Data merging, introduced in Chapter 11, can improve data availability by filling in data gaps with the involvement of multiple sensors' observations. Data merging can also be extended to create coherent multi-sensor data, such as total column ozone time series for air resources management and global environmental change assessment. The need to create long-term consistent total ozone time series for analyzing global ozone variability as well as climate change detection and attribution triggers a data merging effort between the OMI and OMPS observations in this study. This chapter focuses on the significant discrepancies between cross-mission sensor observations, which need to be eliminated before data merging. This chapter is thus to demonstrate the data merging procedure between the OMI and OMPS total column ozone observations at the global scale. It aims to highlight the statistical bias correction scheme for creating a long-term coherent total column ozone record based on OMI and OMPS observations. The statistical bias correction technique and its potential applications described in this chapter include:

a modified quantile-quantile adjustment technique for data merging between the OMI and OMPS observations to create a coherent total column ozone record globally

trend analysis to examine early signs of ozone recovery based on the merged total column ozone data set

a further bias correction scheme to the merged total column ozone record with ground-based total column ozone measurements