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Prediction of the AU , AL , and AE indices using solar wind parameters
Author(s) -
Luo Bingxian,
Li Xinlin,
Temerin M.,
Liu Siqing
Publication year - 2013
Publication title -
journal of geophysical research: space physics
Language(s) - English
Resource type - Journals
eISSN - 2169-9402
pISSN - 2169-9380
DOI - 10.1002/2013ja019188
Subject(s) - electrojet , solar wind , interplanetary magnetic field , earth's magnetic field , physics , atmospheric sciences , geomagnetic storm , ionosphere , interplanetary spaceflight , northern hemisphere , computational physics , environmental science , geophysics , magnetic field , quantum mechanics
An empirical model that predicts the AU index, a measure of the Earth's east electrojet, derived from magnetometers in the Northern Hemisphere, is introduced together with an improved AL model which, combined with the AU model, produces an AE model. All models are based on solar wind and interplanetary magnetic field parameters and the solar F 10.7 index for the years 1995 to 2001. The linear correlation coefficient (LC) between the 10 min averaged AU index and the model is 0.846 for the years 1995–2001. The LC for the updated AL model is 0.846, and using AE = AU − AL , the LC for the AE model is 0.888. The better LC of the AE model over AU and AL models is because AU and AL are better correlated than their errors. The models show that (1) solar ultraviolet intensity plays a significant role in auroral activity by changing the ionospheric conductivity and scale height. Increasing solar ultraviolet intensity increases the eastward electrojet as measured by AU but decreases the westward electrojet as measured by AL ; (2) solar wind dynamic pressure also affects the auroral electrojet indices, although they are much more strongly dependent on the solar wind velocity and the interplanetary magnetic field; (3) AU and AL behave differently during geomagnetic storm main phases: AU , unlike AL , can drop to a small value during storms; (4) the longer averaged auroral electrojets indices can be predicted well but shorter timescale variations are less predictable.