Constraining CO Forecast With MOPITT Radiances: Assimilation of Space Data in the MOZART CTM

Paper presents the first assimilation scheme of the MOPITT CO sensitive radiances as the primary space data in the MOZART CTM. The ensemble forecasting of CO by the CTM provides the dynamical a priori for the future radiance data insertion in the model. The outlined ensemble-based scheme helps directly interpret the information coming from the primary data (radiances) of multi-sensors on the same CTM grid. It shows that the radiances from the thermal channels with the deep layer weighting functions can only constrain the partial CO sub-columns in the free troposphere. We will highlight benefits of the radiance data analysis schemes comparing to the assimilation of reported retrievals. Implications for comparisons between the multi-sensor CO radiance data and retrievals (MOPITT, TES, AIRS) will be discussed. The generalized inverse schemes for the dual estimation of the free troposphere constituents and optimization of their surface sources with infrared radiance data are examined using the toy-CTM CO forecast and synthetic MOPITT radiances.