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

Zaina Abu-Shaar, Tatiana Podladchikova, Astrid M. Veronig, Mateja Dumbovic, Stefan J. Hofmeister

TL;DR

This work addresses CME arrival forecasting under uncertain drag forces by proposing HELIOPANDA, a framework that fuses the Drag-Based Model with direct parameter estimation and Kalman-filter data assimilation. A direct solver recovers the background solar wind speed $w$ and drag parameter $\gamma$ from distance and speed observations, validated on $4{,}480$ synthetic CME profiles, achieving negligible $w$-and-$\gamma$ errors in most cases. The authors show the value of distributed in-situ sensing with nine virtual probes, obtaining Earth arrival forecasts as accurate as roughly $0.6$–$1$ hour for a wide range of CME speeds. Extending to noisy remote-sensing data via a Kalman-filter assimilation of $160$ measurements yields Earth/Mars arrival predictions within $1$–$2$ hours, demonstrating a practical path toward real-time, multi-point CME forecasting with current and future heliophysics missions. The framework thus bridges empirical and MHD approaches, enabling robust, multi-point space-weather alerts.

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 $γ$, 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 $0.1-1.0\times10^{-7}$ 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 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 $γ$, 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.

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

TL;DR

This work addresses CME arrival forecasting under uncertain drag forces by proposing HELIOPANDA, a framework that fuses the Drag-Based Model with direct parameter estimation and Kalman-filter data assimilation. A direct solver recovers the background solar wind speed and drag parameter from distance and speed observations, validated on synthetic CME profiles, achieving negligible -and- errors in most cases. The authors show the value of distributed in-situ sensing with nine virtual probes, obtaining Earth arrival forecasts as accurate as roughly – hour for a wide range of CME speeds. Extending to noisy remote-sensing data via a Kalman-filter assimilation of measurements yields Earth/Mars arrival predictions within – hours, demonstrating a practical path toward real-time, multi-point CME forecasting with current and future heliophysics missions. The framework thus bridges empirical and MHD approaches, enabling robust, multi-point space-weather alerts.

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 and drag parameter , 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 km/s, solar wind speeds of km/s, and drag parameters of km. 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 and , and yielding Earth and Mars arrival-time predictions within 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.

Paper Structure

This paper contains 6 sections, 27 equations, 9 figures.

Table of Contents

  1. Introduction
  2. Direct estimation of solar wind speed and drag parameter from the Drag-Based Model
  3. Coronal mass ejection arrival forecasting through virtual in-situ probes along the Sun--Earth line
  4. Coronal mass ejection arrival forecasting through remote sensing heliospheric measurements
  5. Conclusions
  6. The Kalman filter algorithm

Figures (9)

  • Figure 1: Block diagram illustrating the iterative procedure used to estimate the solar wind speed $w$ and the drag parameter $\gamma$ from the DBM equations.
  • Figure 2: Convergence performance of the iterative estimation method for six representative CME parameter cases. The Y-axis shows the number of successful convergences out of 1,444 tested initial guess pairs at each time step (X-axis). Importantly, across all tested combinations of $v_{0}$, $w$, and $\gamma$, there are always initial guesses that achieve convergence at every time step.
  • Figure 3: Maximum estimation errors of solar wind speed $w$ (left) and drag parameter $\gamma$ (right) over time for six representative CME cases. The y-axes are shown in logarithmic scale.
  • Figure 4: Schematic representation of in-situ probes positioned along the Sun--Earth line to observe Earth-directed CMEs.
  • Figure 5: Estimation results for two representative CME scenarios observed by spacecraft positioned along the Sun–Earth line. Left panels (a), (c), (e), and (g) correspond to a CME with initial speed 600 km/s, solar wind speed 400 km/s, and drag parameter $\gamma = 0.2 \times 10^{-7}$ km$^{-1}$. Right panels (b), (d), (f), and (h) correspond to a CME with initial speed 2500 km/s, solar wind speed 300 km/s, and drag parameter $\gamma = 0.1 \times 10^{-7}$ km$^{-1}$. Panels (a) and (b) show the CME speed as a function of heliocentric distance and CME speed measurements. Panels (c) and (d) show the estimated solar wind speed $w$ at each spacecraft location. Panels (e) and (f) show the estimated drag parameter $\gamma$. Panels (g) and (h) show the arrival-time estimation error (difference between predicted and actual arrival times in minutes).
  • ...and 4 more figures