Application of feature calibration and alignment to high-resolution analysis: Examples using observations sensitive to cloud and water vapor

Alignment errors [i.e., cases where coherent structures ("features") of clouds or precipitation in the background have position errors] can lead to large and non-Gaussian background errors. Assimilation of cloud-affected radiances using additive increments derived by variational and/or ensemble methods can be problematic in these situations. To address this problem, the Feature Calibration and Alignment technique (FCA) is used here for correcting position errors by displacing background fields. A set of two-dimensional displacement vectors is applied to forecast fields to improve the alignment of features in the forecast and observations. These displacement vectors are obtained by a nonlinear minimization of a cost function that measures the misfit to observations, along with a number of additional constraints (e.g., smoothness and nondivergence of the displacement vectors) to prevent unphysical solutions. The method was applied in an idealized case using Weather Research and Forecasting Model (WRF) forecast fields for Hurricane Katrina. Application of the displacement vectors to the three-dimensional WRF fields resulted in improved predicted hurricane positions in subsequent forecasts. When applied to a set of high-resolution forecasts of deep moist convection over the central United States, displacements are able to efficiently characterize part of the ensemble spread. To test its application as an analysis preprocessor, FCA was applied to a real-data case of cloud-affected radiances of one of the Atmospheric Infrared Sounder (AIRS) channels. The displaced background resulted in an improved fit to the AIRS observations in all cloud-sensitive channels.