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 the Heliospheric Observer for Predicting CME Arrival via Nonlinear Drag Assimilation (HELIOPANDA), 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 γ , two key but usually unknown quantities controlling CME propagation, through direct solutions of the DBM equations. We tested the method on 4480 synthetic CME profiles spanning CME speeds of 200–3500 km s −1 , solar wind speeds of 250–800 km s −1 , and drag parameters of 0.1–1.0 × 10 −7 km −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 hr for a 600 km s −1 CME and 1 hr for a 2500 km s −1 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 γ , and yielding Earth and Mars arrival-time predictions within 1–2 hr using 160 simulated hourly measurements. By combining DBM, parameter recovery, and data assimilation, HELIOPANDA provides a pathway to real-time, multipoint CME forecasts, suited to observations from Solar Orbiter, Parker Solar Probe, PUNCH, and planned L4/L5 missions.