AE33B-0499
Empirical Modeling of Plasma Clouds Produced by the Metal Oxide Space Clouds (MOSC) Experiment

Wednesday, 16 December 2015
Poster Hall (Moscone South)
Todd Pedersen1, Ronald G Caton2, Daniel Miller2, Jeffrey M Holmes2 and Keith M Groves3, (1)Air Force Research Laboratory Albuquerque, Albuquerque, NM, United States, (2)Air Force Research Laboratory, Kirtland Afb, NM, United States, (3)Boston College/Inst Sci Res, Chestnut Hill, MA, United States
Abstract:
The Metal Oxide Space Clouds (MOSC) chemical release experiments employed the ALTAIR radar as a primary measurement of plasma density in the clouds. However, the radar provides only the local plasma density along the beam line of sight, and the measurements are of limited value without context to determine the location of the radar beam relative to the larger plasma cloud. We have constructed an empirical model of the cloud locations, shapes, and sizes as a function of time for both MOSC launches using fits to all-sky images recorded from near the launch site. When combined with ALTAIR radar measurements of local plasma density at the sampled point and ionosonde measurements of the peak plasma density, a robust 4-D representation of the plasma density can be derived and used to estimate ionization yields and to study impacts on the background ionosphere and RF propagation. Optical image data was fit to a 2-D Gaussian model to derive peak intensity, background, rotation of the cloud in the horizontal plane, and half-widths in the N-S and E-W directions. The optical images show a closely linear increase in half-width after the first minute or two. Very good agreement between the model and radar integrated total electron content (TEC) measurements are obtained with a simple exponential envelope to the peak TEC within the cloud, indicating that the optical distribution closely tracks the plasma density. Comparison of TEC with peak plasma density and the observed spatial dimensions of the cloud are used to estimate the rate of change in total electron number during the period of observation and to compare with predictions of prior theoretical and numerical models.