Mathematics – Logic
Scientific paper
Dec 2009
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009agufmsa43a1614b&link_type=abstract
American Geophysical Union, Fall Meeting 2009, abstract #SA43A-1614
Mathematics
Logic
[2447] Ionosphere / Modeling And Forecasting, [7924] Space Weather / Forecasting, [7934] Space Weather / Impacts On Technological Systems, [7959] Space Weather / Models
Scientific paper
This paper discusses an approach to assessing the relative utilities of the data types that are assimilated by global models of the ionosphere such as the Utah State University GAIM-GM model, which is used operationally by the Air Force Weather Agency. The general objective of this class of models could be stated as “to specify accurate vertical electron density profiles (EDPs) that can provide temporal and spatial representation of ionospheric features with scale sizes approximately equal to the size of the grid cells and dynamics up to half the data sampling rate.” To provide an approximate ranking of data types, we have developed an equation wherein values for several attributes are assigned to each data type. The attributes include data quality, correlation length, relevance (to the Objective EDPs), and latency. Clearly, the resultant ranking depends critically on the procedures used for obtaining the values assigned to these attributes. Approaching the data utility issue from the global perspective, we have defined an initial set of values for the attributes based on data sources available today. In this initial ranking, we found that the data type with the greatest utility, i.e., the one that contributes most to the objective, was ground-based observations of slant TEC (STEC) made using GPS satellites. We found in situ observations made on board scientific satellites to be at the opposite end of the spectrum of utility. Factors that drive down the utility of such observations include (a) the observations have limited spatial and temporal coverage, (b) the observations provide just one point on a 1500 km profile, and (c) the observations may arrive at the modeling site with data latencies of ~90 minutes. However, one mitigating attribute is that in situ observations made over ocean areas have an intrinsic merit because they are one of the few data types available there. We will discuss in detail our equation, the chosen attributes, and our initial process for assigning values to the attributes. We will also indicate how results may vary for alternate processes and data application perspectives.
Bishop G. J.
Decker Dwight T.
McNamara Leo F.
Welsh James
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