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王溢婕，臧增亮，杨磊库，颜鹏，胡译文，曾勇，尤伟，潘晓滨.2022.风云三号D星MERSI AOD资料的同化应用研究.遥感学报，26（5）： 941-952
Wang Y J，Zang Z L，Yang L K，Yan P，Hu Y W，Zeng Y，You W and Pan X B. 2022. Assimilation application of MERSI AOD of FY-3D satellite data. National Remote Sensing Bulletin， 26（5）：941-952
为了验证风云三号D星MERSI传感器的气溶胶光学厚度（AOD）数据对地面PM,2.5,的污染过程预报的效果，本文基于WRF-Chem（Weather Research and Forecasting model coupled with Chemistry）大气化学模式和三维变分同化方法，针对2020-02-10—2020-02-12中国北方地区的一次PM,2.5,重污染过程，进行了同化和预报试验研究。同化数据来自常规地面站点的PM,2.5,浓度数据和风云三号D星MERSI传感器的气溶胶光学厚度（AOD）数据。控制试验不同化任何资料，3组同化试验分别为仅同化地面PM,2.5,，仅同化卫星AOD，以及同时同化PM,2.5,和卫星AOD两种资料。结果表明，3组同化试验都可以有效提高初始场准确率，以地面PM,2.5,作为检验标准，仅同化PM,2.5,、仅同化AOD、同时同化两种资料相对于控制试验，初始场的平均偏差分别降低54.9%、21.9%和49.0%，平均相关系数分别提升51.4%、16.0%和34.0%，平均均方根误差分别降低50.6%、17.2%和42.3%。以卫星AOD作为检验标准，3组同化试验相对于控制试验，初始场的平均偏差分别降低37.6%、78.4%和83%，平均均方根误差分别降低31.6%、62.2%和65.2%。同化后的初始场对预报有显著的改进，改进持续时间达24 h，以地面PM,2.5,作为检验标准，同时同化两种资料的试验对24 h预报的平均偏差减少19.7%，相关系数提升8.8%，均方根误差减少17.2%；以卫星AOD作为检验标准，24 h预报的平均偏差减少40.1%，相关系数提升25.9%，均方根误差降低34.7%。试验结论为，相对于仅同化地面PM,2.5,资料，同化风云卫星AOD资料可以提升后期预报效果。
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 PM,2.5,.,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 PM,2.5, pollution process in northern China from February 10 to 13, 2020.,The assimilation data were derived from PM,2.5, 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 PM,2.5, satellite AOD, and PM,2.5, 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 PM,2.5, as the test standard, compared with the control experiment, assimilating PM,2.5, data, AOD data, and PM,2.5, 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 PM,2.5, 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 PM,2.5, data.
遥感WRF-Chem模式三维变分资料同化风云三号气溶胶光学厚度
remote sensingWRF-Chem Modelthree-dimensional Variationdata assimilationFY-3 satelliteAOD
Cui Y J, Wang Z F, Zhu J and Fu Q Y. 2006. A preliminary study on data assimilation for numerical air quality model prediction. Climatic and Environmental Research, 11(5): 616-626
崔应杰, 王自发, 朱江, 付晴艳. 2006. 空气质量数值模式预报中资料同化的初步研究. 气候与环境研究, 11(5): 616-626 [DOI: 10.3969/j.issn.1006-9585.2006.05.006http://dx.doi.org/10.3969/j.issn.1006-9585.2006.05.006]
Derber J and Bouttier F. 1999. A reformulation of the background error covariance in the ECMWF global data assimilation system. Tellus A: Dynamic Meteorology and Oceanography, 51(2): 195-221 [DOI: 10.3402/tellusa.v51i2.12316http://dx.doi.org/10.3402/tellusa.v51i2.12316]
Feng S Z, Jiang F, Jiang Z Q, Wang H M, Cai Z and Zhang L. 2018. Impact of 3DVAR assimilation of surface PM2.5 observations on PM2.5 forecasts over China during wintertime. Atmospheric Environment, 187: 34-49 [DOI: 10.1016/j.atmosenv.2018.05.049http://dx.doi.org/10.1016/j.atmosenv.2018.05.049]
Forkel R, Balzarini A, Baró R, Bianconi R, Curci G, Jiménez-Guerrero P, Hirtl M, Honzak L, Lorenz C, Im U, Pérez J L, Pirovano G, José R S, Tuccella P, Werhahn J and Žabkar R. 2015. Analysis of the WRF-Chem contributions to AQMEII phase2 with respect to aerosol radiative feedbacks on meteorology and pollutant distributions. Atmospheric Environment, 115: 630-645 [DOI: 10.1016/j.atmosenv.2014.10.056http://dx.doi.org/10.1016/j.atmosenv.2014.10.056]
Gao M, Guttikunda S K, Carmichael G R, Wang Y S, Liu Z R, Stanier C O, Saide P E and Yu M. 2015. Health impacts and economic losses assessment of the 2013 severe haze event in Beijing area. Science of the Total Environment, 511: 553-561. [DOI: 10.1016/j.scitotenv.2015.01.005http://dx.doi.org/10.1016/j.scitotenv.2015.01.005]
Gao Q X, Wang N, Gao W K, Gao W O, Li Y X, Fu J F and Dai J Q. 2020. Analysis of two atmospheric heavy pollution processes in Beijing-Tianjin-Hebei and surrounding areas during the Spring Festival of 2020. Journal of Environmental Engineering Technology, 10(4): 517-530
高庆先, 王宁, 高文康, 高文欧, 李迎新, 付加锋, 代佳庆. 2020. 2020年春节期间京津冀及周边地区2次大气重污染过程分析. 环境工程技术学报, 10(4): 517-530 [DOI: 10.12153/j.issn:1674-991X.20020087http://dx.doi.org/10.12153/j.issn:1674-991X.20020087]
Grell G A, Peckham S E, Schmitz R, McKeen S A, Frost G, Skamarock W C and Eder B. 2005. Fully coupled “online” chemistry within the WRF model. Atmospheric Environment, 39(37): 6957-6975 [DOI: 10.1016/j.atmosenv.2005.04.027http://dx.doi.org/10.1016/j.atmosenv.2005.04.027]
Jiang Z Q, Liu Z Q, Wang T J, Schwartz C S, Lin H C and Jiang F. 2013. Probing into the impact of 3DVAR assimilation of surface PM10 observations over China using process analysis. Journal of Geophysical Research: Atmospheres, 118(12): 6738-6749 [DOI: 10.1002/jgrd.50495http://dx.doi.org/10.1002/jgrd.50495]
Jin L B, Zang Z L, Pan X B, Wang T J, Hao Z L and Jiang Z Q. 2016. Data assimilation and application experiments of PM2.5 and PM2.5~10 during Nanjing Youth Olympic Games. China Environmental Science, 36(2): 331-341
靳璐滨, 臧增亮, 潘晓滨, 王体健, 郝子龙, 蒋自强. 2016. PM2.5和PM2.5~10资料同化及在南京青奥会期间的应用试验. 中国环境科学, 36(2): 331-341 [DOI: 10.3969/j.issn.1000-6923.2016.02.003http://dx.doi.org/10.3969/j.issn.1000-6923.2016.02.003]
Li Z, Zang Z, Li Q B, Chao Y, Chen D, Ye Z, Liu Y and Liou K N. 2013. A three-dimensional variational data assimilation system for multiple aerosol species with WRF/Chem and an application to PM2.5 prediction. Atmospheric Chemistry and Physics, 13(8): 4265-4278 [DOI: 10.5194/acp-13-4265-2013http://dx.doi.org/10.5194/acp-13-4265-2013]
Liang Y F, Zang Z L, Liu D, Yan P, Hu Y W, Zhou Y and You W. 2020. Development of a three-dimensional variational assimilation system for lidar profile data based on a size-resolved aerosol model in WRF–Chem model v3.9.1 and its application in PM2.5 forecasts across China. Geoscientific Model Development, 13(12): 6285-6301 [DOI: 10.5194/gmd-13-6285-2020http://dx.doi.org/10.5194/gmd-13-6285-2020]
McHenry J N, Vukovich J M and Hsu N C. 2015. Development and implementation of a remote-sensing and in situ data-assimilating version of CMAQ for operational PM2.5 forecasting. Part 1: MODIS aerosol optical depth (AOD) data-assimilation design and testing. Journal of the Air and Waste Management Association, 65(12): 1395-1412 [DOI: 10.1080/10962247.2015.1096862http://dx.doi.org/10.1080/10962247.2015.1096862]
Pang J M, Liu Z Q, Wang X M, Bresch J, Ban J M, Chen D and Kim J. 2018. Assimilating AOD retrievals from GOCI and VIIRS to forecast surface PM2.5 episodes over Eastern China. Atmospheric Environment, 179: 288-304 [DOI: 10.1016/j.atmosenv.2018.02.011http://dx.doi.org/10.1016/j.atmosenv.2018.02.011]
Parrish D F and Derber J C. 1992. The national meteorological center’s spectral statistical-interpolation analysis system. Monthly Weather Review, 120(8): 1747-1763 [DOI: 10.1175/1520-0493(1992)120<1747:tnmcss>2.0.co;2http://dx.doi.org/10.1175/1520-0493(1992)120<1747:tnmcss>2.0.co;2]
Saide P E, Kim J, Song C H, Choi M, Cheng Y F and Carmichael G R. 2014. Assimilation of next generation geostationary aerosol optical depth retrievals to improve air quality simulations. Geophysical Research Letters, 41(24): 9188-9196 [DOI: 10.1002/2014gl062089http://dx.doi.org/10.1002/2014gl062089]
Shi Y R, Levy R C, Yang L K, Remer L A, Mattoo S and Dubovik O. 2021. A Dark Target research aerosol algorithm for MODIS observations over eastern China: increasing coverage while maintaining accuracy at high aerosol loading. Atmospheric Measurement Techniques, 14(5): 3449-3468 [DOI: 10.5194/AMT-14-3449-2021http://dx.doi.org/10.5194/AMT-14-3449-2021]
Tang Y H, Pagowski M, Chai T F, Pan L, Lee P, Baker B, Kumar R, Monache L D, Tong D and Kim H C. 2017. A case study of aerosol data assimilation with the Community Multi-scale Air Quality Model over the contiguous United States using 3D-Var and optimal interpolation methods. Geoscientific Model Development, 10(12): 4743-4758 [DOI: 10.5194/gmd-10-4743-2017http://dx.doi.org/10.5194/gmd-10-4743-2017]
Xue W B, Wang J N, Yang J T, Lei Y, Wang Y M and Chen X. 2013. Domestic and foreign research progress of air quality model. Environment and Sustainable Development, 38(3): 14-20
薛文博, 王金南, 杨金田, 雷宇, 汪艺梅, 陈曦. 2013. 国内外空气质量模型研究进展. 环境与可持续发展, 38(3): 14-20 [DOI: 10.19758/j.cnki.issn1673-288x.2013.03.004http://dx.doi.org/10.19758/j.cnki.issn1673-288x.2013.03.004]
Yang L K, Hu X Q, Wang H, He X W, Liu P, Xu N, Yang Z D and Zhang P. 2021. Preliminary test of quantitative capability in aerosol retrieval over land from MERSI-II onboard Fengyun-3D. National Remote Sensing Bulletin, 25(6): 1-25
杨磊库, 胡秀清, 王涵, 何兴伟, 刘培, 徐娜, 杨忠东, 张鹏. 2021. 风云三号D星MERSI-II陆地气溶胶反演定量能力初测. 遥感学报, 26(7) [DOI:10.11834/jrs.20210286]
Yang Z D, Zhang P, Gu S Y, Hu X Q, Tang S H, Yang L K, Xu N, Zhen Z J, Wang L, Wu Q, Dou F L, Liu R X, Wu X, Zhu L, Zhang L Y, Wang S J, Sun Y Q and Bai W H. 2019. Capability of Fengyun-3D satellite in earth system observation. Journal of Meteorological Research, 33(6): 1113-1130 [DOI: 10.1007/s13351-019-9063-4http://dx.doi.org/10.1007/s13351-019-9063-4]
Yumimoto K, Nagao T M, Kikuchi M, Sekiyama T T, Murakami H, Tanaka T Y, Ogi A, Irie H, Khatri P, Okumura H, Arai K, Morino I, Uchino O and Maki T. 2016. Aerosol data assimilation using data from Himawari-8, a next-generation geostationary meteorological satellite. Geophysical Research Letters, 43(11): 5886-5894 [DOI: 10.1002/2016gl069298http://dx.doi.org/10.1002/2016gl069298]
Zang Z L, Li Z J, Pan X B, Hao Z L and You W. 2016. Aerosol data assimilation and forecasting experiments using aircraft and surface observations during CalNex. Tellus B: Chemical and Physical Meteorology, 68(1): 29812 [DOI: 10.3402/tellusb.v68.29812http://dx.doi.org/10.3402/tellusb.v68.29812]
Zaveri R A, Easter R C, Fast J D and Peters L K. 2008. Model for simulating aerosol interactions and chemistry (MOSAIC). Journal of Geophysical Research: Atmospheres, 113(D13): D13204 [DOI: 10.1029/2007jd008782http://dx.doi.org/10.1029/2007jd008782]
Zhang R H, Li Q and Zhang R N. 2014. Meteorological conditions for the persistent severe fog and haze event over eastern China in January 2013. Science China Earth Sciences, 57(1): 26-35
张人禾, 李强, 张若楠. 2014. 2013年1月中国东部持续性强雾霾天气产生的气象条件分析. 中国科学: 地球科学, 44(1): 27-36 [Doi: 10.1007/s11430-013-4774-3http://dx.doi.org/10.1007/s11430-013-4774-3]
Zhang Y, Liao H, Zhu K F and Yin Y. 2009. Role of black carbon-induced changes in snow albedo in predictions of temperature and precipitation during a snowstorm. Atmospheric and Oceanic Science Letters, 2(4): 230-236 [DOI: 10.1080/16742834.2009.11446801http://dx.doi.org/10.1080/16742834.2009.11446801]
Zhang Y X, Li X, Zhang M, Kang Q, Wei W, Zheng X B, Zhang Y.2020.On-orbit Radiometric Calibration for Thermal Infrared Band of FY3D/MERSI-Ⅱ Satellite Remote Sensor Based on Qinghai Lake Radiation Calibration Test-site
张允祥,李新,张孟,康晴,韦玮,郑小兵,张勇.基于青海湖辐射校正场的FY3D/MERSI-Ⅱ卫星遥感器热红外波段在轨辐射定标[J].光子学报,2020,49(05):134-142
Zhao Z J, Wei Y J, Zhang X Z, Qin W and Xie H F. 2015. The correlation analysis of Nanjing haze days and meteorological factors. China Environmental Science, 35(12): 3570-3580
赵子菁, 魏永杰, 张祥志, 秦玮, 谢慧芳. 2015. 南京市霾天气与主要气象条件的相关分析. 中国环境科学, 35(12): 3570-3580 [DOI: 10.3969/j.issn.1000-6923.2015.12.006http://dx.doi.org/10.3969/j.issn.1000-6923.2015.12.006]
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