Probabilistic prediction of geomagnetic storms and theKpindex

Chakraborty, Shibaji; Morley, S.

doi:10.1051/swsc/2020037

Cited by 31 publications

(33 citation statements)

References 75 publications

(118 reference statements)

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“…Predictions of Kp are usually based on solar wind parameters measured at L1 by satellites like ACE or DSCOVR (e.g. Shprits et al., 2019; Wing et al., 2005; Wintoft et al., 2017; Zhelavskaya et al., 2019) or solar wind parameters plus solar X‐ray flux (Chakraborty & Morley, 2020).…”

Section: Introductionmentioning

confidence: 99%

The Geomagnetic Kp Index and Derived Indices of Geomagnetic Activity

et al. 2021

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Geomagnetic variation consists of quiet variation, which is regular in appearance and mostly of solar electromagnetic radiation origin, and geomagnetic disturbance, which is irregular in appearance and mostly driven by the solar wind. The purpose of the Kp index, or Kp for short, is to monitor subauroral geomagnetic disturbance on a global scale. Bartels (1949) introduced the standardized Ks and the planetary Kp indices (see also Bartels, 1957aBartels, , 1957bSiebert & Meyer, 1996), which are derived from observatory-specific three-hourly K indices (Bartels, 1938(Bartels, , 1939Bartels et al., , 1940. The methodology to determine Ks and Kp indices is based on earlier indices, namely the global Km index and the reduced Kr and global Kw indices (Bartels et al., 1940). The K index, for which is an early and excellent description in English, is defined as a quasi-logarithmic measure, ranging in steps of 1 from 0 to 9, of the range of geomagnetic disturbance at a geomagnetic observatory in a three-hourly UT interval (00-03, 03-06, …, 21-24). Geomagnetic disturbance is also denoted as K-variation. The concept of K-variation, also referred to as geomagnetic activity or disturbance, predates the discovery of the solar wind and historically, K-variation was seen as the effect of 'solar particle radiation' (e.g. Bartels, 1957a). Siebert (1971) and Siebert and Meyer (1996) use this definition: "K-variations are all irregular disturbances of the geomagnetic field caused by solar particle radiation within the 3 h interval concerned. All other regular and irregular disturbances are non-K-variations. Geomagnetic activity is the occurrence of K-variations." We regard geomagnetic disturbance that is instantaneously driven by the solar wind as K-variation.The sum of the K-variation and its counterpart, the non-K-variation, equals the measured geomagnetic field variation at a geomagnetic observatory. K-variation includes geomagnetic pulsations, bays or substorms, sudden commencements, geomagnetic storms (with the exception of the recovery phase, see below) and other geomagnetic disturbance from fast changes in the ring-current and other magnetospheric and ionospheric currents. The non-K-variation includes phenomena related to energetic electromagnetic solar radiation (EUV, X-ray) like the daily solar and lunar quiet variation Yamazaki & Maute, 2017) and the rare solar flare effects (SFE;Curto & Gaya-Piqué, 2009;Veldkamp & van Sabben, 1960). However, some phenomena that are related to the solar wind also contribute to the non-K-variation because of their regular appearance. Examples are the quiet-time magnetospheric fields of the tail current, the magnetopause current and the ring current that appear as diurnal variation of the geomagnetic field at a point rotating with the Earth (e.g., Maus & Lühr, 2005). Another example is the slow decay of the ring current field in the recovery phase of a geomagnetic storm (e.g., Kamide & Maltsev, 2007). While the ring current field is driven by the solar wind, its decay in the recovery phase i...

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Section: Introductionmentioning

confidence: 99%

The Geomagnetic Kp Index and Derived Indices of Geomagnetic Activity

et al. 2021

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“…6 C). Nonetheless, between 1995 and 2014, less than 5% of days were geomagnetically disturbed with Kp > 5 [ 77 ]. This indicates that for 95% of the days our method would produce highly accurate results.…”

Section: Discussionmentioning

confidence: 99%

Fusion of wildlife tracking and satellite geomagnetic data for the study of animal migration

et al. 2021

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Background Migratory animals use information from the Earth’s magnetic field on their journeys. Geomagnetic navigation has been observed across many taxa, but how animals use geomagnetic information to find their way is still relatively unknown. Most migration studies use a static representation of geomagnetic field and do not consider its temporal variation. However, short-term temporal perturbations may affect how animals respond - to understand this phenomenon, we need to obtain fine resolution accurate geomagnetic measurements at the location and time of the animal. Satellite geomagnetic measurements provide a potential to create such accurate measurements, yet have not been used yet for exploration of animal migration. Methods We develop a new tool for data fusion of satellite geomagnetic data (from the European Space Agency’s Swarm constellation) with animal tracking data using a spatio-temporal interpolation approach. We assess accuracy of the fusion through a comparison with calibrated terrestrial measurements from the International Real-time Magnetic Observatory Network (INTERMAGNET). We fit a generalized linear model (GLM) to assess how the absolute error of annotated geomagnetic intensity varies with interpolation parameters and with the local geomagnetic disturbance. Results We find that the average absolute error of intensity is − 21.6 nT (95% CI [− 22.26555, − 20.96664]), which is at the lower range of the intensity that animals can sense. The main predictor of error is the level of geomagnetic disturbance, given by the Kp index (indicating the presence of a geomagnetic storm). Since storm level disturbances are rare, this means that our tool is suitable for studies of animal geomagnetic navigation. Caution should be taken with data obtained during geomagnetically disturbed days due to rapid and localised changes of the field which may not be adequately captured. Conclusions By using our new tool, ecologists will be able to, for the first time, access accurate real-time satellite geomagnetic data at the location and time of each tracked animal, without having to start new tracking studies with specialised magnetic sensors. This opens a new and exciting possibility for large multi-species studies that will search for general migratory responses to geomagnetic cues. The tool therefore has a potential to uncover new knowledge about geomagnetic navigation and help resolve long-standing debates.

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“…Gruet, Chandorkar, Sicard, and Camporeale (2018) assess uncertainty in their forecasts via a Gaussian process model with fixed kernel parameters, and this process takes as input their deterministic NN forecasts. Chakraborty and Morley (2020) on the other hand use a deep long short term memory (LSTM) network to learn how to dynamically update the kernel parameters for a Gaussian process representation of the Kp index, which is how they generate probabilistic forecasts. Finally, while not utilizing neural networks, Gu, Wei, Boynton, Walker, and Balikhin (2019) generate probabilistic forecasts of the auroral electrojet (AE) index by considering output from an ensemble of 100 nonlinear autoregressive models trained on independently resampled subsets of their data.…”

Section: Accepted Articlementioning

confidence: 99%

Probabilistic Geomagnetic Storm Forecasting via Deep Learning

Tasistro-Hart

Grayver

Kuvshinov

2021

Preprint

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By causing time variation in Earth's external magnetic field, geomagnetic storms can induce damaging currents in ground-based conducting infrastructure, such as power and communication lines.&#160; The physical link between solar activity and Earth's magnetosphere, while complicated, provides the basis for attempts to forecast geomagnetic storms. Fortunately, we have abundant observational data of both the solar disk and solar wind, which are ameable to the application of data-hungry neural networks to the forecasting problem. To date, almost all neural networks trained for geomagnetic storm forecasting have utilized solar wind observations from the Earth-Sun first Lagrangian point (L1) or closer and have generated deterministic output without uncertainty estimates. Furthermore, existing models generate forecasts for indices that are also sensitive to induced internal magnetic fields, complicating the forecasting problem with another layer of non-linearity. In this work, we present neural networks trained on observations from both the solar disk and the L1 point. Our architecture generates reliable probabilistic forecasts over Est, the external component of the disturbance storm time index, showing that neural networks can learn measures of confidence in their output.&#160;

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Probabilistic prediction of geomagnetic storms and theK_pindex

Cited by 31 publications

References 75 publications

The Geomagnetic Kp Index and Derived Indices of Geomagnetic Activity

The Geomagnetic Kp Index and Derived Indices of Geomagnetic Activity

Fusion of wildlife tracking and satellite geomagnetic data for the study of animal migration

Probabilistic Geomagnetic Storm Forecasting via Deep Learning

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