An Examination of SuperDARN Backscatter Modes Using Machine Learning Guided by Ray‐Tracing
Author:
Affiliation:
1. Bradley Department of Electrical and Computer Engineering Virginia Tech Blacksburg VA USA
2. Thayer School of Engineering Dartmouth College Hanover NH USA
Funder
Division of Atmospheric and Geospace Sciences
National Science Foundation
Dartmouth College
Publisher
American Geophysical Union (AGU)
Subject
Atmospheric Science
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1029/2022SW003130
Reference57 articles.
1. International geomagnetic reference field: The thirteenth generation;Alken P.;Earth Planets and Space,2021
2. Incorporating Physical Knowledge Into Machine Learning for Planetary Space Physics
3. Observations of ionospheric convection from the Wallops SuperDARN radar at middle latitudes
4. Predictions of HF system performance for propagation through disturbed ionospheres measured using low-Earth-orbit satellite radio beacon tomography
5. International Reference Ionosphere 2016: From ionospheric climate to real‐time weather predictions
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1. Dynamics of Mid‐Latitude Sporadic‐E and Its Impact on HF Propagation in the North American Sector;Journal of Geophysical Research: Space Physics;2023-09
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