基于GF-5卫星遥感数据的大气CO_(2)快速反演方法

Fast Retrieval Method of Atmospheric CO_(2)Based on GF-5 Satellite Remote Sensing Data

以高分五号卫星(GF-5)搭载的大气温室气体监测仪(GMI)的观测数据为研究对象,设计一种大气CO_(2)快速反演算法,在物理反演算法框架基础上,利用逐线积分法构建适用GMI的气体吸收截面查找表,加速气体吸收光学厚度计算,并通过构建气溶胶光学厚度参数查找表,拟合计算气溶胶光学厚度廓线,从而实现大气CO_(2)快速反演。在此基础上,利用两年的GMI数据验证所提反演算法,并与全球总碳柱观测网(TCCON)站点观测结果进行对比。结果表明,所提出的加速算法与原始算法反演所得的大气CO_(2)柱浓度之间的平均绝对误差为0.75×10^(-6),相关性达到85.5%,与TCCON站点之间平均绝对误差为3.01×10^(-6),满足1%的反演精度要求,在计算效率上,加速算法减少80%以上的计算时间。

Objective Since the industrialization era,with the continuously growing industrialization,urbanization,and energy consumption,greenhouse gas emissions have risen sharply,thus causing a constant increase in global temperatures.Atmospheric CO_(2)is a crucial factor in global warming,and as a major anthropogenic greenhouse gas emission,it has caught continuous attention from the international community.Current high-precision CO_(2)observations primarily rely on ground-based measurements and satellite remote sensing.While ground-based observations have advantages such as high accuracy and strong reliability,they are essentially single-point measurements and sparsely distributed globally,unable to provide detection on a global scale.Therefore,atmospheric CO_(2)satellite remote sensing has become the main method for high-precision CO_(2)monitoring on a global scale.However,with the development of satellite remote sensing from discrete to imaging observation techniques,there has been a substantial increase in remote sensing data volume,and existing retrieval algorithms struggle to meet computational time requirements.In our study,we propose a fast retrieval method for atmospheric CO_(2).By constructing a suitable look-up table to replace the time-consuming components in the original algorithm,we aim to achieve fast atmospheric CO_(2)retrieval.Methods We focus on the observational data from China’s Gaofen-5 satellite(GF-5),equipped with the greenhouse gas monitoring instrument(GMI),and present a fast retrieval algorithm for atmospheric CO_(2).First,by leveraging the spectral characteristics of GMI,a line-by-line integration method is employed to construct a gas absorption cross-section look-up table suitable for GMI data,thereby expediting the calculation of gas absorption optical thickness.Secondly,via adopting data from the Modern-Era Retrospective analysis for Research and Applications,Version 2(MERRA-2),and based on Gaussian line shapes,fitting is performed on aerosol optical thickness profiles to establish a look-up table for Gaussian parameters of aerosol optical thickness,thus facilitating the computation of aerosol optical parameters.Finally,combined with atmospheric environmental parameters and satellite data,the atmospheric XCO_(2)results are obtained by utilizing a radiative transfer calculation model and a physical retrieval algorithm,achieving fast retrieval of atmospheric CO_(2).Results and Discussions We conduct a comparative validation of retrieval accuracy and computational efficiency by adopting total carbon column observing network(TCCON)site data and GMI observational data.Regarding computational efficiency,the original GMI retrieval algorithm and the proposed improved algorithm are compared in terms of processing time.In the context of single forward model calculation time,the improved algorithm reduces the forward model calculation time by over 85%compared to the original GMI algorithm,leading to an approximately 21.5 times improvement in calculation time.In terms of total computation time,the proposed algorithm achieves a time scale in minutes,significantly lower than the original algorithm’s computation time of over 1.5 h,which represents a substantial improvement in computational efficiency(Table 4).Regarding retrieval accuracy,a comparison is conducted between the retrieval results of the proposed algorithm and the original GMI algorithm.The error in the column concentration of CO_(2)between the two algorithms remains within 2×10^(-6)[Fig.4(a)].The average absolute error of XCO_(2)between the two algorithms reaches 0.75×10^(-6),with the high consistency of results reaching 85.5%[Fig.4(b)].This indicates that the proposed algorithm has a minimal influence on the error in the calculation results of GMI retrieval.By comparing the retrieval results of the original GMI algorithm,the improved algorithm,and TCCON site observational results,it is observed that the concentration discrepancies between the proposed algorithm and TCCON mostly stay within 4×10^(-6).The average absolute error in the results is 3.01×10^(-6),and the retrieval error is less than 1%(Fig.5).Furthermore,the retrieval results of both algorithms are generally consistent,meeting the precision requirements for CO_(2)retrieval.Conclusions To address the inefficiency in atmospheric CO_(2)retrieval,we propose a fast atmospheric CO_(2)retrieval method by adopting look-up tables for acceleration based on the practical requirements of GMI retrieval calculations.By constructing look-up tables for gas absorption cross-sections,the method achieves fast calculation of atmospheric layer-wise gas absorption optical thickness.Combined with molecular scattering calculations and fitting calculations for aerosol optical thickness based on aerosol parameter look-up tables,it reduces the computational time for time-consuming molecular absorption calculations in radiative transfer.By comparing the original GMI retrieval algorithm and the improved algorithm,the average absolute error between their retrieval results is 0.75×10^(-6)with high consistency.When compared to TCCON site observational results,the average absolute error in the retrieval results is 3.01×10^(-6),meeting the 1%precision requirement for retrieval accuracy.In terms of computation time,the improved retrieval algorithm significantly reduces the computation time while ensuring retrieval accuracy.The retrieval computation time can be reduced by over 80%,shifting the computational performance from the hourly level to the minute level.By conducting retrieval experiments and result verification,the proposed fast atmospheric CO_(2)retrieval algorithm can substantially enhance the retrieval calculation speed while maintaining retrieval accuracy.In the future,this algorithm can be applied to multi-year GMI data at a global scale and other satellite observational data.