USU Gauss-Markov Model: High Resolution Regional Capability and Support for AFWA

Abstract

Award Number: N000140510321 http://tipweb.nrl.navy.mil/Projects/Cicas/default.htm LONG-TERM GOALSOur primary goal is to support the Air Force Weather Agency (AFWA) during the implementation of our Gauss-Markov Kalman Filter (GMKF) model for DoD applications. A secondary goal is begin the development of a high-resolution regional capability. OBJECTIVESIn an operational setting, the Gauss-Markov Kalman Filter model runs continuously and reconstructs the global electron density distribution as a function of time. The model automatically acquires the relevant data on the web, quality controls the data, inputs the data into the Kalman filter, and outputs a variety of ionospheric parameters at a 15-minute cadence. The data assimilated can include slant TEC from up to 1000 ground GPS receivers, bottom-side electron density profiles from 20 digisondes, in situ electron densities from several DMSP satellites, and integrated UV emissions from satellites. In practice, however, different amounts of data are assimilated, depending on the data availability. Therefore, one of our objectives is to determine what effect each data type has on the Kalman filter reconstruction. Another objective is to determine how much data are needed in a regional run of the Gauss-Markov model in order to achieve a desired accuracy. A third objective is to support Northrop Grumman, the Naval Research Laboratory (NRL), the Air Force Research Laboratory (AFRL), and AFWA in their validation and implementation efforts. APPROACHGauss-Markov and Full Physics Kalman Filter models were developed at USU as part of a DoD Multidisciplinary University Research Initiative (MURI) program and the USU effort was called Global Assimilation of Ionospheric Measurements (GAIM). The Gauss-Markov Kalman Filter (GMKF) model is based on the Ionosphere Forecast Model (IFM;Schunk et al., 1997), which covers the E-region, F-region, and topside ionosphere up to 1400 km, and takes account of six ion species (NO+, O2+, N2+, O+, He+, H+). However, the output of the model is a 3-dimensional electron density distribution at user specified times. In addition, auxiliary parameters are also provided, including NmF2, hmF2, NmE, hmE, slant and vertical TEC. In the Gauss-Markov Kalman Filter, the ionospheric densities obtained from the IFM constitute the background ionospheric density field on which perturbations are superimposed based on the available data and their errors. To reduce the computational requirements, these perturbations and the associated errors evolve over time with a 1