Control room operators play an important role in mitigating the effects of disturbances on the power grid. We focus on solar storms and geomagnetic disturbances in this paper. Providing operators with advanced tools that reveal relationships between variables that characterize events in real-time enables faster response. For complex events such as GIC (geomagnetically induced currents), the output of the tool should validate domain knowledge to build trust with the operator. In this paper, we apply association rule mining to discover relationships between physical variables from multiple sources of data relevant to GMDs. We aligned features extracted from ACE (Advanced Composition Explorer) satellite measurements with features extracted from terrestrial magnetometer measurements. We mapped these features during solar storms in 2015 to GIC grid event data processed from synchrophasor streams. Then, we discovered relationships or rules between features relevant for predicting the effects of solar storms on the grid and evaluated our results on the 2015 data. By looking at the predictive value of selected features, we find that features most relevant to GIC vary depending on the prediction latency, reflecting the complex, physical dynamics of GMDs.