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Dataset: Random forest models of ultra-low frequency magnetospheric wave power.

Research datakeyboard_double_arrow_right Dataset 26 May 2020Publisher:ZenodoFunded by:UKRI | Modelling the acceleratio..., UKRI | Reading Solar System Scie..., UKRI | STFC Reading 2017 DTP

Authors: Bentley, S N; Stout, J; Bloch, T; Watt, C E J;

doi: 10.5281/zenodo.3828506 , 10.5281/zenodo.3828505

Dataset: Random forest models of ultra-low frequency magnetospheric wave power.

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Abstract

Predictive models of ground-based ultra-low frequency (ULF, 1-15 mHz) wave power, corresponding to magnetospheric waves. The series of decision tree ensembles (random forests) are dependent on solar wind properties, latitude and azimuthal angle around the Earth (magnetic local time, MLT).

Related Organizations

University of Reading
United Kingdom

Keywords

magnetospheric, dawn-dusk asymmetry, machine learning, space physics, decision tree, ground magnetometer, radiation belt, random forest, ULF waves

Impact byBIP!

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	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average