Forecasting Occurrence and Intensity of Geomagnetic Activity With Pattern‐Matching Approaches

Haines, Carl; Owens, M. J.; Barnard, Luke; Lockwood, Mike; Ruffenach, A.; Boykin, K.; McGranaghan, Ryan

doi:10.1029/2020sw002624

Cited by 10 publications

(7 citation statements)

References 72 publications

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“…Generally, linear interpolations have been employed in the past (e.g., Smith, Forsyth, Rae, Garton, et al., 2021; Wintoft et al., 2015), however in the future more complex interpolation methods may give more confidence to interpolating over larger data gaps, for example, using similar historical analogs (Haines et al., 2021), or the use of auto‐regressive models, given the high level of autocorrelation observed. Nonetheless, future space weather missions should look to minimize NRT data gaps, those due to instrument saturation effects for example, (e.g., Nicolaou et al., 2020).…”

Section: Discussionmentioning

confidence: 99%

“…A "pattern matching" approach could also be employed to identify historical analogous intervals to provide a surrogate input (cf. Haines et al, 2021). Meanwhile, longer time-scale reconstruction of solar wind data has also been performed using ground based indices (Kataoka & Nakano, 2021;Machol et al, 2013) and magnetosheath data (Nabert et al, 2015).…”

Section: Data Continuitymentioning

confidence: 99%

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On the Considerations of Using Near Real Time Data for Space Weather Hazard Forecasting

et al. 2022

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Space weather has the potential to pose a severe threat to modern society. The Earth's magnetosphere is constantly buffeted by the solar wind that emanates from the Sun. It is the variable nature of the solar wind that leads to a highly dynamic environment in near-Earth space. There are many different facets to space weather, including the changing radiation environment in near-Earth space, a hazard that faces Earth orbiting satellites (Baker et al., 1987;Iucci et al., 2005), and strong ground magnetic field variability that can induce damaging currents (Geomagnetically Induced Currents, GICs) in conductive infrastructure (Boteler, 2021;Boteler et al., 1998;Rajput et al., 2020). Forecasting intervals of risk in a timely manner is key; this allows necessary mitigating action to be taken.

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Section: Discussionmentioning

confidence: 99%

Section: Data Continuitymentioning

confidence: 99%

On the Considerations of Using Near Real Time Data for Space Weather Hazard Forecasting

et al. 2022

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“…A future large-scale thermospheric density data base can be used in studies involving Machine Learning (ML) applications. ML studies have become very popular in the field of Earth and Space Sciences in the past decade (e.g., Keesee et al, 2020;Smith et al, 2020;Bortnik and Camporeale, 2021;Haines et al, 2021). For example, historical data sets (geomagnetic and solar indices, sunspot numbers) prior to 2000 can be used for training a model to predict storm drivers and the subsequent global thermospheric density and orbital drag of a LEO satellite in a given location (Licata et al, 2020;2021c).…”

Section: Discussionmentioning

confidence: 99%

The Current State and Future Directions of Modeling Thermosphere Density Enhancements During Extreme Magnetic Storms

Oliveira

Zesta

Mehta

et al. 2021

Front. Astron. Space Sci.

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Satellites, crewed spacecraft and stations in low-Earth orbit (LEO) are very sensitive to atmospheric drag. A satellite’s lifetime and orbital tracking become increasingly inaccurate or uncertain during magnetic storms. Given the planned increase of government and private satellite presence in LEO, the need for accurate density predictions for collision avoidance and lifetime optimization, particularly during extreme events, has become an urgent matter and requires comprehensive international collaboration. Additionally, long-term solar activity models and historical data suggest that solar activity will significantly increase in the following years and decades. In this article, we briefly summarize the main achievements in the research of thermosphere response to extreme magnetic storms occurring particularly after the launching of many satellites with state-of-the-art accelerometers from which high-accuracy density can be determined. We find that the performance of an empirical model with data assimilation is higher than its performance without data assimilation during all extreme storm phases. We discuss how forecasting models can be improved by looking into two directions: first, to the past, by adapting historical extreme storm datasets for density predictions, and second, to the future, by facilitating the assimilation of large-scale thermosphere data sets that will be collected in future events. Therefore, this topic is relevant to the scientific community, government agencies that operate satellites, and the private sector with assets operating in LEO.

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Section: Discussionmentioning

confidence: 99%

The current state and future directions of modeling thermosphere density enhancements during extreme magnetic storms

Oliveira,

Zesta,

Mehta

et al. 2021

Preprint

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Satellites, crewed spacecraft and stations in low-Earth orbit (LEO) are very sensitive to atmospheric drag. A satellite's lifetime and orbital tracking become increasingly inaccurate or uncertain during magnetic storms. Given the planned increase of government and private satellite presence in LEO, the need for accurate density predictions for collision avoidance and lifetime optimization, particularly during extreme events, has become an urgent matter and requires comprehensive international collaboration. Additionally, long-term solar activity models and historical data suggest that solar activity will significantly increase in the following years and decades. In this article, we briefly summarize the main achievements in the research of thermosphere response to extreme magnetic storms occurring particularly after the launching of many satellites with state-of-the-art accelerometers from which high-accuracy density can be determined. We find that the performance of an empirical model with data assimilation is higher than its performance without data assimilation during all extreme storm phases. We discuss how

show abstract

Forecasting Occurrence and Intensity of Geomagnetic Activity With Pattern‐Matching Approaches

Cited by 10 publications

References 72 publications

On the Considerations of Using Near Real Time Data for Space Weather Hazard Forecasting

On the Considerations of Using Near Real Time Data for Space Weather Hazard Forecasting

The Current State and Future Directions of Modeling Thermosphere Density Enhancements During Extreme Magnetic Storms

The current state and future directions of modeling thermosphere density enhancements during extreme magnetic storms

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