Comparison of the path-weighted Cn2 derived from time-lapse imagery and weather radar

Irradiance based techniques are not suitable for profiling over long, nearly horizontal paths through atmospheric turbulence since they suffer from saturation effects. Alternate techniques that can provide reliable turbulence information over strong turbulence paths are currently being investigated. Two such approaches are introduced here. The first approach is a phase-based technique that uses the turbulence induced random motion in time-lapse images of a distant scene to estimate the path-weighted Cn2. An imaging experiment was conducted at the Air Force Institute of Technology to demonstrate this approach. A tripod-mounted digital camera captured images of a distant building every minute. Two different components of motion were apparent in the imagery: the random, faster motion due to atmospheric turbulence and the slower, vertical motion due to changes in the average refractive index gradient along the path. A correlation algorithm was used to measure the image shifts. The technique uses a derived set of path weighting functions that depend on the size of the imaging aperture and the patch size in the image whose motion is being tracked. The second method estimates Cn2 values at different locations along a path using a combination of weather (NEXRAD) radar and numerical weather prediction (NWP) data. Two techniques have been used to derive the estimates. The first uses radar doppler information to determine the eddy thermal dissipation rate and combining this with NWP gradients to form a Cn2 estimate. The second technique attempts to correct Cn2 derived from radar clear-air reflectivity for noise and clutter and wavelength. The weights derived in the time-lapse imaging method have been applied to the noise and wavelength corrected NWP-radar derived Cn2 profile to obtain the path-weighted value. The path-weighted estimates obtained using the optically-based, time-lapse imaging and weather radar-based methods have been compared. Both approaches show great potential in estimating turbulence strengths over strong turbulence paths.