Forecasting High-Latitude Ionospheric Convection Using the BAS Reanalysis of SuperDARN Data

Forecasting of 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 ionospheric convection E×B drift velocity which is 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 B_y, (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.