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Propagate L1 solar wind measurements to Earth with the help of machine learning

Baumann, Carsten and McCloskey, Aoife (2021) Propagate L1 solar wind measurements to Earth with the help of machine learning. Applications of Statistical Methods and Machine Learning in the Space Sciences, 17.- 21. Mai 2021, virtuell.

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Official URL: http://spacescience.org/workshops/mlconference2021.php


The propagation of solar wind measurements from L1 to the bow shock nose of Earth is the basis of the frequently used OMNI dataset. Depending on the solar wind speed, the propagation time delay from L1 to Earth lies between 20 and 90 minutes. In this study we present a machine learning algorithm that is suitable to predict the solar wind propagation delay between Lagrangian point L1 and the Earth. This work introduces the proposed algorithm and investigates its applicability to propagate ACE data to Earth. The propagation delay is measured from interplanetary shocks passing the Advanced Composition Explorer (ACE) first and their sudden commencements within the magnetosphere later, as recorded by ground-based magnetometers. Overall 380 interplanetary shocks with data ranging from 1998 to 2018 builds up the database that is used to train the machine learning model. We investigate two different feature sets. The training of one machine learning model will use all three components of solar wind speed , the other only bulk solar wind speed. Both feature sets also contain the position of the spacecrafts. The major advantage of the machine learning approach is its simplicity when it comes to its application. After training, values for the different features have to be fed into the algorithms only and the evaluation of the propagation delay can be continuous. Both machine learning models will be used to propagate ACE data to Earth. The propagated ACE measurements are compared to OMNI data during different solar wind conditions. Future assessments will include a comparisons of propagated solar wind data to satellite measurements of the IMF just outside of Earth magnetosphere.

Item URL in elib:https://elib.dlr.de/143599/
Document Type:Conference or Workshop Item (Speech)
Title:Propagate L1 solar wind measurements to Earth with the help of machine learning
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Baumann, Carstencarsten.baumann (at) dlr.dehttps://orcid.org/0000-0001-7104-5992
McCloskey, AoifeAoife.McCloskey (at) dlr.dehttps://orcid.org/0000-0002-4830-9352
Refereed publication:No
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Keywords:machine learning, solar wind propagation
Event Title:Applications of Statistical Methods and Machine Learning in the Space Sciences
Event Location:virtuell
Event Type:international Conference
Event Dates:17.- 21. Mai 2021
Organizer:Space Science Institute
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Project Space weather research
Location: Neustrelitz
Institutes and Institutions:Institute for Solar-Terrestrial Physics > Space Weather Impact
Deposited By: Baumann, Carsten
Deposited On:13 Sep 2021 15:22
Last Modified:13 Sep 2021 15:22

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