基于统计偏差校正方法创建EMI-2和TROPOMI连续臭氧总柱

EMI-2 and TROPOMI Coherent Ozone Total Columns Based on Statistical Bias Correction Method

长期一致的臭氧总柱(TCO)记录对于臭氧层变化评估和连续观测有重要意义。臭氧的卫星监测数据较为充足,但各个数据集之间的一致性较差,卫星载荷、光谱仪器设计校准和反演算法的差异,导致在相同区域不同载荷TCO观测有明显的跨载荷偏差。为了获得一致的TCO记录,原始数据和算法层面的均质化虽然更符合物理意义,但需要不同卫星载荷团队之间完全共享所有仪器参数、原始数据和全部反演算法,难度很大。文章介绍基于统计学的跨载荷系统偏差的消除方法。提出了一种基于分位数-分位数(Q-Q)偏差校正方法,以消除大气痕量气体差分吸收光谱仪(EMI-2)和对流层观测仪(TROPOMI)之间跨载荷TCO系统偏差。利用2021年11月重叠时间段内的共同观测结果,通过Q-Q偏差校正方法来表征EMI-2和TROPOMI之间的系统偏差,然后将EMI-22021年12月期间的TCO观测结果同质化到TROPOMI水平,这种Q-Q偏差校正方法显着提高了TCO跨载荷观测整体一致性,将EMI-2和TROPOMI的相关性R从0.96提升到0.98,为臭氧的连续观测提供基础。将EMI-2均质化前后的数据与地面站点数据进行偏差分析表明Q-Q偏差校正方法提升EMI-2观测的准确性和一致性,与地基数据的误差从5%进一步降低到3%。地基站点数据表明EMI-2数据在温带和寒带地区数据的精度较高,但在热带地区误差高于5%,初步推测是热带云高更高,云分数更大,云数据的云压云分数精度不够,“鬼柱”补偿的云下臭氧效果较差,但经过均质化后偏差减小。研究表明,Q-Q偏差校正方法对全球长期TCO记录至关重要,可应用于未来对全球范围内臭氧恢复的评估。

Long-term consistent records of Total Column Ozone(TCO)are of great significance for assessing ozone layer changes and continuous observation.Although there is abundant satellite monitoring data for ozone,the consistency between different datasets is poor.Differences in satellite payloads,design calibration of spectrometers,and inversion algorithms lead to significant cross-payload biases in TCO observations in the same region.To obtain consistent TCO records,homogenization at the raw data and algorithms level is more physically meaningful,but it requires complete sharing of all instrument parameters,raw data,and all inversion algorithms between different satellite payload teams,which is very difficult.This paper introduces a method to eliminate cross-payload systematic bias based on statistics.In this paper,a quantile-quantile(Q-Q)bias correction method is proposed to eliminate the cross-payload TCO systematic bias between the Environmental Trace Gases Monitoring Instrument 2(EMI-2)and the TROPO spheric Monitoring Instrument(TROPOMI).Using the overlapping observations in November 2021,this study characterizes the systematic bias between EMI-2 and TROPOMI through the Q-Q bias correction method.Then,it homogenizes the TCO observations of EMI-2 in December 2021 to the TROPOMI level.This Q-Q bias correction method significantly improves the overall consistency of cross-payload TCO observations,increasing the correlation coefficient R between EMI-2 and TROPOMI from 0.96 to 0.98,providing a basis for continuous ozone observation.Bias analysis of the data before and after homogenization of EMI-2 with ground station data shows that the Q-Q bias correction method improves the accuracy and consistency of EMI-2 observations,reducing the error with ground-based data from 5%to 3%.Ground station data indicate that the accuracy of EMI-2 data is higher in temperate and polar regions,but the error is higher than 5%in tropical regions.It is preliminarily speculated that this is because the cloud height is higher.The cloud fraction is larger in tropical regions,and the accuracy of cloud pressure and cloud fraction in cloud data is insufficient.The effect of compensating for the ozone below the clouds with“ghost columns”is poor,but the bias is reduced after homogenization.The study shows that the Q-Q bias correction method introduced in this paper is crucial for global long-term TCO records and can be applied to future assessments of global ozone recovery.