Coronal mass ejection arrival forecasting with the drag-based assimilation of satellite observations

Abstract: Forecasting the arrival of coronal mass ejections (CMEs) is vital for protecting satellites, power systems, and human spaceflight. We present HELIOPANDA: Heliospheric Observer for Predicting CME Arrival via Nonlinear Drag Assimilation, a framework that integrates the Drag-Based Model (DBM) with spacecraft observations using iterative parameter estimation and Kalman filter assimilation. We introduce a method for estimating the solar wind speed $w$ and drag parameter $\gamma$, two key but usually unknown quantities controlling CME propagation, through direct solutions of the DBM equations. We tested the method on 4,480 synthetic CME profiles spanning CME speeds of $200-3500$ km/s, solar wind speeds of $250-800$ km/s, and drag parameters of $0.1-1.0\times10<sup>{-7}$ km$<sup>{-1}$. The results demonstrate that the framework provides accurate reconstructions of the DBM input parameters, providing a solid basis for in-situ and remote-sensing applications. By testing a single virtual spacecraft positioned at nine distances along the Sun-Earth line, HELIOPANDA achieved arrival-time errors as low as 0.6 hours for a 600 km/s CME and 1 hour for a 2500 km/s CME when the spacecraft was located 30 million km from the Sun. We developed a Kalman filter framework to assimilate noisy heliospheric data into the DBM, enabling recursive updates of CME kinematics and robust estimates of $w$ and $\gamma$, and yielding Earth and Mars arrival-time predictions within $1-2$ hours using 160 simulated hourly measurements. By combining DBM, parameter recovery, and data assimilation, HELIOPANDA provides a pathway to real-time, multi-point CME forecasts, suited to observations from Solar Orbiter, Parker Solar Probe, PUNCH, and planned L4/L5 missions.