Physics-Based Kalman Filters for Neutral Density Specification and Forecasts

Details Physics-Based Kalman Filters for Neutral Density Specification and Forecasts Physics-Based Kalman Filters for Neutral Density Specification and Forecasts

Dec 2002

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002agufmsa22a..03f&link_type=abstract

American Geophysical Union, Fall Meeting 2002, abstract #SA22A-03

Physics

0350 Pressure, Density, And Temperature, 2447 Modeling And Forecasting, 2788 Storms And Substorms, 3337 Numerical Modeling And Data Assimilation, 3369 Thermospheric Dynamics (0358)

Scientific paper

With sufficient global data coverage, accurate neutral density specification can be accomplished using a Gauss-Markov Kalman filter where the state is assumed to gradually relax to climatology. However, in data sparse regions and in forecasting, the state propagator becomes more critical in the data assimilation process. The value of physical models as Kalman state propagators is that they can capture the time-dependent response to geomagnetic events, including the propagation of neutral density waves and the development of deep density holes. If sufficient observations are available to specify the current conditions and initialize the physical model, a short-term forecast is feasible. The problem in the use of physics-based models is that they require accurate specification of the spatial and temporal variation of the geomagnetic sources at high latitudes that are used to drive the model. The approach is to include the model drives in the Kalman state and optimize the model forcing as well as the initial conditions. Such models will be able to use the observed neutral density response to automatically adjust and optimize the model forcing functions. The best estimate of the current state can then be used as the initial conditions for a short-term forecast.

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