Aeronomy: Challenges of Data Assimilation

Details Aeronomy: Challenges of Data Assimilation Aeronomy: Challenges of Data Assimilation

Dec 2005

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2005agufmsa42a..03s&link_type=abstract

American Geophysical Union, Fall Meeting 2005, abstract #SA42A-03

Statistics

2415 Equatorial Ionosphere, 2437 Ionospheric Dynamics, 2443 Midlatitude Ionosphere, 2447 Modeling And Forecasting, 2475 Polar Cap Ionosphere

Scientific paper

Data assimilation models have been widely used in meteorology and oceanography for several decades, and since their introduction there has been a continued improvement in weather prediction. In contrast, the use of data assimilation models in aeronomy has gained prominence only during the last few years. Nevertheless, there has been significant progress in the development of physics-based, Kalman filter, data assimilation models for the global ionosphere. The state-of-the-art models can assimilate several data types, including in situ electron densities from satellites, bottomside electron density profiles from a network of 100 ionosondes, line-of-sight Total Electron Content (TEC) measurements between as many as 1000 ground stations and the GPS satellites, TECs via occultations between low-altitude and high-altitude orbiting satellites, and line-of-sight ultraviolet emission data. However, the construction of data assimilation models is not always straightforward for several reasons: (1) Although there are a lot of data available via the world wide web, the data quality is generally not adequate for data assimilation models and data errors are not routinely provided; (2) There is a limited number of real-time data sources for specification and forecast models; (3) The application of a rigorous Kalman filter is not feasible and approximations are necessary; (4) The physics-based models have uncertain parameters that need to be determined and/or have missing physics; and (5) Validation of data assimilation models requires massive independent data sets so that statistics can be done with regard to the accuracy of the models. These and other challenges related to the development of data assimilation models will be discussed.

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