Solar Wind to Radiation Belt Energetic Electron Response Functions Using Multi-Channel Prediction Filters

Details Solar Wind to Radiation Belt Energetic Electron Response Functions Using Multi-Channel Prediction Filters Solar Wind to Radiation Belt Energetic Electron Response Functions Using Multi-Channel Prediction Filters

Dec 2002

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002agufmsa22a..10r&link_type=abstract

American Geophysical Union, Fall Meeting 2002, abstract #SA22A-10

Physics

2720 Energetic Particles, Trapped, 2722 Forecasting, 2753 Numerical Modeling, 2784 Solar Wind/Magnetosphere Interactions

Scientific paper

A number of recent studies have used linear and nonlinear data filtering techniques to generate response functions that characterize, spatially and temporally, the coupling between solar wind velocity and energetic electron flux variations in the Earth's magnetosphere. This data intensive modeling technique is not new, but has historically been of limited use because it requires long, continuous, and relatively high-quality data sets with which to train the filters. The SAMPEX satellite provides just such a data set with its 2-6MeV electron channel (ELO). The ELO data is available as either daily or orbit-averaged flux measurements, in 0.1 L-shell bins, from L = 0.1 to L > 8.0, providing both high time and spatial resolution flux measurements. In addition, autoregressive (AR) filters have been shown to behave as an efficient multi-bandstop filter, capable of effectively removing diurnal variations in electron flux data without losing short time scale dynamics. This means that impulse response functions can be determined at time scales down to ~90 minutes (the orbit period of SAMPEX, and effective sampling period of the data used). Finally, adaptive optimization algorithms have been used to provide real-time updates to the model parameters with each new solar wind and SAMPEX measurement, demonstrating the non-linearity and non-time stationarity of the Earth's radiation belt response to solar wind inputs. Previous studies will be reviewed in the context of this being a purely data-based assimilation technique, and expanded to include additional solar wind inputs via multi-channel, autoregressive prediction filters. The resulting models offer a visually intuitive description of the dominant dynamical drivers of electron loss and enhancement in space (L-shell) and time, in addition to providing a robust and reasonably accurate short-term space weather forecasting tool.

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