asia in April 2011. Each 0600 UTC analysis initialized a 24-hWeather Research and Forecasting with Chemistry model forecast. The results generally showed that the assimilation of satellite AOD observational data can significantly improve model aerosol mass prediction skills. The AOD distribution of the analysis field was closer to the observations of the satellite after assimilation of satellite AOD data. In addition, the analysis resulting from the experiment assimilating both FY-3A/MERSI (Medium-resolution Spectral Imager) AOD data and MODIS AOD data had closer agreement with the ground-based values than the individual assimilation of the two datasets for the dust storm over East Asia. These results suggest that the Chinese FY-3A satellite aerosol products can be effectively applied to numerical models and dust weather analysis.
摘要
气溶胶光学厚度(Aerosol Optical Depth, AOD)是表征大气气溶胶光学特征的最基本量, 并且可以用来推算大气气溶胶含量. 本研究使用GSI三维变分同化系统, 同化了风云3A(FY-3A)和MODIS两种卫星的气溶胶数据, 并应用在2011年4月东亚一次沙尘过程中. 研究中将每天06时(UTC)作为同化时刻, 用WRF-Chem(Weather Research and Forecasting with Chemistry model)模式向后积分24小时. 结果表明, 同化卫星观测数据能够显著提高模式预报能力. 同化试验后, 分析场的AOD分布更接近卫星观测场. 此外, 在本次沙尘个例研究中, 通过试验对比发现, 同时同化FY-3A与MODIS两种卫星数据的试验结果比单独同化时更接近地面观测数据. 以上结果表明, 我国FY-3A卫星气溶胶数据产品在数值模式及空气质量预报中具有广泛的应用前景, 对于我国风云系列卫星气溶胶产品的应用和推广具有一定的积极指示意义.
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Acknowledgements
This research was primarily supported by the National Key Research and Development Program of China (Grant Nos. 2017YFC1502100 and 2016YFA0602302), the Natural Science Foundation of Jiangsu Province (Grant Nos. BK20160954 and BK20170940), the Beijige Funding from Jiangsu Research Institute of Meteorological Science (Grant Nos. BJG201510 and BJG201604), the Startup Foundation for Introducing Talent of NUIST (Grant Nos. 2016r27, 2016r043 and 2017r058), a project for data application of Fengyun3 meteorological satellite [FY-3(02)-UDS-1.1.2], and the Priority Academic Program Development of Jiangsu Higher Education Institutions.
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Xia, X., Min, J., Shen, F. et al. Aerosol Data Assimilation Using Data from Fengyun-3A and MODIS: Application to a Dust Storm over East Asia in 2011. Adv. Atmos. Sci. 36, 1–14 (2019). https://doi.org/10.1007/s00376-018-8075-9
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DOI: https://doi.org/10.1007/s00376-018-8075-9