Proceedings Article | 15 July 1997
KEYWORDS: Clouds, Atmospheric modeling, Scattering, Data modeling, 3D modeling, Visualization, Sensors, Radiative transfer, Aerosols, Atmospheric particles
Visualization technologies are improving our ability to assess the effectiveness of the warfighter on today's battlefield. Increasingly, our ability to predict the behavior and performance of competing systems is being facilitated by simulations. These predictions typically involve visual and sensor simulations, but they may also be used for mission performance generalizations. A key link in this analysis involves the assessment of real optics, ATR algorithms, and observers under the changing influence of the natural atmosphere. The effects of the atmosphere can be as diverse as target contrast degradation, dynamic range influences of light and shadows in the viewed field, cloud free lines of sight, and optical turbulence. However, in general, the modeling and simulation community has only treated limited versions of the full influences of the atmosphere. In some cases these influences have been modeled using cartoon-like emulations of reality, bereft of physical content. Physics-based solutions are usually bypassed in the drive for near-realtime results. In this paper, we discuss the need for three-dimensional (3-D) solutions to near-earth atmospheric representation, describe a set of physics-based programs designed to generate cloudy- hazy atmospheric scenarios, run a robust 3-D radiative transfer (RT) model, and present this representation for visualization by a perspective view generator of the resulting radiance fields. The cloud fields are generated with a stochastic model that uses cloud layer height information, cloud type, and vertical sounding profile data. The output from this model is coupled to standard vertical haze profiles to produce a 3-D field of atmospheric properties. The official Army Research Laboratory discrete ordinates method (DOM) RT code, contained in the WAVES modeling package, has difficulties with dense cloud conditions. Here, we discuss a recommended upgrade to WAVES in the form of a specially designed RT code that accesses these cloud/haze data and is insensitive to cloud density variations. This feature allows it to effectively simulate effects in and around natural clouds. Further processing compresses and interprets the outputs of the RT code for a given sensor spectral response. Point to point calculations can then be performed on the resulting database for path characterization, including path radiance and contrast transmission calculations. These can be used in assessing system performance for each color channel of a sensor, or in visualizing the cloud fields themselves.
