主被动遥感在黑碳气溶胶反演与排放校验中的方法研究

黑碳气溶胶对气候变化、生态环境和公共健康都有显著影响。但主被动遥感在黑碳光学特性的反演过程中，还存在数据覆盖率低、气溶胶模式固化、缺乏先验知识约束等缺点。本项目首先采用葵花8号静止卫星数据，借鉴MISR多角度思想来构建经验正交函数模拟太阳天顶角的变化，通过求解GEOS-Chem格网尺度的动态气溶胶模式，对黑碳气溶胶光学厚度进行反演。其次，基于地基、卫星和大气化学模式等先验知识，对CALIOP激光雷达的气溶胶分类算法进行分析。重点解决GEOS-Chem与CALIOP气溶胶类型转换、识别阈值评估和尺度融合等问题，进而得到高精度、高覆盖率的黑碳垂直廓线。最后，开展基于“自上而下”遥感观测约束的黑碳排放清单多维校验研究。通过构建比例因子和情景假设法验证黑碳排放与浓度之间的响应关系。在此基础上，通过模式模拟并和观测数据比对，评估伴随模式等不同方法、以及不同遥感产品在黑碳排放校验中的适用性及误差。

Black carbon aerosols have significant effects on climate change, ecological environment and public health. However, when using the active and passive remote sensing technology to retrieve the optical properties of black carbon, there are large limitations such as the low data coverage, the static aerosol model and the lack of prior knowledge constraints. This approach will first refer MISR multi-angle thought into Himawari-8 geostationary satellite data by constructing an empirical orthogonal function to simulate the variation of solar zenith angle. Then the black carbon optical depth will be retrieved by solving the dynamic aerosol model from grid-scale GEOS-Chem simulations. Second, the CALIOP aerosol classification algorithm will be evaluated based on the prior knowledge of the ground-based and satellite-based observations and the atmospheric chemistry model simulations. To derive the high precision and coverage black carbon vertical profiles, we will focus on the aerosol type’s detection threshold settings in CALIOP, the transformation of aerosol model and the scale fusion problems between GEOS-Chem and CALIOP data. Finally, we plan to conduct studies on the multi-dimensional verification technology of black carbon emission inventory by using the top-down remote sensing constraints. The relationship between black carbon emissions and concentrations will be explored by constructing a scale factor method and a scenario hypothesis algorithm. On this basis, the applicability and errors of different approaches (e.g. our methods and GEOS-Chem adjoint model) and satellite products in black carbon emission verification will be evaluated by comparing GEOS-Chem simulations with observation datasets.