Forecasting of the effects of thermospheric drag on satellites will be improved significantly with better modeling of space weather effects on the high-latitude ionosphere, in particular the Joule heating arising from electric field variability. We use a regression analysis to build a model of the ionospheric convection drift velocity which is driven by relatively few solar and solar wind variables. The model is developed using a solar cycle's worth (1997–2008 inclusive) of 5-min resolution Empirical Orthogonal Function (EOF) patterns 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 model, we use the percentage of explained variance P to see how well the model reproduces the EOF data. The final model is driven by four variables: (a) the interplanetary magnetic field component B _y, (b) the solar wind coupling parameter epsilon ε, (c) a trigonometric function of day-of-year, and (d) the monthly F _10.7 index. The model can reproduce the EOF velocities with a characteristic P = 0.7. The model and EOF data compare best around the solar maximum of 2001. (Figure presented.) is lower around solar minimum, due to occasional limitations in the geographical and temporal coverage of the SuperDARN measurements. This may indicate the need to modify our model around the minimum of the solar cycle. Our model has the potential to be used to forecast the ionospheric electric field using the real-time solar wind data available from spacecraft located upstream of the Earth.