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Using machine learning approach to reproduce the measured feature and understand the model-to-measurement discrepancy of atmospheric formaldehyde

文献类型：期刊论文

作者Yin, Hao2,3; Sun, Youwen2,3; You, Yan4; Notholt, Justus6; Palm, Mathias6; Wang, Wei2; Shan, Changgong2; Liu, Cheng1,3,5,7
刊名SCIENCE OF THE TOTAL ENVIRONMENT
出版日期2022-12-10
卷号851
ISSN号0048-9697
关键词HCHO FTIR Machine learning Remote sensing GEOS-Chem
DOI10.1016/j.scitotenv.2022.158271
通讯作者Sun, Youwen(ywsun@aiofm.ac.cn) ; You, Yan(yyou@must.edu.mo)
英文摘要The solar absorption spectrometry in the infrared spectral region, using high-resolution Fourier transform infrared (FTIR) spectrometer, has been established as a powerful tool in atmospheric science. These observations cannot be per-formed continuously, for example, clouds prevent observations. On the other hand, chemical transport models give continuously data. Their results depend on the knowledge of emission inventories, the chemistry involved, and the me-teorological fields, yielding to potential biases between measurements and simulations. In our study we concentrated on Formaldehyde (HCHO) and used machine learning approach to fill the gap between the observations, performed on an irregular time scale and having their measurement lacks, and model data, giving continuous data, but having poten-tial variable biases. The proposed machine learning approach is based on the Light Gradient Boosting Machine (LightGBM) algorithm and created by using GEOS-Chem simulations, meteorological fields, emission inventory, and is referred to as the GEOS-Chem-LightGBM model. The results of established GEOS-Chem-LightGBM model have generated consistent HCHO predictions with the ground-based FTIR and satellite (OMI and TROPOMI) observations. In order to understand the GEOS-Chem model to measurement discrepancy, we have investigated the contribution of each input variable to GEOS-Chem-LightGBM model HCHO predictions through the SHapely Additive exPlanations (SHAP) approach. We found that the GEOS-Chem model underestimates the sensitivities of HCHO total column to most photochemical variables, contributing to lower amplitudes of diurnal cycle and seasonal cycle by the GEOS-Chem model. By correcting the model-to-measurement discrepancy, the sensitivities of HCHO total column to all variables by the GEOS-Chem-LightGBM became to be in good agreement with the FTIR observations. As a result, GEOS-Chem-LightGBM model has significantly improved the performance of HCHO predictions compared to the GEOS-Chem alone. The proposed GEOS-Chem-LightGBM model can be extendible to other atmospheric constituents obtained by various measurement techniques and platforms, and is expected to have wide applications.
WOS关键词GROUND-BASED FTIR ; MAX-DOAS OBSERVATIONS ; EASTERN CHINA ; GEOS-CHEM ; TIME-SERIES ; ABSORPTION-SPECTROSCOPY ; ANTHROPOGENIC EMISSIONS ; STATISTICAL-ANALYSIS ; NO2 CONCENTRATIONS ; FEATURE-SELECTION
资助项目Youth Innovation Promotion Association, CAS[2019434] ; Science and Technology Development Fund, Macao SAR[0064/2020/A2]
WOS研究方向Environmental Sciences & Ecology
语种英语
出版者ELSEVIER
WOS记录号WOS:000863254000016
资助机构Youth Innovation Promotion Association, CAS ; Science and Technology Development Fund, Macao SAR
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/129127]  
专题中国科学院合肥物质科学研究院
通讯作者Sun, Youwen; You, Yan
作者单位1.Univ Sci & Technol China, Key Lab Precis Sci Instrumentat, Anhui Higher Educ Inst, Hefei 230026, Peoples R China
2.Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Key Lab Environm Opt & Technol, HFIPS, Hefei 230031, Peoples R China
3.Univ Sci & Technol China, Dept Precis Machinery & Precis Instrumentat, Hefei 230026, Peoples R China
4.Macau Univ Sci & Technol, Macao Environm Res Inst, Natl Observat & Res Stn Coastal Ecol Environm Maca, Macau 999078, Peoples R China
5.Univ Sci & Technol China, Anhui Prov Key Lab Polar Environm & Global Change, Hefei 230026, Peoples R China
6.Univ Bremen, Inst Environm Phys, POB 330440, D-28334 Bremen, Germany
7.Chinese Acad Sci, Inst Urban Environm, Ctr Excellence Reg Atmospher Environm, Xiamen 361021, Peoples R China
推荐引用方式
GB/T 7714		 Yin, Hao,Sun, Youwen,You, Yan,et al. Using machine learning approach to reproduce the measured feature and understand the model-to-measurement discrepancy of atmospheric formaldehyde[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2022,851.
APA Yin, Hao.,Sun, Youwen.,You, Yan.,Notholt, Justus.,Palm, Mathias.,...&Liu, Cheng.(2022).Using machine learning approach to reproduce the measured feature and understand the model-to-measurement discrepancy of atmospheric formaldehyde.SCIENCE OF THE TOTAL ENVIRONMENT,851.
MLA Yin, Hao,et al."Using machine learning approach to reproduce the measured feature and understand the model-to-measurement discrepancy of atmospheric formaldehyde".SCIENCE OF THE TOTAL ENVIRONMENT 851(2022).

入库方式： OAI收割

来源：合肥物质科学研究院

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