Using Unsupervised and Supervised Machine Learning Methods to Correct Offset Anomalies in the GOES‐16 Magnetometer Data-Reference-Cited by-同舟云学术

Using Unsupervised and Supervised Machine Learning Methods to Correct Offset Anomalies in the GOES‐16 Magnetometer Data

Published:2021-12 Issue:12 Volume:19 Page:
ISSN:1542-7390
Container-title:Space Weather
language:en
Short-container-title:Space Weather

Author:

Inceoglu F.¹²³^ORCID,Loto'aniu Paul T. M.¹²

Affiliation:

1. Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder Boulder CO USA

2. National Centers for Environmental Information National Oceanographic and Atmospheric Administration Boulder CO USA

3. GFZ German Research Centre for Geosciences, Wissenschaftpark “Albert Einstein” Potsdam Germany

Funder

National Oceanic and Atmospheric Administration

University of Colorado

Publisher

American Geophysical Union (AGU)

Subject

Atmospheric Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021SW002892

Reference30 articles.

1. Correcting the Arcjet Thruster Disturbance in GOES‐16 Magnetometer Data

2. Arcjet Thruster Influence on Local Magnetic Field Measurements from a Geostationary Satellite

3. A Gray‐Box model for a probabilistic estimate of regional ground magnetic perturbations: Enhancing the NOAA operational geospace model with machine learning;Camporeale E.;Journal of Geophysical Research,2020

4. Mountains versus valleys: Semiannual variation of geomagnetic activity

5. Enhancing SDO/HMI images using deep learning

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