Examining the sensitivity of simulated surface temperatures due to meteorological conditions

The U.S. Army Engineer Research and Development Center (ERDC) has developed a suite of models that replicate the signicant geo-physical processes which aect the thermal signatures sensed by infrared imaging systems. This suite of models also includes an electro-optical/infrared (EO/IR) sensor model that produces synthetic thermal imagery. The EO/IR sensor model can be adapted to replicate the performance of other infrared sensor systems as well. It is well known that eld-collected IR imagery can be in uenced by the micro-topographic features of a particular location. As a result, the performance of automated target recognition algorithms and decisions based on their results can also be aected. Other signicant contributors to false alarms and issues with probabilities-of- detection include the relative locations of vegetation and local changes in soil types or properties. For example, a change in the retention of soil moisture alone is known to contribute to false alarms due to changes in radiative and thermal properties of wet versus dry soil. Many aspects of eld data collection eorts (weather, soil uniformity, etc.) cannot be controlled nor changed after the fact. Within a computational framework, however, plant and object locations, as well as weather patterns can, all be changed. In this work, the sensitivity of simulated IR imagery will be examined as it relates to initial states and boundary forcing terms due to weather conditions. Dierent approaches to these inputs will be examined using the computational testbed developed at the ERDC.