A study on harmonizing total ozone assimilation with multiple sensors

Bias estimations and corrections of total column measurements are applied and evaluated with ozone data from satellite instruments providing near-real-time products during summer 2014 and 2015 and winter 2015. The developed standalone bias-correction system can be applied in near-real-time chemical data assimilation and long-term reanalysis. The instruments to which these bias corrections were applied include the Global Ozone Monitoring Experiment-2 instruments on the MetOp-A and MetOpB satellites (GOME-2A and GOME-2B), the total column ozone mapping instrument of the Ozone Mapping Profiler Suite (OMPS-NM) on the Suomi National Polar-orbiting Partnership (S-NPP) satellite, and the Ozone Monitoring Instrument (OMI) instrument on the Aura research satellite. The OMI data set based on the TOMS version 8.5 retrieval algorithm was chosen as the reference used in the bias correction of the other satellite-based total column ozone data sets. OMI data were chosen for this purpose instead of ground-based observations due to OMI's significantly better spatial and temporal coverage, as well as interest in near-realtime assimilation. Ground-based Brewer and Dobson spectrophotometers, and filter ozonometers, as well as the Solar Backscatter Ultraviolet satellite instrument (SBUV/2), served as independent validation sources of total column ozone data. Regional and global mean differences of the OMI-TOMS data with measurements from the three ground-based instrument types for the three evaluated 2-month periods were found to be within 1 %, except for the polar regions, where the largest differences from the comparatively small data set in Antarctica exceeded 3 %. Values from SBUV/2 summed partial columns were typically larger than OMI-TOMS on average by 0.6% to 1.2 %, with smaller differences than with ground-based observations over Antarctica. Bias corrections as a function of latitude and solar zenith angle were performed for GOME-2A/B and OMPS-NM using colocation with OMI-TOMS and three variants of differences with short-term model forecasts. These approaches were shown to yield residual biases of less than 1 %, with the rare exceptions associated with bins with less data. These results were compared to a time-independent bias-correction estimation that used colocations as a function of ozone effective temperature and solar zenith angle which, for the time period examined, resulted in larger residual biases for bins whose bias varies more in time. The impact of assimilating total column ozone data from single and multiple satellite data sources with and without bias correction was examined with a version of the Environment and Climate Change Canada variational assimilation and forecasting system. Assimilation experiments for July-August 2014 show a reduction of global mean biases for short-term forecasts relative to ground-based Brewer and Dobson observations from a maximum of about 2.3% in the absence of bias correction to less than 0.3% in size when bias correction is included. Both temporally averaged and time-varying mean differences of forecasts with OMI-TOMS were reduced to within 1% for nearly all cases when bias-corrected observations are assimilated for the latitudes where satellite data are present. Creative Commons Attribution 4.0 License and the OGL are interoperable and do not conflict with, reduce or limit each other. (c) Crown copyright 2019