SSUSI Bibliography



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Found 2 entries in the Bibliography.


Showing entries from 1 through 2


2015

Methodology of evaluating the science benefit of various satellite/sensor constellation orbital parameters to an assimilative data forecast model

A methodology for evaluating the science benefit of adding space weather sensor data from a modest number of small satellites to the Utah State University Global Assimilation of Ionospheric Measurements\textemdashFull Physics (GAIM-FP) model is presented. Three orbital scenarios are presented, two focusing on improved coverage of narrowly specified regions of interest, and one on global coverage of the ionosphere as a whole. An Observing System Simulation Experiment is used to obtain qualitative and quantitative results of the impact of the various orbital scenarios on the ionospheric specifications. A simulated \textquotedbllefttruth\textquotedblright run of the ionosphere is obtained from a first principle model of the ionosphere/plasmasphere model and used to generate global simulated Global Positioning Satellite total electron content (GPS-TEC) data as well as in situ plasma density observations. Initially, only GPS data were assimilated by GAIM-FP, and the results of this limited run were compared to the truth run. Next, the simulated in situ plasma densities corresponding to our three orbital scenarios were assimilated together with the GPS data, and the results were compared to both the truth run and the limited GPS-TEC only GAIM-FP run. These model simulations have shown that adding a constellation of small satellites/sensors in addition to global TEC inputs does indeed converge the GAIM-FP model closer to truth in the situations described.

Balthazor, Richard; McHarg, Matthew; Enloe, Lon; Mueller, Brandon; Barnhart, David; Hoeffner, Zachary; Brown, Robert; Scherliess, Ludger; Wilhelm, Lance;

Published by: Radio Science      Published on: 04/2015

YEAR: 2015     DOI: 10.1002/2014RS005426

Ionosphere; modeling

2014

Global Assimilation of Ionospheric Measurements (GAIM) - Gauss Markov (GM) Model: Improved Specifications with Multiple Data Types

The Earth\textquoterights ionosphere is a highly dynamic region that is almost constantly in a state of flux. Solar radiation, geomagnetic activity, chemical reactions, and natural dynamics all act to perturb the state of the ionosphere. The ionosphere changes on time scales of hours to days, with the fine-scale ionospheric structures that are frequently observed lacking in global physics-based models due to time-step and spatial resolution constraints. To properly specify the ionosphere, data is needed, thus data assimilation. The Utah State University GAIM-GM model uses a data assimilation method to correct a physics-based model of the ionosphere using 5 different data types, divided into 9 different data sources. Multiple data types are necessary because the data from any individual data source will not be sufficient for global reconstructions. The GAIM-GM specification (in real-time) can then be used to correct for ionospheric propagation delays, thereby improving geo-location and communications. The focus here is to show the quantitative effects that multiple data types have on GAIM-GM ionospheric specifications for a relatively quiet day (April 19) in 2012.

Gardner, L.; Schunk, R.; Scherliess, L.; Sojka, J.; Zhu, L.;

Published by: Space Weather      Published on: 11/2014

YEAR: 2014     DOI: 10.1002/2014SW001104

Data Assimilation; Ionosphere; modeling



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