Inner-Magnetospheric Data Assimilation With an Ensemble Kalman Filter

Details Inner-Magnetospheric Data Assimilation With an Ensemble Kalman Filter Inner-Magnetospheric Data Assimilation With an Ensemble Kalman Filter

May 2005

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2005agusmsm43b..04n&link_type=abstract

American Geophysical Union, Spring Meeting 2005, abstract #SM43B-04

Physics

2700 Magnetospheric Physics, 2722 Forecasting, 2730 Magnetosphere: Inner, 2753 Numerical Modeling

Scientific paper

The Ensemble Kalman Filter is a data assimilation technique that incorporates observational data into a physical model to estimate the state of a system, using Monte Carlo methods to estimate the model error statistics. The application of this technique to the inner-magnetospheric kilovolt plasma environment is explored in a series of identical twin experiments, using the Magnetospheric Specification Model (MSM) to represent the inner-magnetospheric state. Simulated IMAGE/HENA data is incorporated into the MSM, using a forward modeling algorithm to relate the observed ENA intensity to MSM variables. Results from experiments with different ensemble sizes, sampling strategies, and analysis schemes are presented, and the sensitivity of the assimilation to various sources of error is investigated.

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