Abstract. The impact of assimilating total column ozone datasets from single and multiple satellite data sources with and without bias correction has been examined with a version of the Environment and Climate Change Canada variational assimilation and forecasting system. The assimilated and evaluated data sources include the Global Ozone Monitoring Experiment-2 instruments on the MetOp-A and MetOp-B 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. 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 for total column ozone. Regional and global mean differences of the OMI-TOMS data with measurements from the three ground-based instrument types for the three evaluated two month periods were found to be within 1 %, except for the polar regions with the largest differences from the comparatively small dataset in Antarctica exceeding 3 %. Values from SBUV/2 summed partial columns were typically larger than OMI-TOMS on average by 0.6 to 1.2 ± 0.7 %, with smaller differences than with ground-based over Antarctica. OMI-TOMS was chosen as the reference used in the bias correction instead of the ground-based observations due to OMI’s significantly better spatial and temporal coverage and interest in near-real time assimilation. Bias corrections as a function of latitude and solar zenith angle were performed with a two-week moving window using colocation with OMI-TOMS and three variants of differences with short-term forecasts. These approaches are 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 changes in residual biases as a function of time for some cases. Assimilation experiments for the July-August 2014 period show a reduction of global and temporal mean biases for short-term forecasts relative to ground-based Brewer and Dobson data 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 are reduced to within 1 % for nearly all cases when bias corrected observations are assimilated for the latitudes where satellite data is present. The impact of bias correction on the standard deviations and anomaly correlation coefficients of forecast differences to OMI-TOMS is noticeable but small compared to the impact of introducing any total column ozone assimilation. The assimilation of total column ozone data can result in some improvement, as well as some deterioration, in the vertical structure of forecasts when comparing to Aura-MLS and ozonesonde profiles. The most significant improvement in the vertical domain from the assimilation of total column ozone alone is seen in the anomaly correlation coefficients in the tropical lower stratosphere, which increases from a minimum of 0.1 to about 0.6. Nonetheless, it is made evident that the quality of the vertical structure is most improved when also assimilating ozone profile data, which only weakly affects the total column short-term forecasts.
A study on harmonizing total ozone assimilation with multiple sensors
