Importance of Forecast Error Multivariate Correlations in Idealized Assimilations of GPS Radio Occultation Data with the Ensemble Adjustment Filter
Monthly Weather Review
The importance of multivariate forecast error correlations between specific humidity, temperature and surface pressure in perfect model assimilations of Global Positioning System (GPS) radio occultation (RO) refractivity data is examined using the Ensemble Adjustment Filter (EAF, Anderson 2001) and the NCAR global Community Atmospheric Model (CAM3). The goal is to explore whether inclusion of the multivariate forecast error correlations in the background term of 3D and 4D variational data
... iational data assimilation systems is likely to improve RO data assimilation in the troposphere. It is not possible to explicitly neglect multivariate forecast error correlations with the EAF because they are not used directly in the algorithm. Instead, the filter only makes use of the forecast error correlations between observed quantities (RO here) and model state variables. However, because the forecast error correlations for RO observations are dominated by correlations with a subset of state variable types in certain regions, one can indirectly assess the importance of multivariate forecast error correlations between state variables. This is done by setting the forecast error correlations of RO observations and some state variables (for instance temperature) to zero in a set of assimilation experiments. Comparing these experiments to a control in which all state variables are impacted by RO observations allows an indirect assessment of the importance of multivariate correlations between state variables not impacted by the observations and those that are. Results suggest that proper specification of the multivariate forecast error correlations in 3D and 4D variational systems should improve the analysis of specific humidity, surface pressure, and temperature in the troposphere when assimilating RO data.