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The El Niño-Southern Oscillation (ENSO), originating in the central and eastern equatorial Pacific, is a defining mode of interannual climate variability with profound impact on global climate and ecosystems. However, an understanding of how the ENSO might have evolved over geological timescales is still lacking, despite a well-accepted recognition that such an understanding has direct implications for constraining human-induced future ENSO changes. Here, using climate simulations, we show that ENSO has been a leading mode of tropical sea surface temperature (SST) variability in the past 250 My but with substantial variations in amplitude across geological periods. We show this result by performing and analyzing a series of coupled time-slice climate simulations forced by paleogeography, atmospheric CO2 concentrations, and solar radiation for the past 250 My, in 10-My intervals. The variations in ENSO amplitude across geological periods are little related to mean equatorial zonal SST gradient or global mean surface temperature of the respective periods but are primarily determined by interperiod difference in the background thermocline depth, according to a linear stability analysis. In addition, variations in atmospheric noise serve as an independent contributing factor to ENSO variations across intergeological periods. The two factors together explain about 76% of the interperiod variations in ENSO amplitude over the past 250 My. Our findings support the importance of changing ocean vertical thermal structure and atmospheric noise in influencing projected future ENSO change and its uncertainty. [ABSTRACT FROM AUTHOR]

In this paper, the study of the homogeneous resistivity and two‐layer soil influences on the performance of the electrodes of grounding systems subjected to lightning strikes is carried out considering soil ionization. The study is done on the behaviour of the ground electrodes while the resistivity of the soil is altered from small to high value in the case of homogeneous soil, while the study is obtained on two‐layer soils in the case of diverse values of the reflection factor. In the suggested algorithm for two‐layer soils, each layer has its resistivity and dielectric constant, where the dielectric constant and soil resistivity depend on the lightning frequency. The field resulting from lightning strikes, which causes soil ionization, has been studied, and the results have been used to understand the behaviour of ground rods under the influence of lightning strikes. The credibility of the results has been strengthened by comparing it with what others have obtained recently. The article's novelty can be summarized in the investigation of the performance of the electrodes of grounding grids that are installed in high soil resistivity of uniform and two‐layer soils containing the impacts of soil ionization, lightning frequency, soil resistivity, and permittivity variations. [ABSTRACT FROM AUTHOR]

Sentinel-2/MSI and Landsat-8/OLI sensors enable the mapping of ocean color-related bio-optical parameters of surface coastal and inland waters. While many algorithms have been developed to estimate the Chlorophyll-a concentration, Chl-a, and the suspended particulate matter, SPM, from OLI and MSI data, the absorption by colored dissolved organic matter, acdom, a key parameter to monitor the concentration of dissolved organic matter, has received less attention. Herein we present an inverse model (hereafter referred to as AquaCDOM) for estimating acdom at the wavelength 412 nm (acdom (412)), within the surface layer of coastal waters, from measurements of ocean remote sensing reflectance, Rrs (λ), for these two high spatial resolution (around 20 m) sensors. Combined with a water class-based approach, several empirical algorithms were tested on a mixed dataset of synthetic and in situ data collected from global coastal waters. The selection of the final algorithms was performed with an independent validation dataset, using in situ, synthetic, and satellite Rrs (λ) measurements, but also by testing their respective sensitivity to typical noise introduced by atmospheric correction algorithms. It was found that the proposed algorithms could estimate acdom (412) with a median absolute percentage difference of ~30% and a median bias of 0.002 m−1 from the in situ and synthetic datasets. While similar performances have been shown with two other algorithms based on different methodological developments, we have shown that AquaCDOM is much less sensitive to atmospheric correction uncertainties, mainly due to the use of band ratios in its formulation. After the application of the top-of-atmosphere gains and of the same atmospheric correction algorithm, excellent agreement has been found between the OLI- and MSI-derived acdom (412) values for various coastal areas, enabling the application of these algorithms for time series analysis. An example application of our algorithms for the time series analysis of acdom (412) is provided for a coastal transect in the south of Vietnam. [ABSTRACT FROM AUTHOR]

To investigate the interference effects of lightning on unmanned aerial vehicle (UAV) navigation receivers, an analytical approach for the evaluation of indirect lightning effects is suggested. The blocking mechanism of lightning pulses on the receiver is analyzed using the vector method. A behavioral-level simulation model of the receiver's radio frequency (RF) front-end is constructed, and a lightning pulse injection simulation is performed based on the advanced design system (ADS). The simulation results indicate that lightning pulses induce blocking interference on the RF front-end of the navigation receiver, reducing its sensitivity and dynamic range. This interference is attributed to gain compression and degradation of the noise figure in the low-noise amplifier (LNA). To further validate the simulation results, a pin injection test is carried out to examine the influence of lightning pulses with different peak voltages on receiver sensitivity. The test results indicate a notable reduction in the receiver sensitivity when the lightning pulse peak reaches 250 V. The sensitivity trends across various test panels are consistent, and the damage thresholds are similar. These findings confirm the accuracy of the simulation results. [ABSTRACT FROM AUTHOR]

Lightning generated electromagnetic impulses propagating in the magnetospheric plasma disperse into whistlers – several seconds long radio wave signals with decreasing frequency. Sometimes, multiple reflections form long echo trains containing many whistlers with increasing dispersion. On January 3, 2017, two necessary prerequisites – a pronounced lightning activity and a magnetospheric plasma duct – allowed for observations of a large number of whistler echo trains by the high-latitude station in Kannuslehto, Finland. Our investigation reveals that the duct existed for nearly eight hours. We show that causative lightning sferics arrived to the duct entry from three different winter thunderstorms: a small storm at the Norwegian coast, which produced energetic lightning capable to trigger echo trains in 50% of cases, and two large storms at unexpectedly distant locations in the Mediterranean region. Our results show that intense thunderstorms can repetitively feed electromagnetic energy into a magnetospheric duct and form whistler echo trains after subionospheric propagation over distances as large as 4000 km. When lightning generated whistler waves echo multiple times, they are called whistler echo trains. Here, the authors show lightning strokes from three thunderstorm systems responsible for long-lasting and intense whistler echo trains. [ABSTRACT FROM AUTHOR]

Independent lightning rods are often installed in substation grounding systems for lightning protection. The concentrated discharge channel formed when a lightning current flows to the ground through a grounding electrode will cause electromagnetic interference to the directly buried secondary cable. And the three-dimensional structure of the discharge channel will affect the transient electromagnetic field distribution, thereby affecting the electromagnetic transients on cable shields. In order to explore the influence of soil discharge phenomena on the electromagnetic interference of the directly buried secondary cable, in this paper, we carried out experiments on cables in two different grounding modes, single-ended and double-ended grounding, and captured image of soil discharge channels. The results show that the cable grounding mode will affect the coupling mode that causes the shielding layer current. The relative spatial position of the soil discharge channel and the cable has a significant impact on the magnitude of the shielding layer current under both grounding modes. The water content and salt content of the soil also have different degrees of influence on the coupling current of the shielding layer in different grounding modes. [ABSTRACT FROM AUTHOR]

Chromospheric condensations (CCs) are a prominent feature of flare footpoint heating in the solar flare standard model, yet their timescales and velocities are not well understood. Fisher derived several important analytical relationships, which have rarely been examined with modern spectral observations. The Interface Region Imaging Spectrograph (IRIS) provides a wealth of flare data with a high enough cadence to sufficiently capture CC evolution. We analyzed Doppler shifts in Mg ii 2791 and Fe ii 2814 from a sample of flare footpoint pixels observed by IRIS to compare with Fisher's analytics and recent flare models. We found a detection lifetime of 1 minute occurs in 50% of the sample, with Mg ii showing several pixels with longer values and Fe ii almost categorically shorter, and both growing with the maximum velocity, v max. The shifts' half-life is commonly <40 s and is inversely related to v max, indicating that the first half of the CC evolution has more efficient kinetic energy loss. The lifetime's wide range and growth with v max indicate that the footpoint atmospherics and heating scenarios can vary more widely than first postulated in Fisher. Around 90% of the sample had observable acceleration periods, lasting an average of 38 and 32 s for Mg ii and Fe ii, respectively. These acceleration periods, as well as serving as flare model diagnostics themselves, could potentially be used to calculate other model diagnostics such as the initially accelerated mass. [ABSTRACT FROM AUTHOR]

The rapid growth of e-commerce and the increasing importance of online shopping experiences necessitate a better understanding of online atmospherics. This research paper presents a comprehensive study on the antecedents of online atmospherics, proposing a conceptual framework to analyze the factors that contribute to a positive online experience. The study divides online atmospherics factors into three main categories: Design Factors, Ambient Factors, and Virtual Agents. Design play a crucial role in shaping users' perceptions and influencing their purchasing decisions. Ambient Factors, create an immersive atmosphere that enhances the overall user experience. Virtual Agents, facilitate user interactions and provide personalized assistance. This research highlights the need for online retailers to holistically consider these factors to optimize their online presence, ultimately leading to improved customer satisfaction, increased user engagement, and higher sales. The proposed conceptual framework serves as a foundation for future empirical studies to further investigate the relationships between these factors and their impact on online atmospherics. [ABSTRACT FROM AUTHOR]

NASA's Ice, Cloud, and land Elevation Satellite (ICESat-2), designed for surface altimetry, plays a pivotal role in providing precise ice sheet elevation measurements. While its primary focus is altimetry, ICESat-2 also offers valuable atmospheric data. Current conventional processing methods for producing atmospheric data products encounter challenges, particularly in conditions with low signal or high background noise. The thresholding technique traditionally used for atmospheric feature detection in lidar data uses a threshold value to accept signals while rejecting noise, which may result in signal loss or false detection in the presence of excessive noise. Traditional approaches for improving feature detection, such as averaging, lead to a trade-off between detection resolution and accuracy. In addition, the discrimination of cloud from aerosol in the identified features is difficult given ICESat-2's single wavelength and lack of depolarization measurement capability. To address these challenges, we demonstrate atmospheric feature detection and cloud–aerosol discrimination using deep learning-based semantic segmentation by a convolutional neural network (CNN). The key findings from our research are the effectiveness of a deep learning model for feature detection and cloud–aerosol classification in ICESat-2 atmospheric data and the model's surprising capability to detect complex atmospheric features at a finer resolution than is currently possible with traditional processing techniques. We identify several examples where the traditional feature detection and cloud–aerosol discrimination algorithms struggle, like in scenarios with several layers of vertically stacked clouds, or in the presence of clouds embedded within aerosol, and demonstrate the ability of the CNN model to detect such features, resolving the boundaries between adjacent layers and detecting clouds hidden within aerosol layers at a fine resolution. [ABSTRACT FROM AUTHOR]

In recent years, synthetic aperture radar (SAR) interferometry (InSAR) has emerged as a valuable tool for measuring water level change (WLC) to study hydrodynamic processes in coastal wetlands. However, the highly dynamic wet atmosphere conditions common in these areas have a significant impact on InSAR observations, producing errors in the derived values. Standard methods for estimating atmospheric noise in InSAR time series lack the spatial or temporal resolution needed to adequately correct for wet tropospheric delays. In this study, we utilize the Independent Component Analysis (ICA) signal decomposition technique to identify the likely WLC signal and eliminate atmospheric noise in a time series derived from rapid repeat measurements made with the L‐band uninhabited aerial vehicle synthetic aperture radar airborne instrument. The method compares in‐situ water level measurements with the independent components (IC) to identify the ICA components corresponding to WLC. The signal‐to‐noise ratio between the WLC after the ICA‐based filtering and in situ water level gauges used for validation reaches 16 dB compared to an average of 2.6 dB before filtering. The excluded IC are used to generate maps showing a time series of likely atmospheric features. The identified features in the maps generally correspond to atmospheric features identifiable in Next Generation Weather Radar (NEXRAD) S‐band ground weather radar reflectivity maps collected during the SAR acquisitions. The method is sufficiently general to be applied to any InSAR‐derived surface displacement time series. Key Points: Independent Component Analysis (ICA) is used to remove the wet troposphere noise in an InSAR‐derived time series of water level change in wetlandsIn‐situ data are used to identify components showing water level change and the other components used to generate tropospheric delay mapsThe filtered water level change values have improved accuracy based on measurements from water gauges located throughout the wetlands [ABSTRACT FROM AUTHOR]

In this work, we propose a differential complementary split-ring resonator (CSRR) sensor as the resonating element for permittivity analysis of liquid samples. In comparison to conventional non-differential sensors, differential sensors are found to be immune to environmental changes. The proposed sensor operates at 2.35 GHz of the ISM band and is built on a low-cost FR4 substrate. The sensor is thoroughly optimized and validated using Ansys High Frequency Structure Simulator software. Multiple liquid samples covering a wide dielectric range of 1–111 are used to determine the sensing performance of the sensor. The sensitivity of the sensor is found to be high and on par with recent works related to differential CSRR sensors. Based on the frequency of the transmission notch observed, a fit equation is developed to determine the dielectric constant of unknown samples. The proposed differential sensor promises to be a good alternative to traditional CSRR sensors for requirements involving noise and atmospheric fluctuations. [ABSTRACT FROM AUTHOR]

• A novel optimization algorithm integrating random and grid search mechanisms as function calls. • A cost-effective low inference time deep learning model for detecting 2D ionospheric plasma structures from all-sky airglow images. • The issue of uncertainty quantification has been addressed using the approach of bootstrapping. The ionosphere, part of the Earth's atmosphere, where different plasma irregularities/structures are generated through various electrodynamical processes. Airglow imaging is one of the tools to observe various morphological features of these irregularities. We aim to help all-sky airglow imaging-based ionospheric research by proposing novel deep learning at the edge framework for the detection of different types of mid-latitude ionospheric plasma structures (medium scale traveling ionospheric disturbances with single and multiple bands, and plasma bubbles). The primary challenge was to find an optimal deep neural network by considering the trade-off between accuracy and inference time. In order to address this, a novel hyperparameter optimization technique has been used that integrates the approaches of random and grid search mechanisms as a compact function. The random function generates the subspace for hyperparameters to check the convergence of the model while the grid search creates possible combinations to tune these hyperparameters. Our novel optimization method for deep learning inference at the edge led us to a low-cost, high-accuracy convolutional neural network (CNN) model, which outperformed the complex state-of-the-art deep learning models such as Inception-v3, DenseNet169, VGG16, and VGG19. For airglow image-based plasma structure detection, the proposed model recorded an accuracy of 99.9% with an inference time of 5.8 s. This was an improvement of about 4% in accuracy while 85% reduction in inference time over the best-performing baseline. There was about 93% reduction in the number of model parameters with respect to the best-performing baseline. Uncertainty quantification has also been performed in the present work through bootstrapping to validate the robustness of the proposed model. The findings of our study show the promise of deep learning at the edge for all-sky airglow imaging systems by demonstrating an alternate low-cost, high-accuracy deep neural network. [ABSTRACT FROM AUTHOR]

Space plasma turbulence plays a relevant role in several plasma environments, such as solar wind and the Earth's magnetosphere–ionosphere system, and is essential for describing their complex coupling. This interaction gives rise to various phenomena, including ionospheric irregularities and the amplification of magnetospheric and ionospheric currents. The structure and dynamics of these currents have relevant implications, for example, in studying ionospheric heating and the nature of electric and magnetic field fluctuations in the auroral and polar environments. In this study, we investigate the nature of small-scale fluctuations characterizing the ionospheric magnetic field in response to different geomagnetic conditions. We use high-resolution (50 Hz) magnetic data from the ESA's Swarm mission, collected during a series of high-latitude crossings, to probe the scaling features of magnetic field fluctuations in auroral and polar cap regions at spatial scales still poorly explored. Our findings reveal that magnetic field fluctuations in field-aligned currents (FACs) and polar cap regions across both hemispheres are characterized by different scaling properties, suggesting a distinct driver of turbulence. Furthermore, we find that geomagnetic activity significantly influences the nature of energy dissipation in FAC regions, leading to more localized filamentary structures toward smaller scales. [ABSTRACT FROM AUTHOR]

Despite the acknowledged connection between physical environment and human behavior, the literature often underestimates the crucial role of hotel spatial layout and its relation to other factors in shaping customer loyalty. The layout of theme hotels is often simply considered a general environmental consideration, ignoring its far-reaching impact. This research proposes that guests staying at theme hotels will perceive value when utilizing the layout and appreciate all aspects of hotel facilities. This, in turn, influences their value perception and loyalty to the hotel. To analyze the data, confirmatory factor analysis and path analysis were employed on a sample of 298 individuals who stayed in theme hotels. The findings from these analyses reveal that both the hotel layout and the external environment positively influence guests' evaluations of the internal environment (R2 = 0.640). Furthermore, the evaluation of the internal environment and layout significantly impacts guests' perceived values (R2 = 0.517), subsequently shaping their loyalty to the hotel (R2 = 0.370). Perceived value emerges as a vital mediating factor between the layout and customer loyalty. This study presents compelling evidence that spatial layout significantly influences customer loyalty in theme hotels through perceived value, transcending internal and external environmental boundaries. [ABSTRACT FROM AUTHOR]

We present the observations of field‐aligned currents and the equatorial electrojet during the 23 March 2023 magnetic storm, focusing on the effect of the drastic decrease of the solar wind dynamic pressure occurred during the main phase. Our observations show that the negative pressure pulse had significant impact to the magnetosphere‐ionosphere system. It weakened large‐scale field‐aligned currents and paused the progression of the storm main phase for ∼3 hr. Due to the sudden decrease of the plasma convection after the negative pressure pulse, the low‐latitude ionosphere was over‐shielded and experienced a brief period of westward penetration electric field, which reversed the direction of the equatorial electrojet. The counter electrojet was observed both in space and on the ground. A transient, localized enhancement of downward field‐aligned current was observed near dawn, consistent with the mechanism for transmitting MHD disturbances from magnetosphere to the ionosphere after the negative pressure pulse. Plain Language Summary: The solar wind is a continuous stream of charged particles blowing from the Sun. The Earth's magnetic field forms a protective shield around our planet, called the magnetosphere, which deflects most of the solar wind particles away from the Earth. Disturbances in the solar wind can interact with the magnetosphere and impact the Earth's upper atmosphere (ionosphere). The interaction creates electric fields forcing charged particles to move in the magnetosphere, which creates electric currents flowing along the magnetic field lines connecting to the high‐latitude ionosphere and drives the movement of charged particles there. The low‐latitude ionosphere is generally shielded from these electric fields. Sudden changes in the solar wind can break such balance, leading to the electric field penetration to low latitudes. We examined how the magnetosphere and ionosphere interacted during the 23 March 2023 geomagnetic storm, focusing on what happened when the solar wind dynamic pressure suddenly decreased. We found the pressure drop caused a sudden decrease of the high‐latitude electric field, resulting in a brief period of overshielding and the electric field in the equatorial ionosphere reversed its direction. This changed the direction of the equatorial electrojet, a major electric current in the ionosphere at the magnetic equator. Key Points: Direct evidence of prompt penetration of electric field in the equatorial ionosphere caused by negative solar wind pressure pulseTransient counter electrojet caused by westward penetration electric field after the arrival of negative pressure pulseSignificant decrease of global large‐scale field‐aligned currents (FACs) and transient enhancement of localized FAC in response to negative pressure pulse [ABSTRACT FROM AUTHOR]

We statistically evaluate the global distribution and energy spectrum of electron precipitation at low‐Earth‐orbit, using unprecedented pitch‐angle and energy resolved data from the Electron Losses and Fields INvestigation CubeSats. Our statistical results indicate that during active conditions, the ∼63 keV electron precipitation ratio peaks at L > 6 at midnight, whereas the spatial distribution of precipitating energy flux peaks between the dawn and noon sectors. ∼1 MeV electron precipitation ratio peaks near midnight at L > ∼6 but is enhanced near dusk during active times. The energy spectrum of the precipitation ratio shows reversal points indicating energy dispersion as a function of L shell in both the slot region and at L > ∼6, consistent with hiss‐driven precipitation and current sheet scattering, respectively. Our findings provide accurate quantification of electron precipitation at various energies in a broad region of the Earth's magnetosphere, which is critical for magnetosphere‐ionosphere coupling. Plain Language Summary: Precipitation into the Earth's upper atmosphere is an important loss process of radiation belt electrons and can change the ionospheric conductance and atmospheric chemistry. Electrons can be moved into the loss cone through either wave‐particle interactions or due to special magnetic field geometries, such as current sheet scattering (CSS). To determine the relative contribution of wave‐particle interactions and CSS, it is crucial to investigate the global distribution and energy spectra of the electron precipitation rate. Previous measurements of energetic electron precipitation at low‐Earth‐orbit either had low resolution in pitch angle and energy or were contaminated by ions and penetrating particles. In this study, we used data from the twin Electron Losses and Fields INvestigation CubeSats, which do not suffer from similar deficiencies, to statistically analyze the dependence of energetic electron precipitation rates on geomagnetic activity, with high resolution in both pitch angle and energy. These findings cast new light on the physical mechanisms that determine the precipitation rate from the magnetosphere into the atmosphere and may have implications for future studies of the ionospheric conductivity and atmospheric chemistry. Key Points: A global survey of electron precipitation is performed using the pitch angle and energy resolved data from Electron Losses and Fields INvestigation CubeSatsAt ∼1 MeV, electron precipitation is strongest at L > ∼6 near midnight and is enhanced near dusk during active timesThe energy spectrum of the precipitation ratio shows reversal points indicating energy dispersion in L shells in the slot region and L > ∼6 [ABSTRACT FROM AUTHOR]

Global navigation satellite system (GNSS) provides users with all-weather, continuous, high-precision positioning, navigation, and timing (PNT) services. In the operation and use of GNSS, the influence of the space environment is a factor that must be considered. For example, during geomagnetic storms, a series of changes in the Earth's magnetosphere, ionosphere, and upper atmosphere affect GNSS's positioning performance. To investigate the positioning performance of global satellite navigation systems during geomagnetic storms, this study selected three geomagnetic storm events that occurred from September to December 2023. Utilizing the global positioning system (GPS)/Beidou navigation satellite system (BDS) dual-system, kinematic precise point positioning (PPP) experiments were conducted, and the raw observational data from 100 stations worldwide was analyzed. The experimental results show that the positioning accuracy of some stations in high-latitude areas decreases significantly when using the conventional Geometry Free (GF) cycle-slip detection threshold during geomagnetic storms, which means that the GF is no longer applicable to high-precision positioning services. Meanwhile, there is no significant change in the satellite signal strengths received at the stations during the period of the decrease in positioning accuracy. Analyzing the cycle-slip rates for stations where abnormal accuracy occurred, it was observed that stations experiencing a significant decline in positioning accuracy exhibited serious cycle-slip misjudgments. To improve the kinematic PPP accuracy during magnetic storms, this paper proposes an adaptive threshold for cycle-slip detection and designs five experimental strategies. After using the GF adaptive threshold, the station positioning accuracy improved significantly. It achieved the accuracy level of the quiet period, while the cycle-slip incidence reached the average level. During magnetic storms, the ionosphere changes rapidly, and the use of the traditional GF constant threshold will cause serious cycle-slip misjudgments, which makes the dynamic accuracy in high latitude areas and some mid-latitude areas uncommon, while the use of the GF adaptive threshold can alleviate this phenomenon and improve the positioning accuracy in the high-latitude regions and some of the affected mid-latitude areas during the magnetic storms. [ABSTRACT FROM AUTHOR]

Mass changes in the hydrosphere represent an important contributor to polar motion (PM) variations, especially at seasonal time scales (i.e., annual and semiannual). Although well studied, hydrological angular momentum (HAM) remains a major source of uncertainty in estimating PM excitation. In this work, we use a large number of climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to determine HAM series both from individual models and their combination, formed with a multimodel mean, a weighted mean, and a three-cornered hat (TCH) method. The CMIP6-based HAM series are analysed in several spectral bands and evaluated with a reference hydrological signal in geodetically observed PM excitation (GAO). HAM determined from CMIP6 were also compared to HAM calculated from Gravity Recovery and Climate Experiment (GRACE) measurements. We find that while climate models do not allow for reliable estimation of nonseasonal changes in HAM, they can help interpret seasonal variability. For annual prograde and semiannual retrograde oscillations, several combined CMIP6- based series exhibit higher amplitude and phase consistency with GAO than the corresponding series computed from GRACE data. Whether one uses a simple average of the models, a weighted average, or a combination of models from the TCH method has little impact on the resulting HAM series and their level of agreement with GAO. Our study advances the understanding of hydrological signal in Earth's rotation at seasonal time scales. [ABSTRACT FROM AUTHOR]

The North Pacific sea surface temperature (SST) has a profound climatic influence. The El Niño‐Southern Oscillation (ENSO) significantly impacts the North Pacific SST; however, the influence of the distinct phases of ENSO on SST predictability remains unclear. To overcome the model limitations, this study assessed SST predictability under diverse ENSO phases using reanalysis. The predictability limit of the North Pacific SST under La Niña (8.4 months) is longer than that under Neutral (5.9 months) and El Niño (5.5 months) conditions, which unveils asymmetry. This asymmetry mirrors contemporary multimodal prediction skills. Error growth dynamics reveal La Niña's robust signal strength with a slow error growth rate, in contrast to El Niño's weaker signal and faster error growth. There exhibits intermediate signal strength and elevated error growth in Neutral condition. Physically, predictability signal strength aligns with SST variability, whereas the error growth rate correlates with atmospheric‐ocean heating anomalies. La Niña, which induces positive heating anomalies, minimizes the impact of atmospheric noise, resulting in lower error growth. The result is beneficial for improving North Pacific SST predictions. Plain Language Summary: The North Pacific sea surface temperature (SST) significantly impacts global climate, particularly in East Asia and the United States. Accurate prediction of North Pacific SST, which is crucial for seasonal and interannual forecasting, remains a focal point in atmospheric and oceanic research. This study investigates how the El Niño‐Southern Oscillation (ENSO) influences SST predictability in the North Pacific during different phases (El Niño, La Niña, Neutral). Using reanalysis data, this research introduces the concept of predictability limit (PL), revealing an asymmetry in the PL response: La Niña extends the PL to 8.4 months, El Niño shortens it to 5.5 months, and Neutral falls in 5.9 months. This asymmetry aligns with the existing prediction skills. Examining the error growth dynamics, La Niña exhibits a robust SST signal with slower error growth, whereas El Niño displays a weaker signal with faster error growth. Neutral, meanwhile, shows a high signal strength but large error growth. This study sheds light on these differences in SST predictability and provides valuable insights for refining North Pacific SST predictions through model advancements. Key Points: El Niño‐Southern Oscillation phases asymmetrically impact North Pacific sea surface temperature (SST) predictability: El Niño (5.5 months), La Niña (8.4 months), Neutral (5.9 months)Error growth dynamics vary: La Niña displays strong signals with slow growth, El Niño exhibits weak signals and rapid growthA positive heating anomaly from La Niña reduces atmospheric noise impact, resulting in a smaller error growth rate of the North Pacific SST [ABSTRACT FROM AUTHOR]

A fully automated processing system for measuring long-term ground deformation time series and deformation rates frame-by-frame using the Differential Interferometric Synthetic Aperture Radar (DInSAR) processing technique was developed and tested. Among the several factors that affect DInSAR's precision, such as temporal decorrelation and atmospheric noise, it was observed that seasonal spatial-temporal variability in radar penetration depth is the greatest contributor to the loss of precision of the long-term deformation rate measurement. This spatial-temporal signal is described for a specific region in the northern US; however, it is widely observed in multiple regions. In these regions, a gradual penetration depth increase is observed in fall and winter, and an abrupt penetration depth decrease is observed in spring. The latter effect is likely caused by rapid melting of ice and snow that, at the same time, significantly reduces interferometric coherence. Within a specific geographic region, radar penetration depth spatially varies proportionally to the elevation due to an elevation-dependent temperature gradient, producing artifacts that correlate well with the topography. This signal does not extend to high-elevation regions (above ~2500 m) that do not experience seasonal thawing, which allows for distinguishing it from the topography-dependent atmospheric signals. Without accounting for the spatial-temporal variability in radar penetration depth, deformation time series in these areas can contain either an annual periodic signal with an amplitude of over 0.1 m or, if the abrupt penetration depth decrease produced by melting is not resolved due to lower coherence, an erroneous interannual trend. Spatial-temporal variability in radar penetration depth needs to be better understood and corrected to improve the precision of long-term deformation rate products, particularly in regions susceptible to seasonal freezing and thawing. [ABSTRACT FROM AUTHOR]

Infrared multispectral imaging technology can achieve the long-distance, wide-ranging and fast detection of target gas, and has been widely used in the fields of dangerous-gas detection and environmental monitoring. However, due to the difficulty in acquiring background radiation as well as atmospheric disturbance and noise interference in the detection process, the quantitative detection of gas concentration has become a difficult problem to solve. Therefore, this paper proposes an inversion method for gas column concentration based on infrared multispectral imaging technology. Firstly, infrared background radiation images of the non-target gas absorption spectrum band were collected and converted into background radiation images of the target gas absorption spectrum band according to the dual-channel mapping relationship. Then, combined with the gas radiation images of the target gas absorption spectrum band, the column concentration distribution of the gas was obtained by using the measured calibration relationship between absorbance and column concentration. Experiments of gas detection in different environments were carried out, and the column concentration distribution of the target gas was inverted using this method; the results showed that the average relative error of the inversion of the gas column concentration was 4.84%, which enables the quantitative detection of gas column concentration in a complex environment. [ABSTRACT FROM AUTHOR]

Slow and long-term variations of sea surface temperature anomalies have been interpreted as a red-noise response of the ocean surface mixed layer to fast and random atmospheric perturbations. How fast the atmospheric noise is damped depends on the mixed layer depth. In this work we apply this theory to determine the relevant spatial and temporal scales of surface layer thermal inertia in lakes. We fit a first order auto-regressive model to the satellite-derived Lake Surface Water Temperature (LSWT) anomalies in Lake Garda, Italy. The fit provides a time scale, from which we determine the mixed layer depth. The obtained result shows a clear spatial pattern resembling the morphological features of the lake, with larger values (7.18± 0.3 m) in the deeper northwestern basin, and smaller values (3.18 ± 0.24 m) in the southern shallower basin. Such variations are confirmed by in-situ measurements in three monitoring points in the lake and connect to the first Empirical Orthogonal Function of satellite-derived LSWT and chlorophyll-a concentration. Evidence from our case study open a new perspective for interpreting lake-atmosphere interactions and confirm that remotely sensed variables, typically associated with properties of the surface layers, also carry information on the relevant spatial and temporal scales of mixed-layer processes. [ABSTRACT FROM AUTHOR]

Earthquake source parameters can be estimated using seismological observations, but the identification of the fault responsible is often complicated by location uncertainties and the inherent ambiguity between nodal planes. Satellite Interferometric Synthetic Aperture Radar (InSAR) can be used to observe ground deformation and model fault geometry but is limited by climate conditions (water vapour) and ground coverage (dense vegetation). In the tropics, the atmosphere is dynamic and most regions are densely vegetated, making detecting coseismic deformation challenging. Here, we perform a systematic inspection of coseismic interferograms from Sentinel-1 SAR images, to assess their suitability for detecting coseismic deformation in Costa Rica. Using data from the seismological network, we target seven earthquakes between 2016 and 2020 with depths |$\le \, 20$| km and magnitudes M w 5.3–6.2. For each event, we use the seismic parameters to compute line-of-sight displacements for ascending and descending geometries and for both nodal planes and generate 12- and 24-d coseismic interferograms where available. We obtain interferograms with coseismic displacement signals for three of the seven earthquakes. We invert the geodetic data to retrieve the earthquake source parameters but the lack of offshore geodetic coverage causes trade-offs between parameters and large uncertainties. The Jacó and Golfito earthquakes likely occurred on the subduction interface and the geodetic locations were 6–9 km closer to the coast than previous seismic estimates. The Burica earthquake occurred on a shallow steeply dipping thrust fault in the outer forearc. For the other earthquakes, no coseismic deformation was detected due to atmospheric noise or poor coherence. These results demonstrate the suitability of 12-d Sentinel-1 interferograms for monitoring shallow earthquakes of magnitude > M w 5.7 in Central America. This approach can be used to begin a surface deformation catalogue for the region, which will ultimately help improve the understanding of active deformation processes and improve hazard maps. [ABSTRACT FROM AUTHOR]

Imaging has been an important strategy for exploring space weather. The Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) is a joint Chinese Academy of Sciences (CAS) and European Space Agency (ESA) mission, aiming at studying the interaction between Earth's magnetosphere and solar wind near the subsolar point via soft X-ray imaging. As the boundary of Earth's magnetosphere, magnetopause is a significant detection target to mirror solar wind's change for the SMILE mission. In preparation for inverting three-dimensional magnetopause, we proposed an OESA-UNet model to detect the magnetopause position. The model obtains magnetopause with a U-shaped structure, in an end-to-end manner. Inspired by attention mechanisms, these blocks are integrated into ours. OESA-UNet captures low and high-level feature maps by adjusting the receptive field for precise localization. Adaptively pre-processing the image provides a prior for the network. Availability metrics are designed to determine whether it can serve three-dimensional inversion. Lastly, we provided ablation and comparison experiments by qualitative and quantitative analysis. Our recall, precision, and f1 score are 93.8%, 92.1%, and 92.9%, respectively, with an average angle deviation of 0.005 under the availability metrics. Results indicate that OESA-UNet outperforms other methods. It can better serve the purpose of magnetopause tracing from an X-ray image. [ABSTRACT FROM AUTHOR]

Non-parametric morphology statistics have been used for decades to classify galaxies into morphological types and identify mergers in an automated way. In this work, we assess how reliably we can identify galaxy post-mergers with non-parametric morphology statistics. Low-redshift (z ≲ 0.2), recent (t post-merger ≲ 200 Myr), and isolated (r > 100 kpc) post-merger galaxies are drawn from the IllustrisTNG100-1 cosmological simulation. Synthetic r -band images of the mergers are generated with SKIRT9 and degraded to various image qualities, adding observational effects such as sky noise and atmospheric blurring. We find that even in perfect quality imaging, the individual non-parametric morphology statistics fail to recover more than 55 per cent of the post-mergers, and that this number decreases precipitously with worsening image qualities. The realistic distributions of galaxy properties in IllustrisTNG allow us to show that merger samples assembled using individual morphology statistics are biased towards low-mass, high gas fraction, and high mass ratio. However, combining all of the morphology statistics together using either a linear discriminant analysis or random forest algorithm increases the completeness and purity of the identified merger samples and mitigates bias with various galaxy properties. For example, we show that in imaging similar to that of the 10-yr depth of the Legacy Survey of Space and Time, a random forest can identify 89 per cent of mergers with a false positive rate of 17 per cent. Finally, we conduct a detailed study of the effect of viewing angle on merger observability and find that there may be an upper limit to merger recovery due to the orientation of merger features with respect to the observer. [ABSTRACT FROM AUTHOR]

In this paper, we attempt to compare the Antarctic SSW of 2019 with the Arctic SSW of 2013. Despite being a minor SSW, the rare Antarctic event of September 2019 caused a significant enhancement of stratospheric temperature at high latitudes with more temperature deviation than the Arctic major SSW of 2013. On the basis of the duration and extent of stratospheric zonal mean zonal wind, the 2019 SSW is comparable to major SSWs. An exceptionally strong wave 1 planetary wave was observed during the 2019 SSW. We have investigated the response of the ionospheric total electron content (TEC) during both the SSWs over two low mid-latitude locations Okinawa and Darwin, which form magnetic conjugate pair. The extent of maximum ionospheric deviations during the 2019 SSW is almost comparable to the 2013 SSW in their respective hemispheres. We have observed mostly night time enhancement in TEC during the Arctic SSW, while daytime enhancement is noted during the Antarctic SSW. Hemispheric asymmetry is observed during both the SSWs. Minor geomagnetic storm has been noticed coinciding with each SSW during different phases of the warming. The SSW, solar flux, and storm contribution on the TEC have been calculated during both the SSWs, which shows clear contribution of SSW on the total TEC despite the presence of storm and during moderately high solar activity. We have also investigated the foF2 variations from ionosonde and IRI-16 at the same two locations. The peak electron density from ionosonde responds to the warming in a manner similar to TEC. The foF2 of IRI-16 exhibits overestimation during daytime and underestimation during nighttime on quiet days during low solar activity (2019), while during moderate solar activity (2013), it overestimates during morning hours. However, it can not predict the enhancements of foF2 during SSWs. [ABSTRACT FROM AUTHOR]

The Internet has become the fastest-growing way to sell luxury products. Purchase intention for luxury products in online stores has taken attention in the last few years since the sector has proliferated. The primary objective of this study is to examine the impact of various factors such as Product knowledge, Price consciousness, Perceived enjoyment, Perceived ease of usage, and usefulness on online luxury purchase intention in developing countries like Pakistan. Data was collected from 267 luxury fashion customers in Pakistan through an online questionnaire, and the results were analyzed using Smart PLS-SEM. In addition, the paper investigates the moderating effect of Perceived risk, Brand awareness and Web Atmospheric on the link between the Attitude and Online Luxury Purchase Intentions of the consumer to buy luxury fashion products online. The framework of this study is validated by structural equation modelling (SEM). The findings of this study show that perceived enjoyment, price consciousness, and Perceived ease of use significantly and positively impact online luxury purchase intention. Additionally, the findings indicated that brand awareness, perceived risk, and web atmospherics each intervened as moderators in the relationship between attitudes toward purchasing luxury products and online luxury purchase intentions. Product knowledge is not directly related to online purchase intention, but Attitude plays a mediating role in the relationship between product knowledge and online luxury buying purpose. In the context of luxury product intention, this study is one of the first to investigate the moderating effect that brand awareness, perceived risk, and web atmospherics play. It will help luxury brands develop the right tactics for selling luxury goods online in developing countries like Pakistan. [ABSTRACT FROM AUTHOR]

Detection of redshifted H  i 21-cm emission is a potential probe for investigating the Universe's first billion years. However, given the significantly brighter foreground, detecting 21-cm is observationally difficult. The Earth's ionosphere considerably distorts the signal at low frequencies by introducing directional-dependent effects. Here, for the first time, we report the use of Artificial Neural Networks (ANNs) to extract the global 21-cm signal characteristics from the composite all-sky averaged signal, including foreground and ionospheric effects such as refraction, absorption, and thermal emission from the ionosphere's F and D-layers. We assume a 'perfect' instrument and neglect instrumental calibration and beam effects. To model the ionospheric effect, we considered the static and time-varying ionospheric conditions for the mid-latitude region, where LOFAR is situated. In this work, we trained the ANN model for various situations using a synthetic set of the global 21-cm signals created by altering its parameter space based on the ' |$\rm \tanh$| ' parametrized model and the Accelerated Reionization Era Simulations (ARES) algorithm. The obtained result shows that the ANN model can extract the global signal parameters with an accuracy of |${\ge}96\ \hbox{per cent}$| in the final study when we include foreground and ionospheric effects. On the other hand, a similar ANN model can extract the signal parameters from the final prediction data set with an accuracy ranging from 97 to 98 per cent when considering more realistic sets of the global 21-cm signals based on physical models. [ABSTRACT FROM AUTHOR]

Opening a business in an existing building incurs lower energy and material consumption than constructing a new building. However, implementing this strategy in certain domains, such as retail and hospitality, requires operational changes. Despite an increasing focus on sustainability in these sectors, the primary objective remains creating appealing spaces for consumers, with companies frequently stipulating numerous requirements for their stores' new buildings. To promote sustainability, scholars have suggested that organizations, designers, and constructors find new uses for existing spaces. This study highlights how adapting buildings not built for commercial use can both promote sustainability and benefit new users. It explores a trend in European cities where existing buildings, often outside the usual commercial districts, are repurposed as cafés. Many of these projects provide attractive user destinations without requiring extensive renovations. Specifically, we investigate coffee shops in Copenhagen and the atmospheric characteristics that enhance their appeal. Based on observations and interviews, new and old atmospheric components and the atmospheres they jointly create are identified and divided into themes: capacious and accommodating environments, uniqueness, synergy between old and new, and appealing neighborhoods. Finally, user responses to these themes—such as lingering, revisiting, sharing narratives, influencing neighborhood development, and building communities—are described. [ABSTRACT FROM AUTHOR]

We study the sub-ionospheric VLF transmitter signals recorded by the Austrian Graz station in the year 2020. Those radio signals are known to propagate in the Earth-ionosphere waveguide between the ground and lower ionosphere. The Austrian Graz facility (geographic coordinates: 15.46°E, 47.03°N) can receive such sub-ionospheric transmitter signals, particularly those propagating above earthquake (EQ) regions in the southern part of Europe. We consider in this work the transmitter amplitude variations recorded a few weeks before the occurrence of two EQs in Croatia at a distance less than 200 km from Graz VLF facility. The selected EQs happened on 22 March 2020 and 29 December 2020, with magnitudes of Mw5.4 and Mw6.4, respectively, epicenters localized close to Zagreb (16.02°E, 45.87°N; 16.21°E, 45.42°N), and with focuses of depth smaller than 10 km. In our study we emphasize the anomaly fluctuations before/after the sunrise times, sunset times, and the cross-correlation of transmitter signals. We attempt to evaluate and to estimate the latitudinal and the longitudinal expansions of the ionospheric disturbances related to the seismic preparation areas. [ABSTRACT FROM AUTHOR]

The time evolution of the total number of free electrons in the Earth's ionosphere, i.e., the Global Electron Content (GEC), during more than two solar cycles is analyzed in this work. The GEC time series has been extracted from the Global Ionospheric Maps (GIMs) of Vertical Total Electron Content (VTEC) estimated by UPC-IonSAT with TOMION-v1 software from global GPS measurements since the end of 1996. A dual-layer voxel-based tomographic model solved with a forward Kalman scalar filter, from dual-frequency carrier GPS data only, provides the so-called UQRG GIM after VTEC kriging interpolation, with a resolution of 15 min in time, 5° in longitude and 2.5° in latitude. UQRG is one of the best behaving GIMs in the International GNSS Service (IGS).In this context, the potential application of the GEC spectrum evolution as a potential space weather index is discussed and demonstrated. [ABSTRACT FROM AUTHOR]

The signals received by very low-frequency/extremely low-frequency nonlinear receivers are frequently affected by intense atmospheric pulse noise stemming from thunderstorms and global lightning activity. Current noise processing algorithms designed for nonlinear channels within these frequency ranges, which are predicated on fractional p-order moment alpha stable distribution criteria (where 0 < p < α < 2, and p and α denote distinct characteristic indices of alpha stable distribution noise), are constrained by their reliance on limited p-order moment statistics. As a result, the performance of low-frequency nonlinear channel receivers experiences significant degradation when confronted with robust pulse noise interference (0 < p < α < 2). To tackle this challenge, the present study introduces a novel variable step robust mixed norm (RMN) adaptive filtering algorithm, designated as SVS-RMN, which is based on the Sigmoid function. Leveraging the nonlinearity of the Sigmoid function and building upon the power function Hammerstein nonlinear channel model, the algorithm aims to enhance the RMN algorithm by deriving new cost functions and adaptive iteration formulas. The performance of the proposed algorithm is evaluated in comparison to conventional RMN algorithms based on fractional low-order moment (FLOM) criteria (0 < p < 2), as well as other algorithms employing variable step sizes and either FLOM or radial basis function (RBF) criteria, across various intensities of pulse noise and mixed signal-to-noise ratios. The experimental results reveal the following: (1) The proposed algorithm effectively mitigates strong pulse noise interference and significantly enhances the tracking performance of the RMN algorithm compared to conventional RMN algorithms based on FLOM criteria. (2) In terms of computational efficiency, simplicity of structure, convergence speed, and stability, the proposed algorithm surpasses other algorithms based on FLOM or RBF criteria. [ABSTRACT FROM AUTHOR]

A deep learning based method is proposed to predict the urban air mobility (UAM) noise propagation in the urban environment. This method aims to efficiently estimate the noise impact of UAM flights on the complex urban area. The noise hemisphere was created via the comprehensive multirotor noise assessment framework to determine the noise level of UAM. The noise propagation to a randomly generated three-dimensional (3D) urban area was then calculated using the ray tracing method, including atmospheric attenuation and multiple reflections. 45 000 two-dimensional noise maps were used to train and evaluate the modified convolutional neural network. The results demonstrated high accuracy, with a root mean square error of only 2.56 dB compared to the ray tracing method, while reducing computation time by more than 1800 times. This model was applied to analyze the noise impact of various UAM flight conditions and landing scenarios at a vertiport. This deep learning approach is a fast method with adequate accuracy for predicting UAM noise impact in 3D urban environments. Also, it can inform the development of noise based strategies for UAM operations. [ABSTRACT FROM AUTHOR]

The article presents issues related to the reliability of power supply for electronic security devices in intelligent buildings. These systems function under varied environmental conditions and are exposed to external or internal natural and artificial interference; lightning, in particular. The authors conducted actual experimental tests of two surge arresters that are used to protect against a lightning impulse. As a result of conducted tests, followed by reliability and operational modelling, it was concluded that their use in internal connection structures increased the probability of a system staying in a state of full fitness. All of the deliberations included in the article enabled developing a new method for assessing the power supply continuity in electronic security systems of intelligent buildings, taking into account external natural interference. This method can also be applied to assess the power supply continuity in other electronic systems and devices. [ABSTRACT FROM AUTHOR]

Given the aggravation of environmental and water problems, there is a need to improve automated methods for extracting and monitoring water bodies in urban ecosystems. The problem of efficient and automated extraction of water bodies is becoming relevant given the large amount of data obtained from satellite systems. The object of study is water bodies that are automatically extracted from Sentinel-2 optical satellite images using machine learning methods. Objective. The goal of the work is to improve the efficiency of the process of extracting the boundaries of water bodies on digital optical satellite images by using machine learning methods. Method. The paper proposes an automated information technology for delineating the boundaries of water bodies on Sentinel-2 digital optical satellite images. The process includes eight stages, starting with data download and using topographic maps to obtain basic information about the study area. Then, the process involved data pre-processing, which included calibrating the images, removing atmospheric noise, and enhancing contrast. Next, the EfficientNet-B0 architecture is applied to identify water features, facilitating optimal network width scaling, depth, and image resolution. ResNet blocks compress and expand channels. It allows for optimal connectivity of large-scale and multi-channel links across layers. After that, the Regional Proposal Network defines regions of interest (ROI), and ROI alignment ensures data homogeneity. The Fully connected layer helps in segmenting the regions, and the Fully connected network creates binary masks for accurate identification of water bodies. The final step of the method is to analyze spatial and temporal changes in the images to identify differences, changes, and trends that may indicate specific phenomena or events. This approach allows automating and accurately identifying water features on satellite images using machine learning. Results. The implementation of the proposed technology is development through Python software development. An assessment of the technology’s accuracy, conducted through a comparative analysis with existing methods, such as water indices and K-means, confirms a high level of accuracy in the period from 2017 to 2023 (up to 98%). The Kappa coefficient, which considers the degree of consistency between the actual and predicted classification, confirms the stability and reliability of our approach, reaching a value of 0.96. Conclusions. The experiments confirm the effectiveness of the proposed automated information technology and allow us to recommend it for use in studies of changes in coastal areas, decision-making in the field of coastal resource management, and land use. Prospects for further research may include new methods that seasonal changes and provide robustness in the selection and mapping of water surfaces. [ABSTRACT FROM AUTHOR]

Objective: to verify the effects of atmospherics of a cool color (i.e., blue) and warm color (i.e., orange) on consumer behavior patterns when evaluating a product. Method: The sample consisted of 90 participants divided into three groups according to the background color of the site. Participants evaluated five products from a video call, and after that, they judged affirmatives. Time was also counted. Results: The blue group obtained the highest informative and utilitarian averages, and there was a high correlation between the informative and utilitarian items and purchase intention, showing that the conditions attached to a product's social status are directly linked to the economic and functional benefits it has and to the motivation to buy. The orange group performed the task with more difficulty, as they took a significant amount of time, so it became an aversive task. The linear regression analyses showed that factors related to the subject's self-esteem when purchasing a product are more determinant than the product's functionality. Originality: The general predictions of the BPM held up despite the color variation, but differences in the measures were still found when analyzing each product individually between the groups. Keywords: atmospherics; colors; Behavioral Perspective Model

Our understanding of the impact of climate change on forests is constrained by a lack of long-term phenological monitoring. It is generally carried out via (1) ground observations, (2) satellite-based remote sensing, and (3) near-surface remote sensing (e.g., PhenoCams, unmanned aerial vehicles, etc.). Ground-based observations are limited by space, time, funds, and human observer bias. Satellite-based phenological monitoring does not carry these limitations; however, it is generally associated with larger uncertainties due to atmospheric noise, land cover mixing, and the modifiable area unit problem. In this context, near-surface remote sensing technologies, e.g., PhenoCam, emerge as a promising alternative complementing ground and satellite-based observations. Ground-based phenological observations generally record the following key parameters: leaves (bud stage, mature, abscission), flowers (bud stage, anthesis, abscission), and fruit (bud stage, maturation, and abscission). This review suggests that most of these nine parameters can be recorded using PhenoCam with >90% accuracy. Currently, Phenocameras are situated in the US, Europe, and East Asia, with a stark paucity over Africa, South America, Central, South-East, and South Asia. There is a need to expand PhenoCam monitoring in underrepresented regions, especially in the tropics, to better understand global forest dynamics as well as the impact of global change on forest ecosystems. Here, we spotlight India and discuss the need for a new PhenoCam network covering the diversity of Indian forests and its possible applications in forest management at a local level. [ABSTRACT FROM AUTHOR]

Very Low Frequency (VLF) waves radiated from ground-based transmitters are crucial for long-distance communication and underwater navigation. These waves can reflect between the Earth's surface and the ionosphere for Earth–ionosphere waveguide propagation. Additionally, they can penetrate not only the ionosphere but also the magnetosphere, where they interact with high-energy particles in the radiation belt. Therefore, studying the spatial and temporal distribution of VLF radio signals holds significant importance. Such research enables us to understand the propagation characteristics of VLF signals, their interaction with radiation belt particles, and their response to space weather and lithospheric activity events. In this paper, we investigate the seasonal variations in the intensity of the Northwest Cape (NWC) transmitter (19.8 kHz) radio signals at satellite altitude and the displacement of the electric field's peak center. Our analysis is based on the nightly China Seismo-Electromagnetic Satellite (CSES) data from 2019 to 2021. The results reveal the following: (1) There is no significant seasonal variation in the electric field strength within a small area (2.5° radius) around the NWC transmitter. However, a clear seasonal variation in the electric field strength is observed within a larger area (15° radius), with higher strength during winter compared with summer. (2) The power spectral density of the electric field remains constant within the peak central area (approximately 1~2° radius), but it decays with distance outside this region, showing a north–south asymmetry. Moreover, the decay rate of the radiation electric field is slower in the northern direction than in the southern direction. (3) The center of the electric field moves northward from summer to winter and southward from winter to summer. (4) In winter, VLF waves radiated by the NWC transmitter may predominantly propagate by being ducted toward the conjugate hemisphere. [ABSTRACT FROM AUTHOR]

Collisions between electrons and neutral molecules are of special interest for the physics of the Earth's ionosphere, in particular, for determining the ionospheric conductivity and current systems in the lower ionosphere of the planet, as well as elucidating the role they play in attenuating radio waves propagating inside the D and E regions of the ionosphere. The effective collision frequency of electrons can be estimated from laboratory studies of electron mobility in atmospheric gases in combination with rocket measurements of temperature and particle density in the Earth's upper atmosphere, or it can be determined independently from analysis of radio occultation data. We have developed a method for reconstructing the vertical profiles of the absorption coefficient of decimeter (wavelength ~19 cm) radio waves by solving the inverse problem of signal absorption in the D and E regions of the Earth's ionosphere. Based on the analysis of radio occultation data from the FORMOSAT-3/COSMIC satellites, the altitude profiles of the absorption coefficient of decimeter (DM) radio waves in the planet's ionosphere during the geomagnetic storm on June 22–23, 2015, were determined. It is known that the absorption coefficient at a given fixed frequency is directly proportional to both the electron density and the collision frequency of electrons with ions and neutrals. Using the data on the vertical profiles of the absorption coefficient of DM radio waves and the electron density reconstructed from the analysis of FORMOSAT-3/COSMIC radio occultations, we estimated the effective collision frequency of electrons in the D and E regions of the Earth's high-latitude ionosphere. The practical significance of studying the frequency of electron collisions and the effects of radio wave absorption in the D and E regions of the planet's ionosphere is associated with maintaining the uninterrupted operation of space radio communication and navigation systems. [ABSTRACT FROM AUTHOR]

The effort to identify and comprehend potential earthquake-related phenomena shares a common goal: successful earthquake forecasting. Advancements in science and technology have made this goal multidisciplinary. Currently, possibly earthquake-related anomalies in the Vertical Total Electron Content (VTEC) of the Earth's ionosphere are being investigated. Global Navigation Satellite Systems (GNSS) can be used to calculate this ionospheric parameter. In this research work, GPS VTEC was calculated for periods between the years 2015 and 2019. The selection of this periods considered both seismically active and non-seismically areas in Mexico. The M w ≥ 5 earthquakes under study were registered by the National Seismological Service. Moreover, different geomagnetic storm and solar activity parameters, such as the geomagnetic equatorial Dst index and the F10.7 index, were analyzed. Additionally, the daily average and monthly mean number of sunspots (R, SSN, respectively) were included as a direct, long-term record of the development of the solar cycle. To the periods under study different statistical methods were applied, such as Mean-Square Error (MSE) and cross-correlation. The above aims to apply a machine learning technique capable of classifying between periods with seismic and non-seismic activity. The features were constructed using statistical data and results from the implemented analysis. Furthermore, Principal Component Analysis (PCA) was applied to reduce the feature vector dimensions, and accuracy scores were compute using k-fold cross-validation. The results from the Support Vector Machine (SVM) model indicated an accuracy of 88.9% for the training set, and an accuracy of 80% was obtained for the test set. One of the limitations of the current study was the sample size. However, the present initial approach for classifying seismic events from non-seismic periods using SVM demonstrated promising results when considering the indicated parameters and the days under study. [ABSTRACT FROM AUTHOR]

Climate variability has distinct spatial patterns with the strongest signal of sea surface temperature (SST) variance residing in the tropical Pacific. This interannual climate phenomenon, the El Niño-Southern Oscillation (ENSO), impacts weather patterns across the globe via atmospheric teleconnections. Pronounced SST variability, albeit of smaller amplitude, also exists in the other tropical basins as well as in the extratropical regions. To improve our physical understanding of internal climate variability across the global oceans, we here make the case for a conceptual model hierarchy that captures the essence of observed SST variability from subseasonal to decadal timescales. The building blocks consist of the classic stochastic climate model formulated by Klaus Hasselmann, a deterministic low-order model for ENSO variability, and the effect of the seasonal cycle on both of these models. This model hierarchy allows us to trace the impacts of seasonal processes on the statistics of observed and simulated climate variability. One of the important outcomes of ENSO's interaction with the seasonal cycle is the generation of a frequency cascade leading to deterministic climate variability on a wide range of timescales, including the near-annual ENSO Combination Mode. Using the aforementioned building blocks, we arrive at a succinct conceptual model that delineates ENSO's ubiquitous climate impacts and allows us to revisit ENSO's observed statistical relationships with other coherent spatio-temporal patterns of climate variability—so called empirical modes of variability. We demonstrate the importance of correctly accounting for different seasonal phasing in the linear growth/damping rates of different climate phenomena, as well as the seasonal phasing of ENSO teleconnections and of atmospheric noise forcings. We discuss how previously some of ENSO's relationships with other modes of variability have been misinterpreted due to non-intuitive seasonal cycle effects on both power spectra and lead/lag correlations. Furthermore, it is evident that ENSO's impacts on climate variability outside the tropical Pacific are oftentimes larger than previously recognized and that accurately accounting for them has important implications. For instance, it has been shown that improved seasonal prediction skill can be achieved in the Indian Ocean by fully accounting for ENSO's seasonally modulated and temporally integrated remote impacts. These results move us to refocus our attention to the tropical Pacific for understanding global patterns of climate variability and their predictability. [ABSTRACT FROM AUTHOR]

The atmospheric noise widely present in very-low-frequency and low-frequency (VLF/LF) communication systems is usually considered as a type of impulsive noise, which can degrade the performance of signal processing methods based on Gaussian noise. To solve the problems of difficult model training and complex noise parameter estimation under impulsive noise in other demodulation schemes of minimum shift keying (MSK) signals, we use one-dimensional convolutional neural networks (1D-CNNs) to replace the integrator and decision module in coherent demodulation instead of the entire demodulation process. Our scheme preserves the operations that are beneficial for neural network training in the conventional coherent demodulation process, and the use of neural networks avoids the estimation of noise parameters. To further improve the symbol error rate (SER) performance, we remove the symbol conversion before output generation and add the symbol conversion before MSK modulation. The simulation results show that our scheme with fewer parameters and less calculation has better SER performance than other neural network demodulators. Our scheme's SER performance exceeds the performance of the demodulation algorithm based on a myriad branch metric, whose performance is very close to the maximum likelihood (ML) performance. And, our scheme does not require complex noise parameter estimation. [ABSTRACT FROM AUTHOR]

A new observational phenomenon, named Simultaneous Global Ionospheric Density Disturbance (SGD), is identified in GNSS total electron content (TEC) data during periods of three typical geospace disturbances: a Coronal Mass Ejection‐driven severe disturbance event, a high‐speed stream event, and a minor disturbance day with a maximum Kp of 4. SGDs occur frequently on dayside and dawn sectors, with a ∼1% TEC increase. Notably, SGDs can occur under minor solar‐geomagnetic disturbances. SGDs are likely caused by penetration electric fields (PEFs) of solar‐geomagnetic origin, as they are associated with Bz southward, increased auroral AL/AU, and solar wind pressure enhancements. These findings offer new insights into the nature of PEFs and their ionospheric impact while confirming some key earlier results obtained through alternative methods. Importantly, the accessibility of extensive GNSS networks, with at least 6,000 globally distributed receivers for ionospheric research, means that rich PEF information can be acquired, offering researchers numerous opportunities to investigate geospace electrodynamics. Plain Language Summary: Electric fields of solar wind and geomagnetic disturbance origin can penetrate into the low latitude upper atmosphere, influencing the ionospheric dynamics and electron density variations. This study employs a new method that utilizes global and continuous GNSS total electron content (TEC) observations to investigate the electric field effects. The analysis focuses on three geospace disturbance events of different intensities and solar‐terrestrial conditions. The study identifies a novel phenomenon named Simultaneous Global Ionospheric Density Disturbance (SGD), primarily occurring on the sunlit portion of the Earth's ionosphere and also near dawn hours with 1% or larger amplitudes of the background TEC, or a few tenths of a TEC unit (1016 m3). The remarkable global extent of ionospheric responses to minor solar‐geomagnetic conditions is noteworthy. The solar wind magnetic field directed southward is highly correlated with most SGDs, lasting for up to 30 min. The findings present an effective approach for continuously monitoring electric field penetration and ionospheric impacts, leading to an improved understanding of space weather and its technological implications. Key Points: Simultaneous global ionospheric disturbances (SGDs) are often observed even during minor solar and geomagnetic disturbancesSGDs occur predominately on dayside and are related to penetration electric fields (PEFs) of solar wind and geomagnetic disturbance originGlobal GNSS networks offer a novel and effective technique for continuous PEF monitoring, providing rich data sets for further study [ABSTRACT FROM AUTHOR]

Tweek signals are a special type of VLF (very low frequency) pulse, originally produced by lightning discharge, which becomes dispersive after repetitive bounces within the waveguide between the Earth's surface and lower ionosphere. As such, tweek signals carry critical information about the region near the reflection height of the VLF waves, namely the D-region ionosphere. Although tweek measurements have been widely utilized in studies of the D-region ionosphere and lightning discharge, few statistical studies have been conducted, mainly due to the difficulty of manually identifying tweek signals from the enormous amount of VLF data with heavy noise. Considering the importance of tweek signals and the lack of a high-precision detection model, in this study, we propose a method to automatically and accurately pick out tweek signals from VLF measurements. This method is explicitly developed based on the you only look once (YOLO) model and a post-tracing process. Using a total of 2495 randomly selected VLF spectrogram images as the testing set, we evaluated the performance of this method. The precision and recall are found to be 92.0% and 89.2% for the first-order mode, and 97.5% and 86.7% for the first-two-order mode tweek, respectively. The time needed to process 10-s VLF measurements with a cadence of 4 μs is only 6.5 s, allowing for identifying the tweek signals from continuous VLF measurements in real time. Therefore, this method represents a reliable means to automatically detect tweek signals and enables the opportunity to statistically investigate the D-region ionosphere and lightning discharge via these signals. [ABSTRACT FROM AUTHOR]

High-quality satellite cloud images are of great significance for weather diagnosis and prediction. However, many of these images are often degraded due to relative motion, atmospheric turbulence, instrument noise, and other factors. In the satellite imaging process, the degradation also cannot be completely corrected. Therefore, it is necessary to further improve the satellite cloud image quality for real applications. In this study, we propose an unsupervised image restoration model with a two-stage network, in which the first stage, named the Prior-Knowledge-based Generative Adversarial Network (PKKernelGAN), aims to learn the blur kernel, and the second stage, named the Zero-Shot Deep Residual Network (ZSResNet), aims to improve the image quality. In PKKernelGAN, we propose a satellite cloud imaging loss function, which is a novel objective function that brings optimization of a generative model into the prior-knowledge domain. In ZSResNet, we build a dataset which contains the original satellite cloud image as high-quality images (HQ) paired with low-quality images (LQ) generated by the blur kernel learning from PKKernelGAN. The above innovations lead to a more efficient local structure in satellite cloud image restoration. The original dataset of our experiment is from the Sunflower 8 satellite provided by the Japan Meteorological Agency. This dataset is divided into training and testing sets to train and test PKKernelGAN. Then, ZSResNet is trained by the "LQ–HQ" image pairs generated by PKKernelGAN. Compared with other supervised and unsupervised deep learning models for image restoration, our model has a better performance. Extensive experiments have demonstrated that our proposed model can achieve better performance on different datasets. [ABSTRACT FROM AUTHOR]

We evaluated the detection efficiency and location accuracy of lightning discharges in Japan using Blitzortung.org, a volunteer-based network for locating lightning discharges from sferics measured by very low frequency (VLF) electromagnetic receivers that have been deployed worldwide in recent years. A comparison of the flash rate (the detected lightning rate per area and period) from Blitzortung.org with that from the satellite-based OTD/LIS and the ground-based World Wide Lightning Location Network (WWLLN) observations showed that Blitzortung.org clearly observed intense lightning activity in and around the Kanto area, including Tokyo, in summer, which is typical of Japanese lightning activity. However, it did not clearly observe lightning activity in and around the Nansei Islands, including Okinawa. Conversely, Blitzortung.org observed winter lightning activity in the Hokuriku area and off the Kanto. In addition, event studies have compared the detection efficiency and location accuracy of Blitzortung.org with those of the Japanese Lightning Location Network (JLDN) to infer their absolute values. The latest detection efficiency of Blitzortung.org in the Kanto area was estimated at roughly 90%. The mean location accuracy was estimated at up to 5.6 km. [ABSTRACT FROM AUTHOR]

The bottom part of the Earth's ionosphere is the so-called D region, which is typically less dense than the upper regions. Despite the comparably lower electron density, the ionization state of the D region has a significant influence on signal absorption for propagating lower to medium radio frequencies. We present local noon climatologies of electron densities in the upper middle atmosphere (50–90 km) at high latitudes as observed by an active radar experiment. The radar measurements cover 9 years (2014–2022) from the solar maximum of cycle 24 to the beginning of cycle 25. Reliable electron densities are derived by employing signal processing, applying interferometry methods, and applying the Faraday-International Reference Ionosphere (FIRI) model. For all years a consistent spring–fall asymmetry of the electron density pattern with a gradual increase during summer as well as a sharp decrease at the beginning of October was found. These findings are consistent with very low frequency (VLF) studies showing equivalent signatures for nearby propagation paths. It is suggested that the meridional circulation associated with downwelling in winter could cause enhanced electron densities through NO transport. However, this mechanism can not explain the reduction in electron density in early October. [ABSTRACT FROM AUTHOR]

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