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Measurements of the solar wind propagation delay for L1 to Earth based on ACE and ground-based magnetometer data

<p>This database is the basis for the analysis described in the manuscript</p> <p>&#39;Timing of the solar wind propagation delay between L1 and Earth based on machine learning&#39;</p> <p>published in Journal of Space Weather and Space Climate.</p> <p>&nbsp;</p> <p><a href="https://doi.org/10.1051/swsc/2021026">https://doi.org/10.1051/swsc/2021026</a></p> <p>&nbsp;</p> <p>The database contains the times of 380 interplanetary shocks detected at ACE (T_ACE) which also caused a sudden impulse (T_SI) in the magnetosphere based on ground-based magnetometer data. This information can be found in &#39;measurement_SW_propagation.txt&#39;.</p> <p>final_learningset_SWdelay.pickle contains the corresponding ACE data (solar wind speed, ACE position) at time T_ACE for each of the 380 cases.</p> <p>The datafile can be loaded with Python as follows:</p> <p>import pickle<br> with open(&#39;final_learningset_SWdelay.pickle&#39;, &#39;rb&#39;) as f:<br> &nbsp;&nbsp;&nbsp; [learnvector_o,learnvector_m,learnvector_s,learnvector,timevector]=pickle.load(f)</p> <p>&nbsp;</p> <p>The content is described as follows:</p> <p>learnvector_o - contains a list of ACE data in its original form, ordering [&#39;rx&#39;,&#39;ry&#39;,&#39;rz&#39;,&#39;vx&#39;,&#39;vy&#39;,&#39;vz&#39;]</p> <p>learnvector_m - median of each feature</p> <p>learnvector_s - standard deviation of each feature</p> <p>learnvector - contains an array of the standardized data, which have been used to train the ML models.</p> <p>timevector - contains an array with the vector delay in seconds(first column), flat delay in seconds (second column), and measured solar wind propagation delay in seconds (third column)</p>