摘要：The Earth's 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 are needed, thus data assimilation. The Utah State University Global Assimilation of Ionospheric Measurements-Gauss Markov (GAIM-GM) model uses a data assimilation method to correct a physics-based model of the ionosphere using five different data types, divided into nine 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 geolocation 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 (19 April) in 2012.
