3DVAR Aerosol Data Assimilation and Evaluation Using Surface PM2.5, Himawari-8 AOD and CALIPSO Profile Observations in the North China

被引:5
|
作者
Zang, Zengliang [1 ]
You, Wei [1 ]
Ye, Hancheng [1 ,2 ]
Liang, Yanfei [1 ,3 ]
Li, Yi [1 ]
Wang, Daichun [1 ,4 ]
Hu, Yiwen [5 ]
Yan, Peng [6 ]
机构
[1] Natl Univ Def Technol, Coll Meteorol & Oceanog, Changsha 410073, Peoples R China
[2] 71901 Unit PLA, Liaocheng 252000, Shandong, Peoples R China
[3] 32145 Unit PLA, Xinxiang 453000, Henan, Peoples R China
[4] 94595 Unit PLA, Weifang 216500, Peoples R China
[5] Nanjing Univ Informat Sci & Technol, Sch Atmospher Phys, Nanjing 211101, Peoples R China
[6] Chinese Meteorol Adm, Meteorol Observat Ctr, Beijing 100081, Peoples R China
基金
国家重点研发计划;
关键词
3DVAR; data assimilation; aerosol; AOD; WRF-Chem; VARIATIONAL DATA ASSIMILATION; TRANS-PACIFIC TRANSPORT; AIR-QUALITY MODEL; SYSTEM; CHEMISTRY; SIMULATION; EVOLUTION; FORECASTS; POLLUTION; OZONE;
D O I
10.3390/rs14164009
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Based on the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) aerosol scheme of the Weather Research and Forecasting model coupled with online Chemistry (WRF-Chem) and the three-dimensional variational (3DVAR) assimilation method, a 3DVAR data assimilation (DA) system for aerosol optical depth (AOD) and aerosol concentration observations was developed. A case study on assimilating the Himawari-8 satellite AOD and/or fine particulate matter (PM2.5) was conducted to investigate the improvement of DA on the analysis accuracy and forecast skills of the spatial distribution characteristics of aerosols, especially in the vertical dimension. The aerosol extinction coefficient (AEC) profile data from The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), surface PM2.5 and Himawari-8 AOD measurements were used for verification. One control experiment (without DA) and two DA experiments including a PM2.5 DA experiment denoted by Da_PM and a combined PM2.5 and AOD DA experiment denoted by Da_AOD_PM were conducted. Both DA experiments had positive effects on the surface PM2.5 mass concentration forecast skills for more than 60 h. However, the Da_PM showed a slight improvement in the analysis accuracy of the AOD distribution compared with the control experiment, while the Da_AOD_PM showed a considerable improvement. The Da_AOD_PM had the best positive effect on the AOD forecast skills. The correlation coefficient (CORR), root mean square error (RMSE), and mean fraction error (MFE) of the 24 h AOD forecasts for the Da_AOD_PM were 0.73, 0.38, and 0.54, which are 0.09 (14.06%), 0.08 (17.39%), and 0.22 (28.95%) better than that of the control experiment, and 0.05 (7.35%), 0.06 (13.64%), and 0.19 (26.03%) better than that of the Da_PM, respectively. Moreover, improved performance for the Da_AOD_PM occurred when the AEC profile was used for verification, as when the AOD was used for verification. The Da_AOD_PM successfully simulated the first increasing and then decreasing trend of the aerosol extinction coefficients below 1 km, while neither the control nor the Da_PM did. This indicates that assimilating AOD can effectively improve the analyses and forecast accuracy of the aerosol structure in both the horizontal and vertical dimensions, thereby compensating for the limitations associated with assimilating traditional surface aerosol observations alone.
引用
收藏
页数:14
相关论文
共 17 条
  • [1] Impact of 3DVAR assimilation of surface PM2.5 observations on PM2.5 forecasts over China during wintertime
    Feng, Shuzhuang
    Jiang, Fei
    Jiang, Ziqiang
    Wang, Hengmao
    Cai, Zhe
    Zhang, Lin
    ATMOSPHERIC ENVIRONMENT, 2018, 187 : 34 - 49
  • [2] Estimating the PM2.5 concentration over Anhui Province, China, using the Himawari-8 AOD and a GAM/BME model
    Xiong, Hong-Bin
    Chen, Jian
    Ma, Xiao
    Fang, Meng-Ying
    ATMOSPHERIC POLLUTION RESEARCH, 2021, 12 (07)
  • [3] Estimating hourly surface PM2.5 concentrations with full spatiotemporal coverage in China using Himawari-8/9 AOD and a two-stage model
    Zhang, Shuyang
    Chen, Peng
    Zhang, Yuchen
    Zhu, Chengchang
    Zhang, Cheng
    Lu, Jierui
    Wu, Mengyan
    Yang, Xinyue
    ATMOSPHERIC POLLUTION RESEARCH, 2025, 16 (07)
  • [4] Assimilating All-sky Infrared Radiances from Himawari-8 Using the 3DVar Method for the Prediction of a Severe Storm over North China
    Xu, Dongmei
    Liu, Zhiquan
    Fan, Shuiyong
    Chen, Min
    Shen, Feifei
    ADVANCES IN ATMOSPHERIC SCIENCES, 2021, 38 (04) : 661 - 676
  • [5] Improved PM2.5 predictions of WRF-Chem via the integration of Himawari-8 satellite data and ground observations
    Hong, Jia
    Mao, Feiyue
    Min, Qilong
    Pan, Zengxin
    Wang, Wei
    Zhang, Tianhao
    Gong, Wei
    ENVIRONMENTAL POLLUTION, 2020, 263
  • [6] Combining Himawari-8 AOD and deep forest model to obtain city-level distribution of PM2.5 in China
    Song, Zhihao
    Chen, Bin
    Huang, Jianping
    ENVIRONMENTAL POLLUTION, 2022, 297
  • [7] Hourly PM2.5 Estimation over Central and Eastern China Based on Himawari-8 Data
    Xue, Yong
    Li, Ying
    Guang, Jie
    Tugui, Alexandru
    She, Lu
    Qin, Kai
    Fan, Cheng
    Che, Yahui
    Xie, Yanqing
    Wen, Yanan
    Wang, Zixiang
    REMOTE SENSING, 2020, 12 (05)
  • [8] HOURLY GROUND LEVEL PM2.5 ESTIMATION for THE SOUTHEAST of CHINA BASED on HIMAWARI-8 OBSERVATION DATA
    Li, Ying
    Xue, Yong
    Guang, Jie
    She, Lu
    Chen, Guili
    Fan, Cheng
    2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019), 2019, : 7850 - 7853
  • [9] Probing into the impact of 3DVAR assimilation of surface PM10 observations over China using process analysis
    Jiang, Ziqiang
    Liu, Zhiquan
    Wang, Tijian
    Schwartz, Craig S.
    Lin, Hui-Chuan
    Jiang, Fei
    JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2013, 118 (12) : 6738 - 6749
  • [10] Assimilating All-sky Infrared Radiances from Himawari-8 Using the 3DVar Method for the Prediction of a Severe Storm over North China
    Dongmei Xu
    Zhiquan Liu
    Shuiyong Fan
    Min Chen
    Feifei Shen
    Advances in Atmospheric Sciences, 2021, 38 : 661 - 676