It is well known that topography can significantly influence the forecasts derived from mesoscale model simulations. Elevated terrain often creates sharp gradients in the model fields influencing the physical processes captured by the model. The present study characterizes the topography's effect on the model sensitivity to initial conditions. This knowledge of initial condition sensitivity can then be used to direct the placement of additional targeted observations. The goal of this study is to identify the locations where a targeted observation might have the greatest positive impact on the forecast.

Simulations from the Pennsylvania State University (PSU), National Center for Atmospheric Research (NCAR) Mesoscale Model Version 5 (MM5) and its adjoint are used to characterize the locations, variables, and magnitudes of initial condition sensitivity that have the most significant impact on a surface wind forecast. This study examines a relatively simple case where an idealized mountain surrounded by a flat plain is located upwind of the forecast verification region. A series of 36 adjoint simulations are used to diagnose the variability of initial condition sensitivity over regions of elevated and flat terrain. The results of the adjoint simulations are then used to direct the perturbation of the initial analysis used in a second series of 36 forward model simulations illustrating the sensitivity of the surface wind forecast to comparable adjustments made over both elevated and flat terrain. The results suggest that when elevated terrain is present upstream of the target forecast area, a greater benefit (in terms of forecast accuracy) can be made by targeting additional observations in the regions containing varying terrain than regions where the terrain is relatively flat.