Comparison of Nighttime UV Radiances Obtained From the USU GAIM Data Assimilation Model With Limb Scan Observations From the LORAAS and SSULI Instruments

Physics

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2415 Equatorial Ionosphere, 2437 Ionospheric Dynamics, 2443 Midlatitude Ionosphere, 2447 Modeling And Forecasting

Scientific paper

Physics-based data assimilation models of the ionosphere were developed at Utah State University as the central part of a DoD MURI funded program called GAIM (Global Assimilation of Ionospheric Measurements). Recently, the Air Force Weather Agency (AFWA) has selected one of the USU GAIM models for its operational use and the same model will also be implemented at the Community Coordinated Modeling Center (CCMC) for scientific studies. The selected model is based on a physics-based model of the ionosphere and a Gauss-Markov Kalman Filter (GMKF) as a basis for assimilating a diverse set of real-time (or near real-time) observations. The physics-based model is the Ionospheric Forecast Model (IFM), which accounts of five ion species and covers the E-region, F-region and the topside from 90 to 1400 km altitude. Within the GMKF, the IFM derived ionospheric densities constitute a background density field on which perturbations are superimposed based on the available data and their errors. To better guide the assimilation of nighttime UV limb scan radiances, GAIM model results are compared with observed radiances from the LORAAS and SSULI instruments onboard the ARGOS and DMSP F16 satellites during different geophysical conditions. For this comparison the GAIM model assimilates slant TEC from a variable number of ground GPS sites, bottom-side N_e profiles from a variable number of ionosondes, and in situ N_e from DMSP satellites and provides the 3-D global plasma density distribution in 15-min increments. The ionospheric plasma densities obtained from our GAIM model are than used to calculate associated UV radiances which are directly compared with the observations. The emphasis of this comparison is on the observed and modeled day-to-day variability and its spatial extend as specified by the GAIM model. The comparison will cover several week- to month-long periods with both small and large day-to-day variability.

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