Joshita Majumdar, D. Rangaprakash, Gopikrishna Deshpande
Summary: The empirical evidence for the effects of changing seasons on humans and other species has long been a subject of public and scientific curiosity. However, supporting evidence is predominantly behavioral, warranting further quantitative/mechanistic validation. Three putative mechanisms proposed here include (1) variability in photoperiod and temperature, (2) gravitational effects from varying Earth-celestial body distances, including lunar phases and eclipses, and (3) changes in geomagnetism. This systematic review (PubMed, Web of Science, and Scopus; until September’24) aims to identify studies on the effects of seasonality, gravity, and geomagnetism on brain function, establishing a baseline for the proposed hypotheses. Distinct search queries were tailored to capture the relevant literature. Behavioral and observational data support the hypotheses, with limited EEG/MRI evidence indicating potential neural correlates, although most research is cross-sectional and preliminary. Future work should employ well-powered, longitudinal, and hypothesis-driven designs to clarify the observed effects, thereby confirming or challenging competing views.
Frank Cenci Bulhões, Luiz Alberto Santos, Marco Cetale
et al.
A common approach to stabilize the ill-posed inverse problem is to apply regularization, which restricts the possible solutions to these problems. Thus, a regularization term is often incorporated into the tomographic objective function to resolve the non-uniqueness of the inverse geophysical problem, restricting the possible solutions to these problems. This work evaluates the effects of regularization and analyzes its impact on the resulting seismic velocities. Grounded in a detailed case study, we investigate Tikhonov's regularization of order 1 and its variants, including order 2, utilizing a tomography program that employs ray tracing, a finite differences scheme with the eikonal equation for first arrivals, and the regularization algorithm. The velocity model is synthetic and based on shallow seabed channel geology. The true model was compared with the tomography results without regularization and with regularization schemes. The results clearly indicate that regularization parameters play a critical role in defining the outcomes of velocity models in tomography inversion. By applying regularization, we significantly reduce structural distortion in tomographic results — this approach proves to be not only effective but essential. Tikhonov’s regularization of order 2 consistently demonstrates faster convergence and notable improvements in the velocity model. Furthermore, our parameter sensitivity tests reveal the extent to which an inappropriate choice can distort geological structures, such as by creating artificial structural highs or lows, underscoring the necessity of careful selection in regularization techniques.
R. Krishnan, Chirag Dhara, Takeshi Horinouchi
et al.
Anthropogenic climate change has led to rapid and widespread changes in the atmosphere, land, ocean, cryosphere, and biosphere, leading to more pronounced weather and climate extremes globally. Recent IPCC reports have highlighted that the probability of compound extreme events, which can amplify risk, has risen in multiple regions. However, significant gaps remain in our understanding of the drivers and mechanisms behind these events. This concept paper discusses compound events in the Asian region in the context of its unique and diverse geographical settings, and regional climatic features including the seasonal monsoons. Notably, Asia is the world’s most disaster-affected region due to weather, climate, and water-related hazards. Therefore, an integrated understanding of how climate change will impact compound events in this region is essential for effective forewarning and risk mitigation. This paper analyzes three typologies of compound events in the Asian region, illustrating their regional complexity and potential linkages to climate change. The first typology pertains to compound floods, for example, the devastating floods in the Indus River Basin and adjoining Western Himalayas during 2022 caused by the combined effects of heavy monsoon rainfall, intense pre-monsoon heatwaves, glacier melt, and modes of climate variability. The second typology relates to compound heatwave-drought events that have prominently manifested in East and South Asia, and are linked to large-scale drivers of the land-atmosphere–ocean coupled system and local feedbacks. The third typology relates to marine extremes involving the compounding effects of ocean warming, sea-level rise, marine heatwaves, and intensifying tropical cyclones. We identify key knowledge gaps in understanding and predicting compound events over the Asian region and discuss advances required in science and technology to address these gaps. We also provide recommendations for the effective utilization of climate information towards improving early warning systems and disaster risk reduction.
Trunali Shah, Veenadhari Bhaskara, Yoshiharu Omura
et al.
Abstract Using observations from the Van Allen Probes spacecraft, this study reports multifrequency banded magnetic field fluctuations during substorm-associated magnetic field dipolarization at L < 6.6. A total of 11 events at MLT 18:00–06:00 h, which corresponds to the night side are analyzed, with a detailed examination of a single event followed by a discussion of other events. Dynamic power spectra characterize the structures within these fluctuations, while ion flux variations provide insights into particle dynamics. Plasma and magnetic pressures are also computed to assess plasma sheet dynamics. The results reveal that multifrequency magnetic field fluctuations coincide with substorm-associated dipolarization, with wave power extending up to the local proton gyrofrequency ( $$\Omega _{{\hbox {H}^{+}}}$$ Ω H + ). In six substorm events, a significant wave power enhancement beyond the local helium ion gyrofrequency ( $$\Omega _{\text{He}^{+}}$$ Ω He + ) is observed, particularly in the magnetic field aligned (MFA) $$\text{B}_{\text{x}}$$ B x and $$\text{B}_{\text{y}}$$ B y components, suggesting quasi-parallel wave activity in the inner magnetosphere. In addition, the inverse relationship between plasma and magnetic pressures leads to a force imbalance, contributing to the acceleration of $$\text{H}^{+}$$ H + ions. The observed increase in plasma pressure further indicate that the dipolarization region expands during substorms. These findings indicates that high-frequency quasi-parallel waves play a role in magnetic field fluctuations in the inner magnetosphere. Graphical Abstract
Abstract From a survey of published accounts of visual sightings of aurorae, a compilation is presented of the lowest identified geomagnetic latitude at which discrete aurorae were seen at local zenith during magnetic storms having intensities with maximum −Dst>200 nT. The compilation includes data for the superstorms of 2 September 1859, 4 February 1872, and 15 May 1921. A statistical model is developed representing the equatorward boundary of discrete aurorae versus storm intensity. The model indicates that a once‐per‐century storm would likely induce discrete aurorae at zenith down to a geomagnetic latitude of 34°. Insofar as aurorae can be taken as a proxy for electrojet currents, such a storm would expose many nighttime electric‐power systems in the contiguous United States or Europe to high levels of geomagnetic disturbance. A Carrington‐class storm would induce discrete aurorae down to 24°. These exposures are much greater than those indicated in recent numerical simulations of extreme magnetic storms. Using the model to infer storm intensity from reports of low‐latitude aurorae, a storm on 28 August 1859 likely had maximum −Dst=673 nT. That this storm occurred just a few days before the Carrington storm of 2 September (maximum −Dst=964 nT) deserves attention. A storm that occurred on 17 September 1770 is estimated to have had maximum −Dst=928 nT. A vision described by Ezekiel could have been inspired by aurorae from a storm with maximum −Dst=550 nT.
The advancement in autonomous vehicles has empowered navigation and exploration in unknown environments. Geomagnetic navigation for autonomous vehicles has drawn increasing attention with its independence from GPS or inertial navigation devices. While geomagnetic navigation approaches have been extensively investigated, the generalizability of learned geomagnetic navigation strategies remains unexplored. The performance of a learned strategy can degrade outside of its source domain where the strategy is learned, due to a lack of knowledge about the geomagnetic characteristics in newly entered areas. This paper explores the generalization of learned geomagnetic navigation strategies via deep reinforcement learning (DRL). Particularly, we employ DRL agents to learn multiple teacher models from distributed domains that represent dispersed navigation strategies, and amalgamate the teacher models for generalizability across navigation areas. We design a reward shaping mechanism in training teacher models where we integrate both potential-based and intrinsic-motivated rewards. The designed reward shaping can enhance the exploration efficiency of the DRL agent and improve the representation of the teacher models. Upon the gained teacher models, we employ multi-teacher policy distillation to merge the policies learned by individual teachers, leading to a navigation strategy with generalizability across navigation domains. We conduct numerical simulations, and the results demonstrate an effective transfer of the learned DRL model from a source domain to new navigation areas. Compared to existing evolutionary-based geomagnetic navigation methods, our approach provides superior performance in terms of navigation length, duration, heading deviation, and success rate in cross-domain navigation.
The work investigates the features of galactic cosmic ray density and anisotropy behavior and their relation to solar sources, interplanetary and geomagnetic disturbances from May 8 to May 13, 2024. During this time, powerful solar flares and fast CMEs were recorded, leading to registration of an extreme geomagnetic storm along with one of the most significant Forbush effects for the entire observation period. All the calculations of cosmic ray characteristics are made using the data of global neutron monitor network and unique methods maintained at IZMIRAN: the Global Survey Method and the Ring of Stations Method. It is determined that the magnitude of Forbush effect under study was 15.7% (for particles with 10 GV rigidity) and as an extreme geomagnetic storm was recorded there was a significant magnetospheric effect observed in the data of neutron monitors (~4%).
Rositsa Miteva, Susan W. Samwel, Svetoslav Zabunov
Solar energetic protons (SEPs) in different energy channels from 10 to above 100 MeV are analyzed and their relationship to solar and geomagnetic activity is investigated. We performed temporal association analysis between the SEPs, solar flares (SFs), coronal mass ejections (CMEs) and geomagnetic storms (GSs) that occurred during solar cycles 23 and 24. The energy dependencies between the SEPs and the strength of the space weather activity are evaluated and presented.
N. Parihar, S. Padincharapad, S. Padincharapad
et al.
<p>We report F-region airglow imaging of fossil plasma depletions around midnight that revived afresh under persisting thermospheric gravity wave (GW) activity. An all-sky imager recorded these events in OI 630 nm imaging over Ranchi (23.3° N, 85.3° E; mlat. <span class="inline-formula">∼19</span>° N), India, on 16 April 2012. Northward-propagating and east–west-aligned GWs (<span class="inline-formula"><i>λ</i>∼210</span> km, <span class="inline-formula"><i>v</i>∼64</span> m s<span class="inline-formula"><sup>−1</sup></span>, and <span class="inline-formula"><i>τ</i>∼0.91</span> h) were seen around midnight. Persisting for <span class="inline-formula">∼2</span> h, this GW activity revived two co-existing and eastward-drifting fossil depletions, DP1 and DP2. GW-driven revival was prominently seen in depletion DP1, wherein its apex height grew from <span class="inline-formula">∼600</span> to <span class="inline-formula">>800</span> km, and the level of intensity depletion increased from <span class="inline-formula">∼17 <i>%</i></span> to 50 %. The present study is novel in the sense that simultaneous observations of thermospheric GW activity and the associated evolution of depletion in OI 630 nm airglow imaging, as well as that around local midnight, have not been reported earlier. The current understanding is that GW phase fronts aligned parallel to the geomagnetic field lines and eastward-propagating are more effective in seeding Rayleigh–Taylor (RT) instability. Here, GW fronts were east–west-aligned (i.e., perpendicular to the geomagnetic field lines) and propagated northward, yet they revived fossil depletions.</p>
Sukanta Sau, Pedrina Terra, Christiano G. M. Brum
et al.
Abstract Rotational temperatures in the Mesosphere‐Lower Thermosphere region are estimated by utilizing the OH(6,2) Meinel band nightglow data obtained with an Ebert‐Fastie spectrometer (EFS) operated at Arecibo Observatory (AO), Puerto Rico (18.35°N, 66.75°W) during February‐April 2005. To validate the estimated rotational temperatures, a comparison with temperatures obtained from a co‐located Potassium Temperature Lidar (K‐Lidar) and overhead passes of the Sounding of the Atmosphere by Broadband Emission Radiometry (SABER) instrument onboard NASA's Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) satellite is performed. Two types of weighting functions are applied to the K‐Lidar temperature profiles to compare them with EFS temperatures. The first type has a fixed peak altitude and a fixed full width at half maximum (FWHM) for the whole night. In the second type, the peak altitude and FWHM vary with the local time. SABER measurements are utilized to estimate the OH(6,2) band peak altitudes and FWHMs as a function of local time and considerable temporal variations are observed in both the parameters. The average temperature differences between the EFS and K‐Lidar obtained with both types of weighting functions are comparable with previously published results from different latitude‐longitude sectors. We found that the temperature comparison improves when the time‐varying weighting functions are considered. Comparison between EFS, K‐Lidar, and SABER temperatures reveal that on average, SABER temperatures are lower than the other two instruments and K‐Lidar temperatures compare better with SABER in comparison to EFS. Such a detailed study using the AO EFS data has not been carried out previously.
Geomagnetic storms (GS) occur when solar winds disrupt Earth's magnetosphere. GS can cause severe damages to satellites, power grids, and communication infrastructures. Estimate of direct economic impacts of a large scale GS exceeds $40 billion a day in the US. Early prediction is critical in preventing and minimizing the hazards. However, current methods either predict several hours ahead but fail to identify all types of GS, or make predictions within short time, e.g., one hour ahead of the occurrence. This work aims to predict all types of geomagnetic storms reliably and as early as possible using big data and machine learning algorithms. By fusing big data collected from multiple ground stations in the world on different aspects of solar measurements and using Random Forests regression with feature selection and downsampling on minor geomagnetic storm instances (which carry majority of the data), we are able to achieve an accuracy of 82.55% on data collected in 2021 when making early predictions three hours in advance. Given that important predictive features such as historic Kp indices are measured every 3 hours and their importance decay quickly with the amount of time in advance, an early prediction of 3 hours ahead of time is believed to be close to the practical limit.
Intense geomagnetic storms are characterized by a minimum value of the Dst index at or below -100 nT. It is well known that these storms are caused by the southward magnetic fields in coronal mass ejections (CMEs) and corotating interaction regions (CIRs). While CIR storms are confined to Dst values at or above -150 nT, CME storms can reach Dst -500 nT or lower. In this report, we illustrate the need to understand the storm evolution based on solar source and solar wind parameters using a recent storm (2023 April 24) by way of providing the motivation to catalog such events for a better understanding of the main phase time structure of geomagnetic storms
The relationship between moderate and extremely high levels of geomagnetic activity, represented by the Kp index (2- to 5+ and 6- to 9), and solar wind conditions during southward IMF intervals was revealed utilizing a newly developed machine learning technique. Potential learning (PL) is a neural network algorithm that emphasizes input parameters with the highest variance during training and identifies the most significant ones influencing the outputs based on a computed metric called "potentiality". We focus on the dependence of solar wind plasma density on moderate-geomagnetic conditions. It has been unclear from what stage of geomagnetic activity the solar wind density begins to control the Kp level. Previously, PL extracted solar wind velocity as the predominant parameter at extremely low (0 to 1+) and high-Kp ranges under southward IMF. In this study, the IMF three components, solar wind speed, and plasma density from the OMNI database (1998-2019), covering solar cycle 23 to early 25, were used as inputs. Again, PL selected solar wind velocity as the most significant parameter for moderate and extremely high Kp. The potentiality of solar wind density for these ranges was, however, 3.5 times higher than in the previous study, suggesting its impact on geomagnetic activity cannot be ignored. We statistically investigated the relation between solar wind speed and plasma density used as PL inputs under all Kp levels. Above moderate Kp, geomagnetic conditions become high even under slow solar wind if density is large, suggesting that not only velocity but also density contributes significantly. These PL and statistical investigations show that solar wind density begins to regulate Kp above moderate geomagnetic activity under southward IMF. They also help understand the relationship between solar wind and geomagnetic activity and forecast geomagnetic activity under various IMF conditions.
The ionospheric currents that flow in the E layer, the magnetospheric currents and the induced currents in the lithosphere under the station are the sources of geomagnetic variations in the calm days Sq. There exist several methods for estimating the values corresponding to magnetospheric currents. The magnitude of the induced currents contribution is considered about one third of the measured horizontal variation Sq. In this work, one proceeds to produce the correction for magnetospheric effects by analyzing the ring current index SMR, getting an efficient correction. Subsequently, the values of the north and east components of the surface current densities (KN and KE) above ROMP stations are estimated for calm days with a Kp index less than or equal to 2 in the 2019-2021 interval. It is found that at 14 hs LT, the maximum KN and KE values are recorded at PIL on 12.4.2020, while the minimum KN and KE values are recorded at ORC on 10.19.19 and 11.16.20, respectively.
Large-amplitude electrostatic solitary waves (ESWs) associated with asymmetric magnetic reconnection at the Earth’s magnetopause are studied in a four-component plasma composed of a mixture of the magnetosheath and magnetosphere plasma of a cold, warm and hot electron populations, and background ions. The species are modeled as adiabatic fluids except for the hot electrons which have a kinetic vortex-like velocity distribution. The hybrid model uses the Sagdeev pseudopotential technique to study the arbitrary amplitude ion- and electron-acoustic solitons and double layers. The numerical computations reveal that for the parameters corresponding to magnetosphere side of the ion diffusion region, only slow electron-acoustic solitons and double layer can exist. On the magnetosheath side of the ion diffusion region, only the electron-acoustic/beam solitons can exist. The electric field amplitude of the electrostatic solitary waves (ESWs) predicted by our model are consistent with the Magnetospheric Multiscale (MMS) observations.
Space weather and its impact on infrastructure presents a clear risk in the modern era, as evidenced by the adverse effects of geomagnetically induced currents (GICs) in power networks. To model GICs, ground-based geomagnetic field (B-field) measurements are critical and need to be available in the region of interest. A challenge globally lies in the sparse distribution of magnetometer arrays, which are seldom located near critical power network nodes. Interpolation of the geomagnetic field (B-field) is often needed, with the spherical elementary current system (SECS) approach developed for high-latitude regions favoured. We adapt this interpolation scheme to include low-cost variometers to interpolate dB/dt directly and increase interpolation accuracy. A further adaptation to the scheme is to physically represent the mid-latitude context where most power networks and pipelines lie. The driving current systems in these regions differ from their high-latitude counterparts. Using a physics-consistent mid-latitude version of SECS, we show why previous implementations in Southern Africa are incorrect but still result in useful interpolation. The scope of these adaptations not only has direct application to research in general, but also to utilities, where effective low-cost instrumentation can be used to improve GIC modelling accuracy.
In 1984, Clarke expanded the domain of observed quantum phenomena from the microscopic regimes of atoms and electrons (10^1-10^3 particles) to macroscopic superconducting circuits exhibiting quantum coherence (10^9-10^12). I show the coherent quantum behavior persists at scales far beyond atoms and circuits: in Earth's (10^51) resonant geophysical cycles, manifesting astro-macroscopic time-crystalline behavior. Thus, based on arguably the most accurate and precise spectral analysis of the most accurate global geomagnetism calibration (CKGPTS95), macroscopic and microscopic phenomena are interconnected through gravitational resonance networks across the cosmos, annulling conventional views of quantum invariance. The analyses unveiled a planet-dominating 9.35-My fundamental cycle arising from Earth's orbital and stellar gravitational influences, which resonantly governs long-term geomagnetic reversals, planetary growth, stratigraphic anomalies that mimic periodic mass extinctions, and the Great Unconformity. The resonances exhibit non-geophysical features of quantum coherence classically confined to microscopic systems, such as time crystals: discrete time-translation symmetry, fractional harmonic locking, and many-body entrainment. Given the ubiquity of the tidal phenomenon, a resonance-based framework exists in which large-scale celestial dynamics calibrate quantum analogously to how extragalactic dynamics calibrate stellar - thereby constraining particle masses, coupling constants, and universal parameters. This data-driven proof completes my 2006 theoretical derivation of G (and thus gravity) from c at both quantum and everyday scales, and confirms the Hyperresonance Unifying Theory, which unified those domains using only high-school algebra arXiv:0801.0876.
We investigate the maximum expected magnitudes of the geomagnetically induced currents (GICs) in the Czech transmission power network. We compute a model utilising the Lehtinen-Pirjola method, considering the plane-wave model of the geoelectric field, and using the transmission network parameters kindly provided by the operator. We find that the maximum amplitudes expected in the nodes of the Czech transmission grid during the Halloween storm-like event are about 15 A. For the "extreme-storm" conditions with a 1-V/km geoelectric field, the expected maxima do not exceed 40 A. We speculate that the recently proven statistical correlation between the increased geomagnetic activity and anomaly rate in the power grid may be due to the repeated exposure of the devices to the low-amplitude GICs.
In this paper, we analyze the applicability of the principal component analysis (PCA) as a tool to extract the Sq variation of the geomagnetic field. We tested different geomagnetic field components and used data measured at different levels of the solar and geomagnetic activity and during different months. Geomagnetic field variations obtained with PCA were classified as SqPCA using two types of reference series: SqIQD series calculated using geomagnetically quiet days and simulations of the ionospheric field with models. The results for the X and Y and Z components are essentially different. The Sq variation is always filtered to the first PCA mode for the Y and Z components. Thus, PCA can automatically extract the Sq variation from the observations of the Y and Z components of the geomagnetic field. For the X component, the automatic extraction of the Sq variation is not possible, and a complimentary analysis, like a comparison to a reference series, is always needed. We tested two types of reference series: the mean SqIQD and the outputs of the CM5 and DIFI3 models. Our results show that both the data-based and model-based reference series can be used but the DIFI3 model performs better. We also recommend estimating the similarity of the series not with the correlation analysis but using metrics that account for possible local stretching/compressing of the compared series, for example, the dynamic time warping (DTW) distance.