Computer Science – Performance
Scientific paper
Dec 2008
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008agufmsa41b..05t&link_type=abstract
American Geophysical Union, Fall Meeting 2008, abstract #SA41B-05
Computer Science
Performance
2400 Ionosphere (6929), 2447 Modeling And Forecasting
Scientific paper
The operational Utah State University (USU) Global Assimilation of Ionospheric Measurements (GAIM) Gauss- Markov Kalman filter (GAIM-GM) uses a physics-based model of the ionosphere and a Kalman filter as a basis for assimilating a diverse set of measurements in either real time or historical study modes. The USU GAIM-GM is now operational at the Air Force Weather Agency (AFWA) and the NASA Community Coordinated Modeling Center (CCMC). The model runs continuously in real time at AFWA and is available at the CCMC for "runs on request" for interested parties. The physics-based model is the Ionosphere Forecast Model (IFM), which is global and covers the E-region, F-region, and topside from 90 to 1400km. It takes account of five ion species (NO+, O2+, N2+, O+, H+). With the GAIM-GM model the ionospheric electron densities obtained from IFM are used as a background upon which perturbations are imposed based on available data and their errors. The density perturbations and associated errors evolve over time via a statistical Gauss-Markov process. The operational Gauss-Markov filter assimilates bottom-side electron density profiles from a variable number of ionosondes; slant TEC from a variable number of GPS satellite/ground station combinations; in-situ electron density from DMSP satellites; and certain line-of-sight UV radiances from satellite-based instruments. The operational model is currently being upgraded, primarily to improve model performance. This will be accomplished by splitting the global model into multiple regions that can run on multiple CPUs. Inter-regional communications within the model of both the state and covariance arrays results in model output equivalent to the monolithic global model, but at substantially increased speed. This allows additional data (both increased numbers of existing data types, and new data types) to be assimilated while maintaining real time operations. We will also discuss plans for future upgrades to the model.
Scherliess Ludger
Schunk Robert W.
Sojka Jan J.
Thompson Daniel C.
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