The Role of the IMF in Forecasting CME-Induced Terrestrial Space Weather

Details The Role of the IMF in Forecasting CME-Induced Terrestrial Space Weather The Role of the IMF in Forecasting CME-Induced Terrestrial Space Weather

Dec 2009

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009agufmsm54a..07j&link_type=abstract

American Geophysical Union, Fall Meeting 2009, abstract #SM54A-07

Physics

[2101] Interplanetary Physics / Coronal Mass Ejections, [2164] Interplanetary Physics / Solar Wind Plasma, [2784] Magnetospheric Physics / Solar Wind/Magnetosphere Interactions, [7900] Space Weather

Scientific paper

The interplanetary magnetic field (IMF) is the major contributor to terrestrial space weather induced by coronal mass ejections (CME). Nowcasting of these events has become quite successful. Our ability to forecast such events on significant time scales (on the order of one day), however, remains a rather elusive goal. In situ observations of the solar wind at the L1 point provide an advance warning of no more than 1 hour. The auto-correlation times of the solar wind are usually too short to enable data driven predictions with sufficient lead times. The resolution of both solar wind propagation modeling from Sun to Earth and solar remote sensing observations is insufficient to predict the CME internal structure at the level needed to predict terrestrial space weather conditions. A practical terrestrial space weather forecast for CMEs will require an integrated approach where remote sensing, modeling and in situ components work together to make the resulting framework stronger than the sum of the individual parts. In this presentation, we will concentrate on in situ observations of the IMF viewed in this context. Ultimately, our goal is to enable the development of integrated, data-driven space weather forecasting. For this, we need to understand the temporal patterns of the IMF inside CMEs at relevant time scales. To uncover these patterns we are classifying solar wind time series segments of various lengths using specially trained neural networks (Kohonen Self-Organizing Maps). We present how well our networks can classify CMEs and their subclasses as a function of the length of these time series segments and the variable types (IMF, solar wind properties) we use. Then we discuss the probability with which the future temporal development of the solar wind can be predicted using those time segment patterns. To close the presentation, we discuss how we envision to correlate our purely empirical CME time segment classifications of the IMF and solar wind measurements with remote sensing solar observations. Combining these correlations with solar wind propagation estimates could lead to probability estimates of IMF temporal developments at L1 and Earth based on remote sensing solar observations alone.

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