The effect of topography on the initial condition sensitivity of a mesoscale model
June 23, 2003
Errors in NWP model forecasts are typically due to deficiencies in the model formulation, inaccuracies associated with the numerical integration techniques, and errors in the specification of initial conditions. This study investigates the latter of these three issues and, in particular, elucidates the errors in the initial conditions due to inadequate data resolution. In a basic sense, for the atmosphere to be adequately sampled at a given length scale, it is not always necessary to increase the number of samples throughout the entire domain. Increased sampling resolution has the greatest benefit in the regions where gradients in the atmospheric conditions exist. Targeted observation techniques attempt to take advantage of this fact by using additional observations to improve the initial analysis in the regions that will have the most impact on forecast accuracy (Emanuel et al. 1995). The result is an economical means to reduce initial condition error and improve forecast accuracy. It is well known that terrain can serve as a localized forcing mechanism in high-resolution models. In addition to acting as a forcing mechanism, variations in terrain can also create strong gradients in the atmospheric fields of models using terrain following vertical coordinates. It is reasonable to assume that if these gradients were better represented in the initial conditions, forecasts accuracies could improve. The present study examines the relationship between terrain variability and the sensitivity of a high-resolution wind forecast to errors in the initial conditions in these areas. The background behind this study and a brief description of the terrain and atmospheric characteristics of the cases used in the experiments are presented in section 2. Initial condition sensitivity analysis results from the fifth generation Pennsylvania State University (PSU), National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) adjoint and forward models are contained in sections 3 and 4. A summary of the results and conclusions are found in section 5.