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We present a novel method to quantify the event‐specific spatial evolution of plasmaspheric hiss wave power using a Drift‐Diffusion model. Constrained by Polar Operational Environmental Satellites/Meteorological Operational Satellites data, the model simulates low‐altitude electron distributions, accounting for azimuthal drift, pitch‐angle diffusion, and atmospheric backscatter. Applying to an event on 15 October 2016, the model quantifies the spatial evolution of hiss waves at L = 3.9, which contributes to the steady decay of electron flux observed by Van Allen Probes (VAPs). The model reproduces local‐time dependent features and shows excellent agreement with hiss wave power observed by VAPs. The model shows increased wave power and spatial spread following increased activities in the AL‐index, consistent with previous statistical results. The model also suggests the presence of “low”‐frequency hiss, which was undetected by VAPs, likely masked by instrument noise. This is the first time low‐altitude measurements are used to quantify event‐specific wave distributions which include both diffusion and drift effects. Plain Language Summary We present a novel method to estimate the location and strength of plasmaspheric hiss waves in the Earth's magnetosphere during specific events. These waves play a critical role in shaping the dynamics of Earth's radiation belts by scattering energetic electrons into the atmosphere. However, directly measuring these waves everywhere in the magnetosphere is unfeasible given the inherent limited coverage of satellites. Our approach uses a Drift‐Diffusion model, a physics‐based simulation on how electrons behave in the presence of these waves, and through iteratively best‐fitting model to observations from Polar Operational Environmental Satellites/Meteorological Operational Satellites, we can quantify the free parameters in the model, such as wave power and its location. The model shows remarkable agreement with hiss wave observations from Van Allen Probes (VAPs) during an event on 15 October 2016. It also shows how the waves intensify and spread following increases in AL‐index activity, consistent with past statistical studies. The model also suggests the presence of “low”‐frequency hiss waves, though they were not detected by VAPs, likely due to instrument noise. This method provides a powerful tool to study wave activity in areas where direct measurements are unavailable. Key Points A novel method using a Drift‐Diffusion model constrained by Polar Operational Environmental Satellites/Meteorological Operational Satellites data estimates the event‐specific spatial evolution of wave power The model reveals increase in hiss wave power and spatial spread during high AL activity, consistent with previous statistical results The model suggests the presence of low‐frequency hiss waves during the event, though they were not observed by Van Allen Probes

The Indo-Gangetic basin is tectonically active, geologically complex, and geomorphologically diversified and has experienced major earthquakes over time in its stretch. This active megalopolis region lacks comprehensive seismic and geotechnical investigations. This study provides a correlation of topographic slope and shear wave velocity, utilizing a region-specific Vs30 model derived from joint modelling of Horizontal-to-Vertical Spectral Ratio (HVSR) and dispersion curves. Shuttle Radar Topography Mission (SRTM30) is considered to generate a new set of topographic-slope coefficients. The region predominantly corresponds to site Class D, as per NEHRP classification. The regional model provides a more accurate representation of seismic site conditions compared to the global model, which tends to overgeneralize and lacks site-specific setting. Seismic hazard maps generated for the Gorkha–Nepal Earthquake (2015) and Nepal–Bihar Earthquake (1934) scenarios show elevated risk in the northeastern region of southern Bihar. For the 2015 scenario, PGA (Peak Ground Acceleration) values in the high-risk eastern area range from 0.06 to 0.084 g, while the 1934 scenario shows values from 0.10 to 0.145 g. Spectral acceleration values at 0.2 and 1-sec periods also confirm greater ground motion potential in the eastern part, particularly around Patna and Nalanda. These findings provide critical insights for organizations such as National Disaster Management Authority of India (NDMA) and Geological Survey of India (GSI), helping to inform earthquake-resistant design, urban planning, and infrastructure development to improve resilience against future earthquakes in southern Bihar.Research highlightsThis study emphasizes the importance of reliable, region-specific shear wave velocity (Vs30) data in assessing seismic hazards and classifying sites in southern Bihar. Several factors influence seismic hazard assessment, depending on soil conditions.Reliable, region-specific Vs30 (shear wave velocity) data is critical for seismic hazard assessment and site classification in southern Bihar.Soil conditions and subsurface features significantly impact seismic wave amplification.Development of a proxy-based Vs30 model regionalized to southern Bihar.Combines Horizontal-to-Vertical Spectral Ratio (HVSR) and surface wave dispersion curves for precise subsurface characterization.Regional Vs30 data from joint modeling improved seismic site classifications compared to global models.Significant differences were observed between the proposed regional model and the global model by Wald and Allen (2007).Regional model showed smaller residuals and better alignment with observed values. Demonstrates the effectiveness of combining HVSR, surface wave dispersion, and topographic slope analysis for localized Vs30 estimation.This research provides valuable insights for organizations such as the National Disaster Management Authority (NDMA) and the Geological Survey of India (GSI), which can use the data to inform earthquake-resistant design, smart city planning for Varanasi, Patna and Bihar Sharif highlighted by the Government of India’s Smart Cities Mission (SCM) initiatives, and major construction projects in Kaimur (Bhabua), Bhojpur, Buxar, Jehanabad, Rohtas significantly for southern Bihar. The study underscores the effectiveness of combining HVSR, surface wave dispersion, and topographic slope analysis for localized Vs30 estimation, ensuring a more reliable seismic hazard evaluation framework.

Understanding past climate is essential to our knowledge of how our current climate system operates, and how it might respond to future change. Techniques to reconstruct climate history are challenging, and both accuracy and certainty are hampered by the quality of the datasets used. Here we both develop a new reconstruction tool and apply it to four ice core proxy based multi-millennial Holocene climate reconstructions, chosen because of their potential influence on East Antarctic climate. The new multi-proxy reconstruction method is called Segmented Linear Integral Correlation Kernel Ensemble Reconstruction (SLICKER). This method employs a segmented linear rather than Gaussian correlation approach and builds an ensemble of reconstructions with a best fit and spread related to the best estimate of uncertainty. This method is robust for non-linear, uneven or differently sampled data and produces high-fidelity reconstructions and associated uncertainty estimates. This new method has the potential to produce more realistic reconstructions, with associated uncertainty estimates based on robust statistical measures that are insensitive to outliers. The main findings from these new reconstructions are: Antarctica temperature shows multi-decadal variability over the last twelve thousand years with increased frequency over the last two thousand years; Zonal Wave 3 index and the Southern Annular Mode both show limited trends over the last two thousand years, but an increase since the 1970s CE; and the Indian Ocean Dipole Moment index has a twentieth century CE upward trend, and a thirteenth to sixteenth century CE below average period which may be related to volcanic activity.

The topographic slope method is an innovative, fast and very low-cost technique for estimating the average S-wave velocity in the upper 30 m (Vs30) based on the relationship between this quantity and the slope of the ground, obtained using a Digital Elevation Model (DEM). The method is based on the good linear correlations log(Vs30)–log(slope) found experimentally, which, ideally, should be determined for each region. If measured Vs30 data are not available to carry out this fitting for the study area, correlations from other areas could be used, although the reliability of the estimated Vs30 results would be lower. In this article, Vs30 observations are made for the city of Almeria, using Spatial Autocorrelation Surveys (SPAC) and Multichannel Analysis of Surface Waves (MASW), obtaining two types of fitting: (a) linear relationship log(Vs30)–log(slope); and (b) considering additional dependence on geological units. The reliability, evaluated by Multiple R-Squared (MRS), varies between 79.2% in the first case and 87.0% in the second, lowering the mean absolute values of the residuals at the observation points in the first case from 40.0 m/s to 29.0 m/s. Using a more generic correlation obtained for other areas of the world, the mean absolute residuals increase to 74.7 m/s.

Sleep spindles are abnormal in several neuropsychiatric conditions and have been implicated in associated cognitive symptoms. Accordingly, there is growing interest in elucidating the pathophysiology behind spindle abnormalities using rodent models of such disorders. However, whether sleep spindles can reliably be detected in mouse electroencephalography (EEG) is controversial necessitating careful validation of spindle detection and analysis techniques. Manual spindle detection procedures were developed and optimized to generate an algorithm for automated detection of events from mouse cortical EEG. Accuracy and external validity of this algorithm were then assayed via comparison to sigma band (10-15 Hz) power analysis, a proxy for sleep spindles, and pharmacological manipulations. We found manual spindle identification in raw mouse EEG unreliable, leading to low agreement between human scorers as determined by F1-score (0.26 ± 0.07). Thus, we concluded it is not possible to reliably score mouse spindles manually using unprocessed EEG data. Manual scoring from processed EEG data (filtered, cubed root-mean-squared), enabled reliable detection between human scorers, and between human scorers and algorithm (F1-score > 0.95). Algorithmically detected spindles correlated with changes in sigma-power and were altered by the following conditions: sleep-wake state changes, transitions between NREM and REM sleep, and application of the hypnotic drug zolpidem (10 mg/kg, intraperitoneal). Here we describe and validate an automated paradigm for rapid and reliable detection of spindles from mouse EEG recordings. This technique provides a powerful tool to facilitate investigations of the mechanisms of spindle generation, as well as spindle alterations evident in mouse models of neuropsychiatric disorders.

Accurate prediction of site amplification is crucial for seismic hazard assessment, particularly at shallow bedrock sites where limited data can constrain modeling efforts. Traditional regression-based models often fail to capture complex nonlinear interactions inherent in seismic ground response. This study aims to develop proxy-based linear and nonlinear site amplification models that provide reliable predictions using machine learning (ML) techniques, enabling practical applications in regional ground motion modeling. The outputs of a series of one-dimensional site response analyses were used for training. Three ML algorithms were used: random forest (RF), extreme gradient boosting (XGB), and deep neural network (DNN). The models incorporated four site proxies and two motion proxies to predict site amplification, and their performance was evaluated against both a conventional regression-based model and a rigorous ML model utilizing full shear-wave velocity profiles and input motion spectra. When identical proxies were used, the differences between the regression and ML-based models were not pronounced. However, when the ML model was trained simultaneously with the site and motion proxies for both linear and nonlinear components, the prediction performance was significantly enhanced. This revealed that the traditional two-track approach of the site-proxy-dependent linear component and motion-proxy-conditioned nonlinear component is ineffective. A pairing scheme for site and motion proxies is recommended to achieve the most accurate predictions. Among the three ML methods, the RF algorithm exhibited the weakest performance. The XGB and DNN algorithms’ prediction accuracies were superior to the RF algorithm. The XGB and DNN outperformed each other when predicting the linear and nonlinear components, respectively. The proposed ML models achieved coefficient of determination (R 2 ) values up to 0.97 with root mean square error (RMSE) as low as 0.04 for linear components, and R 2 up to 0.92 with RMSE as low as 0.06 for nonlinear components, demonstrating significant improvements over conventional regression-based models. Compared with a rigorous ML model, the proxy-based models exhibited agreeable predictions with far less information, illustrating the benefit of adopting the ML algorithms for improved adaptability and predictive capability. The constraint imposed on the site type, considering only profiles with a bedrock depth of less than 30 m, may have resulted in the strong performance of the proxy-based model.

Long-term estimation of extreme wave height remains a key challenge because of the short duration of available wave data, and also because of the possible impact of climate variability on ocean waves. Here, we analyse storm-based statistics to obtain estimates of extreme wave height at locations in the northeast Atlantic and North Sea using the NORA10 wave hindcast (1985–2011), and use a 5 year sliding window to examine temporal variability. The decadal variability is correlated to the North Atlantic oscillation and other atmospheric modes, using a six-term predictor model incorporating the climate indices and their Hilbert transforms. This allows reconstruction of the historic extreme climate back to 1661, using a combination of known and proxy climate indices. Significant decadal variability primarily driven by the North Atlantic oscillation is observed, and this should be considered for the long-term survivability of offshore structures and marine renewable energy devices. The analysis on wave climate reconstruction reveals that the variation of the mean, 99th percentile and extreme wave climates over decadal time scales for locations close to the dominant storm tracks in the open North Atlantic are comparable, whereas the wave climates for the rest of the locations including the North Sea are rather different.

This article reviews main progress in investigations of long-term trends in the mesosphere, thermosphere, and ionosphere over the period 2018–2022. Overall this progress may be considered significant. The research was most active in the area of trends in the mesosphere and lower thermosphere (MLT). Contradictions on CO2 concentration trends in the MLT region have been solved; in the mesosphere trends do not differ statistically from trends near the surface. The results of temperature trends in the MLT region are generally consistent with older results but are developed and detailed further. Trends in temperatures might significantly vary with local time and height in the whole height range of 30–110 km. Observational data indicate different wind trends in the MLT region up to the sign of the trend in different geographic regions, which is supported by model simulations. Changes in semidiurnal tide were found to differ according to altitude and latitude. Water vapor concentration was found to be the main driver of positive trends in brightness and occurrence frequency of noctilucent clouds (NLCs), whereas cooling through mesospheric shrinking is responsible for a slight decrease in NLC heights. The research activity in the thermosphere was substantially lower. The negative trend of thermospheric density continues without any evidence of a clear dependence on solar activity, which results in an increasing concentration of dangerous space debris. Significant progress was reached in long-term trends in the E-region ionosphere, namely in foE (critical frequency of E region, corresponding to its maximum electron density). These trends were found to depend principally on local time up to their sign; this dependence is strong at European high midlatitudes but much less pronounced at European low midlatitudes. In the ionospheric F2 region very long data series (starting at 1947) of foF2 (critical frequency of F2 region, corresponding to the maximum electron density in the ionosphere) revealed very weak but statistically significant negative trends. First results of long-term trends were reported for the topside ionosphere electron densities (near 840 km), the equatorial plasma bubbles, and the polar mesospheric summer echoes. The most important driver of trends in the upper atmosphere is the increasing concentration of CO2, but other drivers also play a role. The most studied one was the effect of the secular change in the Earth's magnetic field. The results of extensive modeling reveal the dominance of secular magnetic change in trends in foF2 and its height (hmF2), total electron content, and electron temperature in the sector of about 50∘ S–20∘ N, 60∘ W–20∘ E. However, its effect is locally both positive and negative, so in the global average this effect is negligible. The first global simulation with WACCM-X (Whole Atmosphere Community Climate Model eXtended) for changes in temperature excited by anthropogenic trace gases simultaneously from the surface to the base of the exosphere provides results generally consistent with observational patterns of trends. Simulation of ionospheric trends over the whole Holocene (9455 BCE–2015) was reported for the first time. Various problems of long-term-trend calculations are also discussed. There are still various challenges in the further development of our understanding of long-term trends in the upper atmosphere. The key problem is the long-term trends in dynamics, particularly in activity of atmospheric waves, which affect all layers of the upper atmosphere. At present we only know that these trends might be regionally different, even opposite.

The time-averaged shear wave velocity in the upper 30 m of soil, also known as V s30 is the basic criteria employed in the seismic site condition and site response analysis. V s30 is globally utilized in preparing Seismic Site Characterization Maps (SSCMs) regardless of its considerable limitations. This paper presents the acquisition, and interpretation of 280 real-field V s30 measurements using a geophysical instrument named Tromino. Models have been derived based on the three proxies i.e., geological age, lithology and topographic slope. Data analysis and statistical approaches show a good correlation between measured V s30 values with the adopted proxies. The models presented in this work consider topographic slope based on a 30 m resolution digital elevation model (DEM) in addition to the three geological age and four lithological groups. Cross-correlation of the proxies is performed to correlate the proxies for constructing proxy-based models. Proxy performance evaluation is carried out on the basis of residual analysis and the correlation between real-field V s30 measurements and V s30 values estimated from proxies is offered to assess the V s30 predictive capacity of these proxies. The study establishes a strong correlation between measured V s30 values in densely populated areas and V s30 values estimated through proxies, suggesting the viability of geological age, lithology, and slope as effective proxies for local-scale V s30 estimation. The models introduced in the study are assessed for proxy performance, and scatter plots for slope-dependent geological units are provided for further evaluation. The final SSCM ( V s30 map) categorizes the area into National Earthquake Hazard Reduction Programme (NEHRP) site classes which shows that most of the area is prone to amplified seismic response.

This study incorporates the comprehensively observed proxies of in situ geotechnical, geophysical, petrophysical, and lithological datasets to estimate groundwater presence. Two machine-learning approaches, random forest regression (RFR) and deep neural network (DNN), are applied. The constructed RFR and DNN models are validated using observed depths of groundwater levels at 772 K-NET sites in Japan. The RFR model exhibited effectiveness and robust performance compared to the poor-fitting performance of the DNN model and previous groundwater detection physical-based approaches. The RFR and DNN models yielded a remarkable 1:1 agreement between the observed and predicted groundwater levels at 733 and 470 K-NET sites, respectively. During the RFR training process, all datasets at the 772 K-NET sites were split into training, validating, and unseen testing datasets with the ratio set at 1:1:11. This k-fold cross-validation strategy demonstrates better-fitting performance for the RFR model. The contributions and interactions among the in situ observed proxies utilizing the variance-based global sensitivity analyses can be understood. The P-wave velocity and the standard penetration test values have exhibited prominent contributions among other proxies at groundwater depths. To apply the RFR model at any given site, reliable and detailed P- and S-wave velocity structures are crucial to building the needed source datasets.