Toward improving space weather predictions based on low-frequency radio emissions

H. CREMADES; O. C. ST. CYR; F. A. IGLESIAS; H. XIE; M. L. KAISER; N. GOPALSWAMY

Göttingen

Conferencia; II Rocks and Stars Conference; 2017

Max Planck Institut für Sonnensystemforschung

Given the crucial consequences for Earth and its near-space activity, space weather forecasting has been a central subject of research in the past decades. This materializes nowadays in the rich and detailed data sets provided by multiple experiments specifically devoted to study the Sun-Earth connection. In this respect, coronal mass ejections (CMEs) constitute a key phenomenon, because their arrival to Earth and their associated MHD shocks are capable of triggering geomagnetic storms. Since it is not possible to predict the occurrence of an Earth-directed CME, once ejected it is of vital importance to ascertain its time of arrival at Earth with the best possible accuracy. Radio emissions produced by shock waves ahead of CMEs allow their tracking in the interplanetary (IP) medium as they drift down in frequency when traveling away from the Sun, and thus serve as a tool to trace forward travel times to Earth. Distance?time diagrams for 71 Earth-directed events during 1997?2007 resulting from the combination of white-light corona, IP Type-II radio, and in-situ data have led to the formulation of descriptive profiles of each CME?s journey toward Earth. Based on these data sets, two different methods for tracking and predicting the location of CME-driven IP shocks have been proposed and tested. The linear method, solely based on Wind/WAVES data, arises after key modifications to a pre-existing technique that linearly projects the drifting low-frequency Type-II emissions to 1 AU. This upgraded method improves forecasts of shock-arrival times by almost 50%. The second predictive method to obtain proxies of shock arrival time at Earth is proposed on the basis of constrains derived from the descriptive profiles. In addition, results on CME?radio emission associations, characteristics of IP propagation, and the relative success of the forecasting methods are discussed.