大气辐射传输计算中气溶胶模型评估及选取

Evaluation and Selection of Aerosol Models in Atmospheric Radiative Transfer Calculations

利用AOD-AROD气溶胶分类方法,选用MODIS数据,分析研究区域气溶胶模型的年际变化规律。在规律不明显的月份,分析了气象要素和气溶胶源对模型的影响,构建了基于随机森林方法的多时相气溶胶模型评估方法,即后向轨迹与大气概率分布场分析方法,并分析了多时相气溶胶模型评估方法的准确性,讨论误差原因。使用所提方法评估了香河地区的气溶胶模型,并使用TROPOMI L1B数据对评估结果进行验证。研究结果显示,采用该模型对香河地区的气溶胶类型进行评估的准确度为71.04%,辐亮度模拟的平均误差率减少了38.25%,表明该方法能有效提高气溶胶模型选择的准确性。

Objective Atmospheric aerosol particles refer to various solid,liquid,and solid-liquid mixed particles suspended in the atmosphere,with particle sizes generally ranging from 0.001μm to 100μm.These particles possess distinct physical and chemical properties that differ from other gas molecules in the atmosphere.As the concentration of aerosol particles in the atmosphere reaches certain thresholds,they exert a pronounced influence on radiation transmission.Moreover,the composition of aerosols undergoes conspicuous temporal and spatial variations,influenced by many factors such as aerosol source distribution,underlying surface composition,season,and meteorological conditions.To facilitate calculation and research,several typical aerosol models are usually given through systematic observation experiments,considering the two premises of aerosol composition and sampling area.These models provide very convenient data for radiative transfer calculations.However,different aerosol models have significant effects on radiative transfer and cannot be ignored.Therefore,accurately selecting an appropriate aerosol radiative transfer model under different circumstances is crucial.Methods Our study is based on the AOD-AROD classification model and integrates it with atmospheric radiative transfer calculations.Using the above model based on MODIS aerosol data in typical areas from 2018 to 2022,the inter-annual patterns of aerosol model changes are calculated by month.We deeply explore the connections between meteorological elements,aerosol source locations,and aerosol models through the random forest method.A multi-temporal aerosol model judgment method is developed,considering the temporal changes of meteorological elements,thus improving the applicability and accuracy of the method.Backward trajectory analysis and atmospheric probability distribution fields are utilized for verification and optimization,enhancing the correlation between meteorological elements and aerosol models.Finally,the aerosol model judgment results are validated using TROPOMI’s surface radiance data to enhance the accuracy of atmospheric radiation transfer calculations.Results and Discussions Based on the AOD-AROD classification method and MODIS satellite data,the aerosol optical thickness data of the two bands are inputted into the AOD-AROD model.Using the aerosol optical thickness of 470 nm and the ratio of the aerosol optical thickness of the 470 nm and 660 nm bands,aerosols are divided into five types:dust,continent,subcontinent,biomass combustion,and urban industry.The aerosol model classification map of the Xianghe area from 2018 to 2022 is obtained,and statistical results for 2018 are shown in Fig.7.In the random forest model,based on the specific meaning of various parameters and the characteristics of the training dataset in the Xianghe area,the corresponding parameters are adjusted in a targeted manner to achieve the highest accuracy of the data.Finally,25%of the dataset is used to verify the judgment results of the random forest,and the predicted aerosol types are compared with the actual aerosol types.The comparison process is shown in Fig.9.The accuracy of the final judgment model reached 69.11%.Conclusions Through a comprehensive analysis based on MODIS satellite data and the AOD-AROD aerosol model classification model,we summarize the interannual variation patterns of aerosol models in the study area.Random forest is effectively used to establish an aerosol-type judgment model,considering meteorological elements and aerosol source locations.By analyzing possible causes of model errors through backward trajectories,we compare random forest models in different phases to build a model that is most suitable for the study area.Simultaneously,we combine the analysis of backward trajectories and atmospheric transport probability distribution fields to further improve the accuracy of the model.The research results show that the accuracy of using this model to evaluate the aerosol model in the Xianghe area is 71.04%,and the average error rate of radiance simulation is reduced by 38.25%.