Modeling the Thermosphere As a Driven-Dissipative Thermodynamic System

Tuesday, 16 December 2014
William Frey1,2, Chin S Lin3, Ariel O Acebal2 and Matthew Garvin2, (1)Air Force Weather Agency Boulder, 2d Weather Squadron/OL-P, Boulder, CO, United States, (2)Air Force Institute of Technology - AFIT, Wright Patterson AFB, OH, United States, (3)Emeritus, Air Force Research Laboratory, RVBXI, Kirtland AFB, NM, United States
Thermospheric density impacts satellite position and lifetime through atmospheric drag. More accurate specification of thermospheric temperature, a key input to current models such as the High Accuracy Satellite Drag Model, can decrease model density errors. This paper improves the model of Burke et al. (2009) to model thermospheric temperatures using the magnetospheric convective electric field as a driver. In better alignment with Air Force satellite tracking operations, we model the arithmetic mean temperature, T1/2, defined by the Jacchia (1977) model as the mean of the daytime maximum and nighttime minimum exospheric temperatures occurring in opposite hemispheres at a given time, instead of the exospheric temperature used by Burke et al. (2009). Two methods of treating the solar ultraviolet (UV) contribution to T1/2 are tested. Two model parameters, the coupling and relaxation constants, are optimized for 38 storms from 2002 to 2008. Observed T1/2 values are derived from densities and heights measured by the Gravity Recovery and Climate Experiment satellite. The coupling and relaxation constants were found to vary over the solar cycle and are fit as functions of F10.7a, the 162 day average of the F10.7 index. Model results show that allowing temporal UV variation decreased model T1/2 errors for storms with decreasing UV over the storm period but increased T1/2errors for storms with increasing UV. Model accuracy was found to be improved by separating storms by type (coronal mass ejection or co-rotating interaction region). The model parameter fits established will be useful for improving satellite drag forecasts.