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Forecasting high-latitude ionospheric convection and electric potential using SuperDARN data

Urheber*innen

Lam,  Mai Mai
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Shore,  Robert
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Chisham,  Gareth
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Freeman,  Mervyn
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Grocott,  Adrian
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Walach,  Maria-Theresia
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Orr,  Lauren
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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Zitation

Lam, M. M., Shore, R., Chisham, G., Freeman, M., Grocott, A., Walach, M.-T., Orr, L. (2023): Forecasting high-latitude ionospheric convection and electric potential using SuperDARN data, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-0120


Zitierlink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5016416
Zusammenfassung
Forecasting the effects of thermospheric drag on satellites will be improved significantly with more accurate modelling of space weather effects on the high-latitude ionosphere, in particular the Joule heating arising from electric field variability. This is the largest uncertainty in orbit prediction for satellites and space debris. We use a regression analysis to build a forecast model of the ExB ionospheric convection drift velocity and electric potential V, driven by relatively few solar and solar wind variables. The model is developed using a solar cycle’s worth (1997 to 2008 inclusive) of 5-minute resolution reanalysis data derived from Super Dual Auroral Radar Network (SuperDARN) line-of-sight observations of the convection velocity across the high-latitude northern hemisphere ionosphere. At key stages of development of the forecast model, we use the Priestley skill score to see how well the model reproduces the reanalysis dataset. The final forecast model is driven by four variables: (1) the interplanetary magnetic field component By, (2) the solar wind coupling parameter epsilon ε, (3) a trigonometric function of day of year, (4) the monthly f10.7 index. The forecast model can reproduce the reanalysis plasma velocities, with a characteristic skill score of 0.7. The forecast and reanalysis data compare best around the solar maximum of 2001. The forecast skill is lower around solar minimum, due to occasional limitations in the geographical and temporal coverage of the SuperDARN instrumentation. In addition, this may also indicate the need to modify our model of driving processes around the minimum of the solar cycle.