Anwesha Maharana

Interacting coronal mass ejections (CMEs) result in complex heliospheric structures that can dramatically enhance their geoeffectiveness compared with isolated events. A striking example of such complex structures is that of the Mother’s Day event, which occurred during 2024 May 10–14, leading to the strongest geomagnetic storm in decades. It was driven by at least 10 interacting CMEs accompanied by flare and filament eruptions from the solar western hemisphere. We aim to understand its solar and heliospheric origins, variations in plasma and magnetic characteristics, and predict its geoeffectiveness. Furthermore, we focus on bringing to the community the challenges and limitations faced during the modelling of such a complex, yet important, environment. We use multipoint remote observations of CMEs and employ different methods to estimate their speeds and geometry. These parameters drive the 3D magnetohydrodynamic simulations of CME evolution and heliospheric propagation in the framework of the EUHFORIA model. The best-performing simulation reproduced the storm’s arrival time and peak intensity with a lead of ∼2 hr, and estimated the storm strength with about 70% accuracy. This work underscores the requirements and challenges involved in accurately modelling extreme events. The major bottleneck was to constrain the CME input parameters that dictate the accuracy of the data-driven simulations. Predicting the impacts of the complex heliospheric structures during this event required the modeling of the CME–CME interactions accurately. As a community, we need to invest more in observational infrastructure while improving the speed and accuracy of our MHD forecasting models.

Anthropogenic climate change poses escalating threats to human health, with India ranked 6th in the global climate risk index. As climate‑sensitive illnesses, extreme heat, air pollution, and climate‑induced displacement intensify, evidence‑driven adaptation becomes essential. This study critically reviews India’s National and State Action Plans for Climate Change and Human Health (NAPCCHH and 32 SAPCCHH as of November 2023) to identify strengths, gaps, and opportunities to improve health-centred climate action. We developed a protocol based on a refined version of the WHO Health National Adaptation Plan quality criteria framework and conducted a quantitative assessment of the plans. While the plans reflect strong political commitments and initial cross-sectoral structures, significant gaps persist in vulnerability assessments, surveillance capacity, resource mobilisation, and monitoring frameworks. Findings highlight the need for iterative, participatory, and data-driven processes that integrate evolving climate-health evidence and better engage civil society and vulnerable communities to advance effective localised adaptation in India.

Between 10 and 14 May 2024, the Sun produced an extraordinary sequence of eruptions, the Mother’s Day event, culminating in the most intense geomagnetic storm in decades. This event was driven by at least ten interacting coronal mass ejections (CMEs), accompanied by solar flares and filament eruptions, forming highly complex heliospheric structures with exceptional geoeffectiveness. Such CME–CME interactions present significant challenges for operational space-weather forecasting. This study addresses the complexity of modelling extreme events within operational space weather frameworks. A key difficulty lies in constraining CME and solar wind input parameters, especially for halo and partial-halo CMEs, where parameter sensitivity is heightened. Accurate representation of CME–CME interactions necessitates physics-based magnetohydrodynamic (MHD) modelling rather than empirical approaches. We employ multi-point remote observations in white light and extreme ultraviolet to identify CME sources and derive their kinematic and geometric properties. These parameters drive three-dimensional MHD simulations of CME evolution and heliospheric propagation using the EUHFORIA model. Our best-performing simulation reproduced the storm’s arrival time within approximately two hours and estimated its peak intensity with ~70% accuracy. Crucially, the inclusion of all contributing CMEs was essential for achieving this level of predictive reliability. Our findings underscore the need for improved observational infrastructure and enhanced modelling capabilities to address the inherent complexity of extreme space weather events. Advancing the speed and accuracy of MHD-based forecasting tools is critical for mitigating the impacts of future solar superstorms. We highlight how the Mother’s Day 2024 event serves as a benchmark for understanding the limitations of current models and a call for the urgent requirement for community-wide investment in both observational and computational resources.

Aims. We propose a new method for constraining the magnetic flux of the spheromak coronal mass ejection (CME) in the European Heliospheric Forecasting Information Asset (EUHFORIA) by utilising the CME’s helicity content and comparing its efficiency with conventional methods. As a proof of concept, we analysed a CME observed on 10 March 2022 from the NOAA active region (AR) 12962. The simulation results were compared with in situ magnetic field and plasma measurements from Solar Orbiter (SolO) at 0.43 AU and WIND at 0.99 AU to assess the method’s reliability and parameter dependencies. Methods. The CME magnetic helicity was related to the spheromak axial field strength ( B _spheromak ). From this result, the toroidal magnetic flux was derived using the CME geometry. Additionally, we assessed the sensitivity to CME density, insertion propagation direction, and toroidal flux through multiple simulations. Results. Our new method effectively reproduces magnetic field strength in situ measurements at 0.43 AU and 0.99 AU. The magnetic and plasma in situ data from SolO, located approximately along the mid-Sun-Earth line, were crucial for refining input parameters and improving the predictive accuracy at L1. Compared with the flux-constraining post-eruption arcade (PEA) method for this case study, at 0.99 AU, the PEA method underestimates the peak magnetic field by 13%, while the helicity-based method underestimates it by 19% Conclusions. Our helicity-based flux estimation method shows promise for constraining CME input parameters in spheromak CME in EUHFORIA and closely matches in situ magnetic field measurements. Incorporating diverse CME events observed by multiple spacecraft will enable more robust assessments of the method’s reliability, efficiency, and compatibility relative to other approaches, and will help clarify its suitability for use with different CME models.

To study the evolution and propagation of Coronal Mass Ejections (CMEs), the advanced magnetized flux rope CME model, Flux Rope in 3D (FRi3D), has been integrated into the European Heliospheric Forecasting Information Asset (EUHFORIA). Recent updates have been addressing the computational efficiency and numerical stability issues of this CME model. In this work, the latest improvements to the FRi3D model are represented, including optimizations of the integral calculations in the ai.fri3d Python package, an updated CME leg disconnection method, and the introduction of a new datacube injection methodology for time‐efficient FRi3D simulations. Within the datacube injection method, the input values of FRi3D at the inner boundary of EUHFORIA are precomputed, eliminating the need to calculate the FRi3D CME model during the solar wind simulation with superimposed CMEs. The datacube injection method is tested by modeling the CME event of 12 July 2012, and by conducting ensemble runs for this event. The updates to the FRi3D Python package reduced the computation time to , and the datacube methodology achieved a computational time reduction of up to compared to previous FRi3D versions. Numerical stability was significantly improved, and ensemble simulations demonstrated robust ensemble spreads for the thermodynamic and magnetic time profiles on Earth. These updates enhance the efficiency, stability, and accuracy of the FRi3D model, making it highly suitable for predicting both the CME arrival time, magnetic field strength, and velocity. As a result, the advancements contribute significantly to the future operational space weather forecasting capabilities of the FRi3D CME model.

Many questions must be answered before understanding the rela- tionship between the emerging magnetic flux through the solar surface and the extreme geoeffective events. Which threshold determines the onset of the eruption? What is the upper limit in energy for a flare? Is the size of the sunspot the only criterion to get extreme solar events? Based on observations of previous solar cycles and theory, the main ingredients for getting X-ray classflares and large interplanetary Coronal Mass Ejections, e.g., the built-up of theelectric current in the corona, are presented, as are the existence of magnetic freeenergy, magnetic energy/helicity ratio, twist, and stress in active regions. Theupper limit of solar energy in the space research era, and the possible chancesof getting super-flares and extreme solar events, can be predicted using MHDsimulations of coronal mass ejections. For eruptive flares with CMEs or confinedflares, magnetic solar energy is discussed in this context, along with data-drivenMHD simulations of the May 2024 events. We show the consistency between thedata-initiated realistic simulation of the May 2024 big event and energy scalingsfrom an idealised simulation of a bipolar eruption. The estimated free magneticenergy did not surpass 5.2 * 1032 erg. Good arrival time predictions (< 3 hours)are achieved with the EUHFORIA simulation with the cone model. We notethe interest in coupling all the chains of codes from the Sun to the Earth anddeveloping different approaches to test the results.

Predicting the geo-effectiveness of CMEs relies on accurate modeling of their propagation and interaction with the solar wind. EUHFORIA (EUropean Heliospheric FORecasting Information Asset) is a state-of-the-art 3D magnetohydrodynamic (MHD) model designed to model the evolution of CMEs in the heliosphere. I will present the several advanced CME models implemented in EUHFORIA, including the spheromak model, Fri3D (Flux-Rope in 3D), spheromak, and two toroidal CME models (Soloviev and Miller-Turner based models). Additionally, a more recent deep learning-based model, PINN, has been developed and implemented in EUHFORIA to enable access to toroidal magnetic field distributions that are otherwise not analytically accessible or computationally expensive to obtain. I will also present the latest advancement in EUHFORIA: its coupling with the global MHD coronal model COCONUT (COolfluid COroNal UnsTructured). While EUHFORIA injects CME models at 0.1 AU, this approach omits critical interactions occurring near the Sun, where the CME engages with the structured solar wind. COCONUT addresses this limitation by simulating the solar corona, starting from the solar surface and extending to 0.1 AU, using observed magnetograms to produce a realistic solar wind environment. This coupling enables us to track the propagation of a CME from its launch at the Sun’s surface through the corona and into the heliosphere. By aligning the outer boundary of COCONUT with the inner boundary of EUHFORIA, we ensure a seamless transfer of CME properties, including its magnetic field structure and plasma characteristics.I will present the first results of this coupling, showcasing how different flux-rope CME models (e.g., Titov-Démoulin and RBSL) propagate dynamically through the coupled domain. This innovative integration marks a significant step forward in our ability to predict CME impacts and understand the physics driving space weather events.

The EUropean Heliospheric FORecasting Information Asset (EUHFORIA, Pomoell and Poedts, 2018), a physics-based and data-driven heliospheric and CME propagation model can predict the solar wind plasma and magnetic field conditions at Earth. It contains several flux-rope CME models, such as the simple spheromak models and the more advanced FRi3D and toroidal CME models. This enables the prediction of the sign and strength of the magnetic field components upon the arrival of the CME at Earth and, thus, the geo-effectiveness of the CME impact. EUHFORIA has been coupled to several global magnetosphere models like OpenGGCM, GUMICS-4, and Gorgon-Space. In addition, the synthetic data at L1 (from the EUHFORIA simulation) can be used as input for empirical models and neural networks to predict the geomagnetic indices like Disturbance-storm-time (Dst) or Kp that quantify the impact of the magnetized plasma encounters on Earth’s magnetosphere. Hence, we also coupled EUHFORIA to empirical models (Obrien and McPherron, 2000b, and Newell et al, 2006) and machine learning (NARMAX, and the models from Wintoft et al. (2017 and 2021)) based models to predict the geomagnetic indices. We then compare the results of these models to observational data to evaluate their performance in predicting the geo-effect indices. To quantify these comparisons, we use the advanced dynamic time warping method. Since we use synthetic data from the EUHFORIA simulations, we can obtain the input parameters for running the geomagnetic indices models two to three days in advance, unlike the 60-90 minutes lead time of the real-time measurements. We perform ensemble modelling considering the L1 monitor precision in its orbit as well as the uncertainty in the initial CME parameters (longitude and latitude) at launch, for error quantification. This is done by evaluating the geomagnetic index models using synthetic data from the virtual satellites around L1 in EUHFORIA’s simulation domain. In addition, we also investigate the impact of the spatio-temporal resolution of EUHFORIA output in forecasting the geomagnetic indices, exploiting the adaptive mesh refinement feature in ICARUS (Baratashvili et al., 2022). Overall, this study validates various space weather forecasting model chains and checks the best compatibility and predictive capabilities using EUHFORIA data for operational space weather forecasting.

Coronal mass ejections (CMEs) undergo erosion, deflection, and deformation upon interaction with the solar wind structures and with other transients during their propagation in the solar corona and heliosphere. In this work, we focus on the process of the erosion of CMEs in the heliosphere, which impacts their magnetic flux content, and its effect on altering their geo-effectiveness. To quantify the erosion of CME magnetic flux and mass in various solar wind environments, we employ 3D magnetohydrodynamic (MHD) simulations. To create a simulated solar wind background resembling a solar minimum period (for simplistic cases), we utilize the EUropean Heliosphere FORecasting Information Asset (EUHFORIA, Pomoell and Poedts, 2018) model. Through this background, we evolved a CME from 0.1 au to 2 au using a linear force-free spheromak model. We employ two distinct methods to assess erosion of magnetic flux and mass. First, the in-situ method, where the single point data at Earth or any other location is used to quantify the magnetic flux erosion. The orientation of the CME in the magnetic cloud frame of reference is determined through minimum variance analysis, and the accumulated axial and azimuthal flux is computed (Dasso et al, 2006). Imbalances in the flux profile serve as indicators of erosion. The second method relies on 3D simulation data, tracking the mass of the magnetic cloud in three dimensions based on criteria developed by Asvestari et al, 2022 for the spheromak model. With this method we can identify the 3D volume of the spheromak, assess its orientation, rotation, and magnetic properties in 3D in the local frame of reference of the spheromak structure. This technique provides us with the magnetic flux content of the spheromak and how it changes in space and time. Following a benchmarking process between the two erosion quantification methods, we conduct simulations to explore the sensitivity of CME erosion to variations in geometrical and magnetic field parameters. The study also investigates the influence of interactions with high-speed streams on erosion. Lastly, we apply an empirical Dst model (O’Brien and McPherron, 2000) to quantify geo-effectiveness and establish correlations with estimated erosion in all cases.