This talk presents a developing framework for estimating magnetosphere–ionosphere–thermosphere coupling by combining AMPERE field-aligned current maps with SuperDARN plasma-drift measurements. The method uses the MIX electrodynamics solver and optimizes ionospheric conductance, beginning from a machine-learning precipitation model trained on DMSP observations together with a solar-EUV conductance specification. Preliminary results show that AMPERE-only solutions reproduce major convection features observed along DMSP satellite tracks, while adding SuperDARN measurements modifies the magnitude and location of the inferred dayside conductance to improve agreement with observed drifts. The approach provides spatially resolved estimates of electric fields, currents, particle energy input, and electromagnetic energy deposition. Remaining challenges include representing plasma transport, diffuse precipitation, and low-conductance regions, as well as systematic validation of the solutions against independent satellite and ground-based observations