Many of the recent conflicts where the United States (US) military forces have been deployed are regions that contain complex terrain (i.e. Korea, Kosovo, Afghanistan, and northern Iraq). Accurate weather forecasts are critical to the success of operations in these regions and are typically supplied by numerical weather prediction (NWP) models like the US Navy NOGAPS, CAOMPS, and US Airforce MM5. Unfortunately the weather observations required to generate accurate initial conditions needed by these models are often not available. In these cases it is desirable to deploy additional weather sensors. The question then becomes: Where should the military planners deploy their sensor resources? This study demonstrates that knowledge of just the terrain within the model domain may be a useful factor for military planners to consider. For NWP, model forecast errors in mountainous areas are typically thought to be due to poorly resolved terrain, or model physics not suited for use in a complex terrain environment. Recent advances in computational technology are making it possible to run these models at resolutions where many of the significant terrain features are now being well resolved. While terrain can be accurately specified, often the gradients in wind, temperature, and moisture fields associated with the higher resolution terrain are not. As a result, initial conditions in complex terrain environments are not be adequately specified. Since not all initial condition errors contribute significantly to model forecast error, knowledge of terrain induced NWP model forecast sensitivity may be important when developing and deploying a weather sensor network to support a regional scale NWP model. The terrain induced model sensitivity can provide an indication of which variables in the initial conditions have a significant influence on the forecast and where initial conditions need to be most accurate to minimize model forecast error. A sensor network can then be designed to minimize these errors by deploying critical sensors in sensitive locations, thereby reducing relevant initial condition error without the costly deployment of a high-density sensor network. This is similar to the targeted observation technique first suggested by Emanuel et al. (1995), except that in this example the targeted observations would be designed to reduce initial condition error associated with poorly resolved atmospheric features created by the terrain. This paper is organized as follows. Section 2 contains a brief description of the data collection effort designed to support this study. The experimental design and the specifics of the case used in this study are described in section 3. The analysis and results from both the forward and adjoint simulations are presented in section 4. Section 5 contains a summary of the results, and a brief discussion of their implications.