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Assimilation application of MERSI AOD of FY-3D satellite data

  • role： First author第一作者
  • Affiliation：

    National University of Defense Technology, Institute of Meteorology and Oceanography, Changsha 410073, China

  • Email：skywangyj@163.com
  • Introduction：王溢婕，研究方向为大气环境污染。E-mail： skywangyj@163.com
WANG Yijie1,  
  • role： Corresponding author通信作者
  • Affiliation：

    National University of Defense Technology, Institute of Meteorology and Oceanography, Changsha 410073, China

  • Email：zzlqxxy@163.com
  • Introduction：臧增亮，研究方向为大气污染资料同化和遥感反演。E-mail： zzlqxxy@163.com
ZANG Zengliang1*,  
  • Affiliation：

    Henan Polytechnic University, Institute of Surveying and Mapping and Land Information Engineering, Jiaozuo 454003, China

YANG Leiku2,  
  • Affiliation：

    Meteorological Observation Center of CMA, Chinese Meteorological Administration, Beijing 100081, China

YAN Peng3,  
  • Affiliation：

    School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044, China

Hu Yiwen4,  
  • Affiliation：

    Unit 95746, Chinese People's Liberation Army, Chengdu 611530,China

ZENG Yong5,  
  • Affiliation：

    National University of Defense Technology, Institute of Meteorology and Oceanography, Changsha 410073, China

YOU Wei1,  
  • Affiliation：

    National University of Defense Technology, Institute of Meteorology and Oceanography, Changsha 410073, China

PAN Xiaobin1

реферат

This study aims to verify the effect of the aerosol optical thickness data of the Fengyun-3D satellite MERSI sensor on the pollution process prediction of PM2.5.This study was based on WRF-Chem (Weather Research and Forecasting Model Coupled with) Atmospheric Chemistry model and three-dimensional variational assimilation method, which were used to study the assimilation and prediction of a PM2.5 pollution process in northern China from February 10 to 13, 2020.The assimilation data were derived from PM2.5 concentration data from conventional ground stations and Aerosol Optical Depth (AOD) data from the MERSI sensor on the FY-3D satellite. The control experiment did not assimilate any data. The three groups of assimilation experiments were to assimilate ground PM2.5, satellite AOD, and PM2.5 and AOD data at the same time.Results show that the three groups of assimilation experiments can effectively improve the accuracy of the initial field. With ground PM2.5 as the test standard, compared with the control experiment, assimilating PM2.5 data, AOD data, and PM2.5 and AOD data at the same time, the average mean deviation of the initial field was decreased by 54.9%, 21.9%, and 49.0%, the average correlation coefficient was increased by 51.4%, 16.0%, and 34.0%, and the average root mean square error was decreased by 50.6%, 17.2%, and 42.3%. With AOD as the test standard, compared with the control experiment, the average mean deviation of the initial field in three assimilation experiments was decreased by 37.6%, 78.4%, and 83%, and the average root mean square error was decreased by 31.6%, 62.2%, and 65.2%. The initial field after assimilation can significantly improve the prediction, and the improvement lasted for more than 24 h. In general, the experiment of assimilating two kinds of data at the same time had the best improvement effect on the 24 h prediction. With ground PM2.5 as the test standard, the average mean deviation of the 24 h forecast was decreased by 19.7%, the correlation coefficient was increased by 8.8%, and the root mean square error was decreased by 17.2. With AOD as the test standard, the average mean deviation of 24 h forecast was decreased by 40.1%, the correlation coefficient was increased by 25.9%, and the root mean square error was decreased by 34.7%.The experiment also found that the assimilation of FY-3D satellite AOD data had a better lasting effect on the late prediction than only assimilating ground PM2.5 data.

ключеви́че слова́

remote sensing;WRF-Chem Model;three-dimensional Variation;data assimilation;FY-3 satellite;AOD

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