Optical Phenomena Research Articles

Chinese Snowscape As a Phenomenon

With unique aesthetic value, snowy landscapes are one of the most important subjects in Chinese painting. Due to their purity, elegance, serenity, simplicity and cold charm, snow scenes have been the focus of artists and scholars from long ago. The artistic features and cultural connotations of Chinese snow paintings have attracted the attention of many Chinese and Russian scholars. However, there is no typological study of snow landscape in their works. In this regard, the authors consider it necessary to actualize this topic. The article provides historical information about the study of snowy landscape, the history of Chinese painting masters’ appeal to this theme. The classification of snow painting depending on seasons, geographical regions of China, atmospheric phenomena, and themes is offered. The authors suggest their definition of the concept of “Chinese snow landscape” and the author’s classification of the content of snow paintings. The paper applies a typological approach to the analysis of “snow” scenes, and examines the development of traditional Chinese aesthetic contexts from ancient artistic codes to contemporary theories. Outstanding examples of snow landscape painting are presented. Snow painting is an important medium of Chinese cultural heritage that demonstrates the diversity and richness of Chinese culture. The given overview of China’s snow scene painting as a phenomenon in the work of Chinese artists can serve as a basis for further in–depth studies.

Investigating the spatio–temporal characteristics of lower atmospheric ducts across the China seas by performing a long–term simulation using the WRF model

In this work, a diagnostic scheme for lower atmospheric ducts was established based on the Weather Research and Forecasting (WRF) model. More specifically, a 10-year simulation test was conducted for the China seas to investigate the spatio-temporal characteristics of the lower atmospheric ducts phenomenon. Compared with the sounding data, the long-term simulations showed a high temporal correlation and the root mean square error of the modified atmospheric refractivity remained between 4 M and 7 M. Based on the simulations, significant regional differences in the occurrence probability of lower atmospheric ducts were detected from south to north. Among them, the surface ducts near the sea surface exhibited the highest occurrence probability, with higher probabilities being recorded in autumn and winter, and the probability gradually increased with the decreasing latitude. The spatio-temporal characteristics of duct height, thickness, and strength were generally consistent. In the seas at mid-latitudes, strong ducts mostly occurred in the spring and autumn, with the single-layer ducts being predominant and the first layer duct showing stronger characteristics than the second layer. In the lower latitude regions, the situation was exactly the opposite. The first duct layer, which existed throughout the year, exhibited weaker characteristics with less pronounced seasonal variations. On the other hand, the second duct layer demonstrated stronger features.

Non-extensive (Tsallis) q-statistics and auroral glow

It is well-known that the auroral region of the magnetosphere–ionosphere interaction is an open, nonlinear dissipative system far from the equilibrium state. It is in this region that auroras are regularly observed, demonstrating not only a wide variety of dynamic forms but also a wide range of temporal and spatial scales. Due to the memory effects and fractal properties of auroral plasma, as well as strong correlations between its individual parts, the dynamics of the high-latitude system are primarily determined by long-range electromagnetic forces. Therefore, it is expected that non-additive and non-extensive thermodynamic principles may characterize their macroscopic behavior. In the presented study, it is shown that pulsating auroras exhibit non-extensive properties and can be described by q-statistics. The correlation of the parameter q with traditional indicators of nonlinear dynamics, such as the flatness coefficient, standard deviation, and scaling index, has been investigated. Small-scale changes are superimposed on large-scale processes characterized by a low correlation dimension, and this leads to an increase in the correlation dimension during geomagnetic activity, which approximately coincides with increased values of q. It is concluded that the q-statistics (Tsallis distribution) can be used to analyze optical phenomena in the auroral region.

Probing atmospheric waves and the middle atmosphere dynamics using infrasound

Infrasound is defined as pressure fluctuations with frequencies between acoustic cut-off (5 min) and human-hearing frequency threshold of sound (16 Hz). Low-frequent infrasonic waves can travel long distances, ranging from hundreds to thousands of kilometres. The middle atmosphere dynamics mainly control the presence of atmospheric waveguides where energy transmission loss is low. These properties are utilized to record atmospheric explosions at highly sensitive pressure sensors (micro-barometers). A global network of 60 infrasound stations was designed as part of the International Monitoring System (IMS) for the Comprehensive Nuclear-Test-Ban Treaty. IMS infrasound stations can record small pressure fluctuations of a few millipascals, which can originate from numerous atmospheric infrasound sources, including meteorological phenomena. In this study, the capability of IMS infrasound arrays for capturing a broad spectrum of atmospheric wave phenomena is highlighted; for instance, mountain-associated infrasonic waves show features that seem to be correlated with orographic gravity waves. Moreover, the interaction of ocean waves produces quasi-continuous infrasound, so-called microbaroms. Infrasonic signatures from microbaroms can be used for probing the middle atmosphere dynamics and assessing atmospheric circulation models. For opening the unique global infrasound network of the IMS for meteorological applications, we also present open-access BGR infrasound data products of two decades and highlight selected case studies.

A comparative study of resistivity models for simulations of magnetic reconnection in the solar atmosphere

Context. Plasmoid-mediated reconnection plays a fundamental role in different solar atmospheric phenomena. Numerical reproduction of this process is therefore essential for developing robust solar models. Aims. Our goal is to assess plasmoid-mediated reconnection across various numerical resistivity models in order to investigate how plasmoid numbers and reconnection rates depend on the Lundquist number. Methods. We used the Bifrost code to drive magnetic reconnection in a 2D coronal fan-spine topology, carrying out a parametric study of several experiments with different numerical resolution and resistivity models. We employed three anomalous resistivity models: (1) the original hyper-diffusion from Bifrost, (2) a resistivity proportional to current density, and (3) a resistivity quadratically proportional to electron drift velocity. For comparisons, experiments with uniform resistivity were also run. Results. Plasmoid-mediated reconnection is obtained in most of the experiments. With uniform resistivity, increasing the resolution reveals higher plasmoid frequency with weaker scaling to the Lundquist number, obtaining 7.9–12 plasmoids per minute for SL ∈ [1.8 × 104, 2.6 × 105] with a scaling of SL0.210 in the highest-resolution resistivity cases, transcending into Petschek reconnection in the high-SL limit (where the diffusive effects of the resistivity become small compared to the non-uniform viscosity) and Sweet-Parker reconnection in the low-SL limit. Anomalous resistivity leads to similar results even with lower resolution. The drift-velocity-dependent resistivity excellently reproduces Petschek reconnection for any Lundquist number, and similar results are seen with resistivity proportional to current-density though with slightly lower reconnection rates and plasmoid numbers. Among the different resistivity models applied on the given numerical resolution, the hyper-diffusion model reproduced plasmoid characteristics in closest resemblance to those obtained with uniform resistivity at a significantly higher resolution.

Modeling of fluctuations in dynamical optoelectronic device simulations within a Maxwell-density matrix Langevin approach

We present a full-wave Maxwell-density matrix simulation tool including c-number stochastic noise terms for the modeling of the spatiotemporal dynamics in active photonic devices, such as quantum cascade lasers and quantum dot structures. The coherent light–matter interaction in such devices plays an important role in the generation of frequency combs and other nonlinear and non-classical optical phenomena. Since the emergence of nonlinear and non-classical features is directly linked to the noise properties, detailed simulations of the noise characteristics are required for the development of low-noise quantum optoelectronic sources. Our semiclassical simulation framework is based on the Lindblad equation for the electron dynamics, coupled with Maxwell’s equations for optical propagation in the laser waveguide. Fluctuations arising from the interactions of the optical field and quantum system with their reservoirs are treated within the quantum Langevin theory. Here, the fluctuations are included by adding stochastic c-number terms to the Maxwell-density matrix equations. The implementation in the mbsolve dynamic simulation framework is publicly available.

How can acoustic measurements help us understand severe weather phenomena such as lightning?

Lightning is an indicator for monitoring severe weather events like heavy precipitation, flash floods, hail… It is also a climate variable for monitoring global warming. Lightning emits light, sound and radioelectromagnetic fields, allowing remote detection and analysis. Acoustic measurements can be used to, for instance, track global warming over more than 70 years via the keraunic level (the number of thunderstorm days per year in a specific location), or detect and monitor severe weather events such as cyclones, e.g. the Medicanes, or characterize optical phenomena such as sprites associated with violent thunderstorms, or highlight temporal changes in the upper layers of the atmosphere, such as the semi-annual oscillation of stratospheric winds in tropical zones. At closer ranges, less than 30 km, acoustic network measurements complement electromagnetic observations to reconstruct the 3D structure of cloud-to-ground and intra-cloud discharges. Recently, it has been shown possible to provide the 3D structure of acoustic power within the lightning source. From flash to flash, this power shows four orders of magnitude variations, similarly to electromagnetism or optics. Moreover, it outlines that large variations of power also exist even within a lightning flash, reflecting heterogeneities in conductivity within the discharge.

Harmonizing Plasmonic and Photonic Effects to Boost Photocatalytic H2 Production over 550 mmol ⋅ h−1 ⋅ gcat−1

AbstractIntegrating plasmonic nanoparticles with photonic crystals holds immense potential to enhance green hydrogen photosynthesis by amplifying localized electromagnetic field through generating surface plasmons and slow photons. Current plasmonic photonic designs primarily employ semiconductor‐based structural backbone deposited with plasmonic nanoparticles. However, the competition between various optical phenomena in these ensembles hinders effective field enhancement rather than facilitating it. This limitation creates a formidable performance bottleneck that retards hydrogen evolution. Herein, we enhance plasmonic catalysis for efficient hydrogen evolution by effectively harmonizing plasmonic and photonic effects. This is achieved by using inert SiO2 opal as a non‐photoabsorbing photonic framework. By aligning the excitation wavelengths of surface plasmons and slow photons, our optimized plasmonic photonic crystals demonstrates a remarkable H2 evolution rate of 560 mmol h−1 gAg−1, surpassing bare plasmonic Ag nanoparticles by >106‐fold and other high‐performance photocatalytic designs by 280‐fold. Mechanistic studies highlight the pivotal role of the non‐photoabsorbing photonic backbone in facilitating effective light confinement through the photonic effect. This in turn boosts the plasmonic field for enhanced photocatalytic H2 evolution, even without needing additional co‐catalysts. Our work offers valuable insights for future design of electromagnetically hot plasmonic catalysts to achieve efficient light‐to‐chemical transformations in diverse energy, chemical, and environmental applications.

Harmonizing Plasmonic and Photonic Effects to Boost Photocatalytic H2 Production over 550 mmol ⋅ h-1 ⋅ gcat -1.

Integrating plasmonic nanoparticles with photonic crystals holds immense potential to enhance green hydrogen photosynthesis by amplifying localized electromagnetic field through generating surface plasmons and slow photons. Current plasmonic photonic designs primarily employ semiconductor-based structural backbone deposited with plasmonic nanoparticles. However, the competition between various optical phenomena in these ensembles hinders effective field enhancement rather than facilitating it. This limitation creates a formidable performance bottleneck that retards hydrogen evolution. Herein, we enhance plasmonic catalysis for efficient hydrogen evolution by effectively harmonizing plasmonic and photonic effects. This is achieved by using inert SiO2 opal as a non-photoabsorbing photonic framework. By aligning the excitation wavelengths of surface plasmons and slow photons, our optimized plasmonic photonic crystals demonstrates a remarkable H2 evolution rate of 560 mmol h-1 gAg -1, surpassing bare plasmonic Ag nanoparticles by >106-fold and other high-performance photocatalytic designs by 280-fold. Mechanistic studies highlight the pivotal role of the non-photoabsorbing photonic backbone in facilitating effective light confinement through the photonic effect. This in turn boosts the plasmonic field for enhanced photocatalytic H2 evolution, even without needing additional co-catalysts. Our work offers valuable insights for future design of electromagnetically hot plasmonic catalysts to achieve efficient light-to-chemical transformations in diverse energy, chemical, and environmental applications.

APPLICATION OF WEATHER PREDICTION TOOL USING ARTIFICIAL NEURAL NETWORK

In the modern world, weather forecasting is a vital occurrence. Even if weather prediction is entirely automated and made possible by technologies like Weather Research & Forecasting (WRF), Advanced Research WRF (ARW), and Weather Processing System (WPS), it's still a difficult and interesting topic because forecasts aren't always accurate. The process of forecasting weather is complex, dynamic, high-dimensional, and ongoing since it incorporates a wide range of atmospheric phenomena. The intricacy of the criteria needed to forecast the weather means that even short-term forecasts are problematic. Artificial neural networks are perfect for weather forecasting because of their ability to learn from past data in addition to analyzing it and generate forecasts for the future. It is possible to simplify weather forecasting. By using the data gathered at a certain station over a predetermined period of time to train artificial neural networks (ANN) with back propagation for supervised learning. They are used to forecast the weather after the model has been trained. The model is revealed to anticipate the values as unknown values as an experimental technique. The results show promise and encourage us to keep working toward this objective.

Complex far fields and optical singularities due to propagation beyond tight focusing: combined effects of wavefront curvature and aperture diffraction

All optical systems, which involve the collimation of a reflected, transmitted or scattered wave subsequent to tight focusing, are subject to two kinds of deviations. One is the wavefront curvature due to inaccurate focal placement of the interface or scatterer particle under consideration, and the other is the diffraction caused by the finite lens aperture. In the present paper we explore these phenomena in detail by considering a rigorous simulated model and an appropriate experimental setup. We hence demonstrate the complicated intensity profiles and optical singularity characteristics of the observed far field. Then we describe ways to minimize these deviations in a general experiment. But more importantly, our analysis proves that these deviations by themselves are significant optical phenomena of fundamental interest. The observed complex field profiles have similarities to standard diffraction-limited tight focal fields, though our field detection is different from the standard schemes. This indicates the relevance of these complex fields to a larger class of systems involving wavefront curvature and aperture diffraction. The detailed analysis and results of the present paper already serve as core explorations of these optical phenomena; and we also suggest future research directions where these system aspects can be purposefully created and explored further.

State-of-Charge Trajectory Planning for Low-Altitude Solar-Powered Convertible UAV by Driven Modes

The conversion efficiency of solar energy and the capacity of energy storage batteries limit the development of low-altitude solar-powered aircrafts in the face of challenging meteorological phenomena in the lower atmosphere. In this paper, the energy planning problem of solar-power convertible unmanned aerial vehicles (SCUAVs) is studied, and a degressive state-of-charge (SOC) trajectory planning method with energy management strategy (EMS) is proposed. The SOC trajectory planning strategy is divided into four stages driven by three modes, which achieves the energy cycle of SCUAV’s long-endurance cruise and multiple hovers without the need to fully charge the battery SOC. The EMS is applied to control the output of solar cell/battery and power distribution for each stage according to three modes. A prediction model based on wavelet transform (WT), long short-term memory (LSTM) networks and autoregressive integrated moving average (ARIMA) is proposed for the weather forecast in the low altitude, where solar irradiance is used for the prediction of solar input power, and the wind and its inflow direction take into account the multi-mode power prediction. Numerical and simulation results indicate that the effectiveness of the proposed SOC trajectory planning method has a positive impact on low-altitude solar-powered aircrafts.

Directional spontaneous emission in photonic crystal slabs

Abstract Spontaneous emission is one of the most fundamental out-of-equilibrium processes in which an excited quantum emitter relaxes to the ground state due to quantum fluctuations. In this process, a photon is emitted that can interact with other nearby emitters and establish quantum correlations between them, e.g., via super and subradiance effects. One way to modify these photon-mediated interactions is to alter the dipole radiation patterns of the emitter, e.g., by placing photonic crystals near them. One recent example is the generation of strong directional emission patterns – key to enhancing super and subradiance effects – in two dimensions by employing photonic crystals with band structures characterized by linear isofrequency contours and saddle points. However, these studies have predominantly used oversimplified toy models, overlooking the electromagnetic field’s intricacies in actual materials, including aspects like geometrical dependencies, emitter positions, and polarization. Our study delves into the interaction between these directional emission patterns and the variables mentioned above, revealing the untapped potential to fine-tune collective quantum optical phenomena.

An investigation of the on-board microwave radiometer of satellite Altimetry for studying the atmosphere variability

Since its first launching, the ability of satellite Altimetry in providing reliable and accurate ocean geophysical information of the sea surface height (SSH), significant wave height (SWH), and wind speed has been proven by numerous researchers, as it was designed for observing the ocean dynamics through nadir range measurement between satellite and the sea surface. However, to achieve high level accuracy, environmental and geophysical effects on the range measurement must be accurately determined and corrected, particularly the effects from the atmospheric water vapor which can divert altimeter range up to 3–45 cm. Thus, satellite Altimetry is originally equipped with the on-board microwave radiometer to measure the water vapour content for correcting the range measurement. To our knowledge, no one has attempted to apply the on-board radiometer for atmospheric studies. In this present work, we attempt to optimize the on-board radiometer data for studying the atmosphere variability due to the El Niño–Southern Oscillation (ENSO) phenomena. We convert the on-board water vapor data into the precipitable water vapour (PWV), and we then investigate whether the derived PWV can capture the variability of ocean–atmosphere phenomena due to ENSO as accurate as the conventional Altimetry-derived sea level anomaly (SLA). Based on our analysis using the empirical orthogonal function (EOF), the results show convincing argument that Altimetry-derived PWV are reliable in examining the atmospheric fluctuation as the correlation of its primary principal component time series (PC1) with Oceanic Nino Index (ONI) is higher (0.87) than SLA (0.80). These results may reinforce the confidence in the ability of satellite Altimetry for ocean–atmospheric studies.Graphical

Chiral and flat-band magnetic quasiparticles in ferromagnetic and metallic kagome layers.

Magnetic kagome metals are a promising platform to develop unique quantum transport and optical phenomena caused by the interplay between topological electronic bands, strong correlations, and magnetic order. This interplay may result in exotic quasiparticles that describe the coupled electronic and spin excitations on the frustrated kagome lattice. Here, we observe novel elementary magnetic excitations within the ferromagnetic Mn kagome layers in TbMn6Sn6 using inelastic neutron scattering. We observe sharp, collective acoustic magnons and identify flat-band magnons that are localized to a hexagonal plaquette due to the special geometry of the kagome layer. Surprisingly, we observe another type of elementary magnetic excitation; a chiral magnetic quasiparticle that is also localized on a hexagonal plaquette. The short lifetime of localized flat-band and chiral quasiparticles suggest that they are hybrid excitations that decay into electronic states.

Angle-Resolved Optical Imaging of Interlayer Rotations in Twisted Bilayer Graphene.

Twisted bilayer graphene (TBG) is a prototypical layered material whose properties are strongly correlated to interlayer coupling. The two stacked graphene layers with distinct orientations are investigated to generate peculiar optical and electronic phenomena. Thus, the rapid, reliable, and nondestructive twist angle identification technique is of essential importance. Here, we integrated the white light reflection spectra (WLRS), the Raman spectroscopy, and the transmission electron microscope (TEM) to propose a facile RGB optical imaging technique that identified the twist angle of the TBG in a large area intuitively with high efficiency. The RGB technique established a robust correlation between the interlayer rotation angle and the contrast difference in the RGB color channels of a standard optical image. The angle-resolved optical behavior is attributed to the absorption resonance matching with the separation of van Hove singularities in the density of states of the TBG. Our study thus developed a route to identify the rotation angle of stacked bilayer graphene by means of a straightforward optical method, which can be further applied in other stacked van der Waals layered materials.

In Search of Atmospheres: Directions, Methods, Perspectives (An Interview With Tonino Griffero)

The interview touches upon the diversity of traditions (including national ones) of atmospheric research, on the correlation of the concepts of (affective) atmosphere with the concepts of mood (_Stimmung_) and _ambiance_, on research methods and ways of conceptualising atmospheric phenomena by the German phenomenologist Hermann Schmitz and the specialist in aesthetics Gernot Böhme, and, finally, on the prospects of using the methods and approaches under consideration in social sciences.

Versatile photonic molecule switch in multimode microresonators

Harnessing optical supermode interaction to construct artificial photonic molecules has uncovered a series of fundamental optical phenomena analogous to atomic physics. Previously, the distinct energy levels and interactions in such two-level systems were provided by coupled microresonators. The reconfigurability is limited, as they often require delicate external field stimuli or mechanically altering the geometric factors. These highly specific approaches also limit potential applications. Here, we propose a versatile on-chip photonic molecule in a multimode microring, utilizing a flexible regulation methodology to dynamically control the existence and interaction strength of spatial modes. The transition between single/multi-mode states enables the “switched-off/on” functionality of the photonic molecule, supporting wider generalized applications scenarios. In particular, “switched-on” state shows flexible and multidimensional mode splitting control in aspects of both coupling strength and phase difference, equivalent to the a.c. and d.c. Stark effect. “Switched-off” state allows for perfect low-loss single-mode transition (Qi ~ 10 million) under an ultra-compact bend size (FSR ~ 115 GHz) in a foundry-based silicon microring. It breaks the stereotyped image of the FSR-Q factor trade-off, enabling ultra-wideband and high-resolution millimeter-wave photonic operations. Our demonstration provides a flexible and portable solution for the integrated photonic molecule system, extending its research scope from fundamental physics to real-world applications such as nonlinear optical signal processing and sixth-generation wireless communication.

A Multichannel-Based Deep Learning Framework for Ocean SAR Scene Classification

High-resolution synthetic aperture radars (SARs) are becoming an indispensable environmental monitoring system to capture the important geophysical phenomena on the earth and sea surface. However, there is a lack of comprehensive models that can orchestrate such large-scale datasets from numerous satellite missions such as GaoFen-3 and Sentinel-1. In addition, these SAR images of different ocean scenes need to convey a variety of high-level classification features in oceanic and atmospheric phenomena. In this study, we propose a multichannel neural network (MCNN) that supports oceanic SAR scene classification for limited oceanic data samples according to multi-feature fusion, data augmentation, and multichannel feature extraction. To exploit the multichannel semantics of SAR scenes, the multi-feature fusion module effectively combines and reshapes the spatiotemporal SAR images to preserve their structural properties. This fine-grained feature augmentation policy is extended to improve the data quality so that the classification model is less vulnerable to both small- and large-scale data. The multichannel feature extraction also aggregates different oceanic features convolutionally extracted from ocean SAR scenes to improve the classification accuracy of oceanic phenomena with different scales. Through extensive experimental analysis, our MCNN framework has demonstrated a commendable classification performance, achieving an average precision rate of 96%, an average recall rate of 95%, and an average F-score of 95% across ten distinct oceanic phenomena. Notably, it surpasses two state-of-the-art classification techniques, namely, AlexNet and CMwv, by margins of 23.7% and 18.3%, respectively.

Extreme events of snow grain size increase in East Antarctica and their relationship with meteorological conditions

Abstract. This study explores the seasonal variations in snow grain size on the East Antarctic Plateau, where dry metamorphism occurs, by using microwave radiometer observations from 2000 to 2022. Local meteorological conditions and large-scale atmospheric phenomena have been considered in order to explain some peculiar changes in the snow grains. We find that the highest ice divide is the region with the largest grain size in the summer, mainly because the wind speed is low. Moreover, some extreme grain size values with respect to the average (over +3σ) were identified. In these cases, the ERA5 reanalysis revealed a high-pressure blocking close to the onsets of the summer increase in the grain size. It channels moisture intrusions from the mid-latitudes, through atmospheric rivers that cause major snowfall events over the plateau. If conditions of weak wind and low temperature occur during the following weeks, dry snow metamorphism is facilitated, leading to grain growth. This determines anomalous high maximums of the snow grain size at the end of summer. These phenomena confirm the importance of moisture intrusion events in East Antarctica and their impact on the physical properties of the ice sheet surface, with a co-occurrence of atmospheric rivers and seasonal changes in the grain size with a significance of over 95 %.