Statistical bias correction for creating coherent total ozone record from OMI and OMPS observations

Abstract

Abstract A long-term coherent total column ozone (TCO) record is essential to ozone layer variability assessment, especially the detection of early signs of ozone recovery after years of depletion. Because of differences in satellite platforms and instruments design, calibration, and retrieval algorithms, however, significant cross-mission biases are observed between multiple sensor TCO observations in the common time–space domain. To attain a coherent TCO record, observed cross-mission biases should be accurately addressed prior to the data-merging scheme. In this study, a modified statistical bias correction method was proposed based on the quantile-quantile adjustment to remove apparent cross-mission TCO biases between the Ozone Monitoring Instrument (OMI) and Ozone Mapping and Profiler Suite (OMPS). To evaluate the effectiveness of this modified algorithm, the overall inconsistency (OI), a unique time-series similarity measure, was proposed to quantify the improvements of consistency (or similarity) between cross-mission TCO time series data before and after bias correction. Common observations during the overlapped time period of 2012–2015 were used to characterize the systematic bias between OMPS and OMI through the modified bias correction method. TCO observations from OMI during 2004–2015 were then projected to the OMPS level by removing associated cross-mission biases. This modified bias correction scheme significantly improved the overall consistency, with an average improvement of 90% during the overlapped time period at the global scale. In addition to the evaluation of consistency improvements before and after bias correction, impacts of cross-mission biases on long-term trend estimations were also investigated. Comparisons of derived trends from the merged TCO time series before and after bias correction across 38 ground-based stations indicate that cross-mission biases not only affect magnitudes of estimated trends, but also result in different phases of trends. Further comparisons of estimated seasonal TCO trends before and after bias correction at the global scale suggest that trends derived from the bias-corrected time series are more accurate than those without bias correction. Overall, the bias correction scheme developed in this study is essential for preparing an accurate long-term TCO record representative of trend analysis to support future assessment of ozone recovery at the global scale.