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1. Heliophysics and space weather science at ∼1.5 AU: Knowledge gaps and need for space weather monitors at Mars

   https://doi.org/10.3389/fspas.2023.1064208  

   Lee, Christina O. ; Sánchez-Cano, Beatriz ; DiBraccio, Gina A. ; Mayyasi, Majd ; Xu, Shaosui ; Chamberlin, Phillip ; Davies, Emma ; Scolini, Camilla ; Filwett, Rachael J. ; Ramstad, Robin ; et al ( February 2023 , Frontiers in Astronomy and Space Sciences)

   This perspective article discusses the knowledge gaps and open questions regarding the solar and interplanetary drivers of space weather conditions experienced at Mars during active and quiescent solar periods, and the need for continuous, routine observations to address them. For both advancing science and as part of the strategic planning for human exploration at Mars by the late 2030s, now is the time to consider a network of upstream space weather monitors at Mars. Our main recommendations for the heliophysics community are the following: 1. Support the advancement for understanding heliophysics and space weather science at ∼1.5 AU and continue the support of planetary science payloads and missions that provide such measurements. 2. Prioritize an upstream Mars L1 monitor and/or areostationary orbiters for providing dedicated, continuous observations of solar activity and interplanetary conditions at ∼1.5 AU. 3. Establish new or support existing 1) joint efforts between federal agencies and their divisions and 2) international collaborations to carry out #1 and #2. 

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2. Challenges in Specifying and Predicting Space Weather

   https://doi.org/10.1029/2019SW002404  

   Schunk, R. W. ; Scherliess, L. ; Eccles, V. ; Gardner, L. C. ; Sojka, J. J. ; Zhu, L. ; Pi, X. ; Mannucci, A. J. ; Komjathy, A. ; Wang, C. ; et al ( February 2021 , Space Weather)

   Physics‐based Data Assimilation (DA) has been shown to be a powerful technique for specifying and predicting space weather. However, it is also known that different data assimilation models simulating the same geophysical event can display different space weather features even if the same data are assimilated. In this study, we used our Multimodel Ensemble Prediction System (MEPS) of DA models to elucidate the similarities and differences in the individual DA model reconstructions of the mid‐low latitude ionosphere when the same data are assimilated. Ensemble model averages were also obtained. For this ensemble modeling study, we selected the quiet/storm period of 16 and 17 March 2013 (equinox, solar medium). Five data assimilation models and one physics‐based model were used to produce an ensemble mean output for Total Electron Content (TEC), ionospheric peak density (N_mF₂), and ionospheric peak height (h_mF₂) for latitudes less than 60° and all longitudes. The data assimilated included ground‐based Global Positioning Satellite TEC and topside plasma densities near 800 km altitude derived from the COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate) satellites. Both a simple average and a weighted average of the models were used in the ensemble averaging in order to determine if there was an improvement of the ensemble averages over the individual models.

    

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3. Space weather disrupts nocturnal bird migration

   https://doi.org/10.1073/pnas.2306317120  

   Gulson-Castillo, Eric R. ; Van Doren, Benjamin M. ; Bui, Michelle X. ; Horton, Kyle G. ; Li, Jing ; Moldwin, Mark B. ; Shedden, Kerby ; Welling, Daniel T. ; Winger, Benjamin M. ( October 2023 , Proceedings of the National Academy of Sciences)

   Space weather, including solar storms, can impact Earth by disturbing the geomagnetic field. Despite the known dependence of birds and other animals on geomagnetic cues for successful seasonal migrations, the potential effects of space weather on organisms that use Earth’s magnetic field for navigation have received little study. We tested whether space weather geomagnetic disturbances are associated with disruptions to bird migration at a macroecological scale. We leveraged long-term radar data to characterize the nightly migration dynamics of the nocturnally migrating North American avifauna over 22 y. We then used concurrent magnetometer data to develop a local magnetic disturbance index associated with each radar station (ΔBmax), facilitating spatiotemporally explicit analyses of the relationship between migration and geomagnetic disturbance. After controlling for effects of atmospheric weather and spatiotemporal patterns, we found a 9 to 17% decrease in migration intensity in both spring and fall during severe space weather events. During fall migration, we also found evidence for decreases in effort flying against the wind, which may represent a depression of active navigation such that birds drift more with the wind during geomagnetic disturbances. Effort flying against the wind in the fall was most reduced under both overcast conditions and high geomagnetic disturbance, suggesting that a combination of obscured celestial cues and magnetic disturbance may disrupt navigation. Collectively, our results provide evidence for community-wide avifaunal responses to geomagnetic disturbances driven by space weather during nocturnal migration.

    

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4. A Model of an Integrated Research and Education Program in Space Weather at a Community College

   https://doi.org/10.1029/2019SW002307  

   Damas, M. C. ; Ngwira, C. M. ; Cheung, T. D. ; Marchese, P. ; Kuznetsova, M. ; Zheng, Y. ; Chulaki, A. ; Mohamed, A. ( January 2020 , Space Weather)
5. Space Weather with Quantified Uncertainties: Improving Space Weather Predictions with Data-Driven Models of the Solar Atmosphere and Inner Heliosphere

   https://doi.org/10.1088/1742-6596/2742/1/012013  

   Pogorelov, Nikolai V ; Arge, Charles N ; Caplan, Ronald M ; Colella, Phillip ; Linker, Jon A ; Singh, Talwinder ; Van_Straalen, Brian ; Upton, Lisa ; Downs, Cooper ; Gebhart, Christopher ; et al ( April 2024 , Journal of Physics: Conference Series)

   To address Objective II of the National Space Weather Strategy and Action Plan “Develop and Disseminate Accurate and Timely Space Weather Characterization and Forecasts” and US Congress PROSWIFT Act 116–181, our team is developing a new set of open-source software that would ensure substantial improvements of Space Weather (SWx) predictions. On the one hand, our focus is on the development of data-driven solar wind models. On the other hand, each individual component of our software is designed to have accuracy higher than any existing SWx prediction tools with a dramatically improved performance. This is done by the application of new computational technologies and enhanced data sources. The development of such software paves way for improved SWx predictions accompanied with an appropriate uncertainty quantification. This makes it possible to forecast hazardous SWx effects on the space-borne and ground-based technological systems, and on human health. Our models include (1) a new, open-source solar magnetic flux model (OFT), which evolves information to the back side of the Sun and its poles, and updates the model flux with new observations using data assimilation methods; (2) a new potential field solver (POT3D) associated with the Wang–Sheeley–Arge coronal model, and (3) a new adaptive, 4-th order of accuracy solver (HelioCubed) for the Reynolds-averaged MHD equations implemented on mapped multiblock grids (cubed spheres). We describe the software and results obtained with it, including the application of machine learning to modeling coronal mass ejections, which makes it possible to improve SWx predictions by decreasing the time-of-arrival mismatch. The tests show that our software is formally more accurate and performs much faster than its predecessors used for SWx predictions.

    

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