Abstract We introduce a novel approach for detecting gravitational waves through their influence on the shape of resolved astronomical objects. This method, complementary to pulsar timing arrays and astrometric techniques, explores the time-dependent distortions caused by gravitational waves on the shapes of celestial bodies, such as galaxies or any resolved extended object. By developing a formalism based on that adopted in the analysis of weak lensing effects, we derive the response functions for gravitational wave-induced distortions and compute their angular correlation functions. Our results highlight the sensitivity of these distortions to the lowest frequencies of the gravitational wave spectrum and demonstrate how they produce distinct angular correlation signatures, including null and polarisation-sensitive correlations. These findings pave the way for future high-resolution surveys to exploit this novel observable, potentially offering new insights into the stochastic gravitational wave background and cosmological models.
Abstract This study derived contact angles for fifteen types of pollens, nine types of fungi, ten types of bacteria, one type of diatom, and twelve types of mineral dust for use in the parameterization of immersion freezing based on the classical nucleation theory (CNT). Our approach is to interpret freezing temperature measurement results with the stochastic nucleation concept. In this way, the abundant freezing temperature data available in the literature can be converted to contact angles that needed in the CNT parameterization for a variety of INPs. The derived contact angles compared well with values independently obtained in earlier studies based on a pure-CNT approach using laboratory nucleation rate data. The uncertainties in contact angle calculation associated with the definition of onset nucleation rate, the activation energy, and the ice-nuclei size are estimated to be about ± 1–2°, ± 1–5°, and ± 1–2°, respectively, among different ice-nucleating particles.
Daniel C. Anderson, Bryan N. Duncan, Junhua Liu
et al.
Abstract Despite its importance for the global oxidative capacity, spatially resolved trends and variability of the hydroxyl radical (OH) are poorly constrained. We demonstrate the utility of a tropospheric column OH (TCOH) product, created from machine learning and satellite proxy data, in determining the spatial variability in trends of tropical OH over the oceans during September through November. While OH increases domain‐wide by 2.1%/decade from 2005–2019, we find significant spatial heterogeneity in regional trends, with decreases in some areas of 2.5%/decade. Our analysis of the trends in the proxy data indicate anthropogenic‐driven changes in emissions of OH drivers as well as increasing temperatures cause these trends. This OH product is potentially a significant advance in constraining OH spatial variability and serves as a useful complement to existing tools in understanding the atmospheric oxidative capacity. Comprehensive observations of TCOH are required to assess the fidelity of this method.
Precipitation nowcasting is a critical aspect of meteorological services, which helps people make reasonable arrangements. Nowadays the methods based on recurrent neural networks are widely employed as the primary solution for precipitation nowcasting. However, the predictive unit of these methods has a narrow temporal receptive field that fails to provide sufficient temporal variation information for accurate prediction of the subsequent frame. In addition, they do not adequately model the spatial deformation of visual appearance, which leads to the predicted frames lack of fine-grained spatial appearances. To address these deficiencies, we propose a spatiotemporal enhanced adversarial network (STEAN), a deep learning model for high-resolution precipitation nowcasting. STEAN incorporates a feature extraction module and an adaptive fusion module to refine the spatial appearance of prediction results. Further, it leverages a specialized Halo Attention Spatiotemporal Long Short-Term Memory (HAST-LSTM) unit to model temporal variation information. In order to improve the realism of the predicted sequences, STEAN is combined with a temporal discriminator during the training stage to reduce the blur of prediction results caused by the loss function. STEAN has been assessed on the Moving MNIST, KNMI, and CIKM datasets and the experimental results show that its prediction performance is superior to several other state-of-the-art models.
Abstract This study investigates the effects of sea spray on extreme precipitation forecast in Beijing of China between 28 July and 2 August 2023 as a case test. In this case, fully coupled model increased upward moisture in the Bohai and Yellow Seas and increased accumulated rainfall by 21% in North China. For the extreme precipitation events with the 5‐day accumulated precipitation exceeding 500 mm, the atmosphere‐only model did not forecast the events; the coupled model without sea spray performed well with the 0.29 threat score (TS) and 88 mm root mean square error (RMSE); in the fully coupled model, the effects of sea spray increased atmospheric instability, which increased the precipitation around Beijing and yielded a more accurate forecast with the 0.37 TS and 65 mm RMSE. This paper suggests a scientific clue to improve numerical simulation for extreme rainfall events, however, more cases are still needed for statistical evaluation.
Abstract An “inverse‐temperature layer” (ITL) of water temperature increasing with depth is predicted based on physical principles and confirmed by in situ observations. Water temperature and other meteorological data were collected from a fixed platform in the middle of a shallow inland lake. The ITL persists year‐around with its depth on the order of one m varying diurnally and seasonally and shallower during daytimes than nighttimes. Water surface heat flux derived from the ITL temperature distribution follows the diurnal cycle of solar radiation up to 300 W m−2 during daytime and down to 50 W m−2 during nighttime. Solar radiation attenuation in water strongly influences the ITL dynamics and water surface heat flux. Water surface heat flux simulated by two non‐gradient models independent of temperature gradient, wind speed and surface roughness using the data of surface temperature and solar radiation is in close agreement with the ITL based estimates.
Ulrike Niemeier, Sandra Wallis, Claudia Timmreck
et al.
Abstract The eruption of the Hunga Tonga—Hunga Ha'apai (HTHH) volcano on 15 January 2022 injected about 150 Tg of water vapor (H2O), roughly 10% of the background stratospheric H2O content, to altitudes above 50 km. Simulations of the spatial evolution of the H2O cloud with the ICON‐Seamless model are very close to observations from the Aura Microwave Limb Sounder. The vertical transport of the H2O cloud had three phases: an initial subsidence phase, a stable phase, and a rising phase. Radiative cooling of H2O clearly affects the transport of the H2O cloud, as demonstrated with passive tracers, and is the main driver within the subsidence phase. It also counteracts the large‐scale rising motion in the tropics, leading to the stable phase, and modulates the large‐scale transport of the H2O cloud for about 9 months. This holds for different QBO phases, where the H2O cloud differs mainly in its vertical extent.
Isma Abdelkader Di Carlo, Pascale Braconnot, Matthieu Carré
et al.
Abstract El Niño‐Southern Oscillation (ENSO) flavors have been defined to characterize ENSO events and their teleconnections. Studying El Niño flavor evolution during the Holocene period can provide valuable insights into changes over long time scales. We investigated ENSO flavor evolution using simulations spanning the last 6,000 years and present‐day observations. Two approaches to computing ENSO flavors, in agreement in the present, lead to opposite trends in the last 6,000 years. The methods also differ significantly in their representation of ENSO flavor patterns. However, incorporating the sensitivity of the methods to calibration periods and mean state changes yields similar interpretations of ENSO variability changes. Both methods suggest an increase in El Niño variability spreading to the west and east tropical Pacific over the past 6,000 years. Standardizing El Niño flavor definitions is necessary for meaningful comparisons between studies and robust climate variability analysis.
The resonant right-hand instability (RHI) is often the dominant mode driven by reflected ions upstream of Earth’s quasi-parallel bow shock. In the tradition of Peter Gary, this paper further explores the right-hand instability using numerical solutions of the plasma dispersion relation and non-linear kinetic simulations, with parameters inspired by observations from NASA’s Magnetospheric Multiscale (MMS) mission. Agreement is found between the ion distributions in the particle-in-cell simulations and Magnetospheric Multiscale spacecraft data, which show the gyrophase bunching characteristic of the instability. The non-linear structures created by right-hand instability tend to be stronger when the plasma beta is lower. These structures have sizes of around 100 to 200 ion inertial lengths perpendicular to the magnetic field, presenting planet-sized disturbances to the magnetosphere. 2d and 3D hybrid particle-in-cell simulations show that modes with a range of propagation angles oblique to the magnetic field are excited, providing a ground to understand previous statistical studies of observed foreshock waves.
Abstract N–S rifting is one of the most typical tectonics in southern Tibet, but its formation mechanism remains controversial. Geophysical observations indicated spatial correlations between rifts and lithospheric mantle anomalies, presumably caused by asthenospheric upwelling. Here, we investigate possible plume‐induced rifting via a series of 2‐D thermomechanical models of plume interactions with a heterogeneous lithosphere. The numerical results indicate that a small‐scale mantle plume could promote the formation of a single giant rift throughout the whole thickened crust. In addition, presence of a weak mid‐crustal zone facilitates the rifting development in the upper crust while inhibiting the formation of a giant crustal rift. Instead, multiple rifts develop in the upper crust and thickened crust, jointly controlled by a heterogeneous weak crustal zone and mantle plume. Our numerical results thus emphasize the distinct roles of the weak mid‐crustal zone and small‐scale mantle plume in promoting N‒S rifting in southern Tibet.
<p>Magnetic interference source identification is a critical preparation step
for magnetometer-mounted unmanned aircraft systems (UAS) used for
high-sensitivity geomagnetic surveying. A magnetic field scanner was built
for mapping the low-frequency interference that is produced by a UAS. It was
used to compare four types of electric-powered UAS capable of carrying an
alkali-vapour magnetometer: (1) a single-motor fixed-wing, (2) a
single-rotor helicopter, (3) a quad-rotor helicopter, and (4) a hexa-rotor
helicopter. The scanner's error was estimated by calculating the
root-mean-square deviation of the background total magnetic intensity over
the mapping duration; averaged values ranged between 3.1 and 7.4 nT. Each
mapping was performed above the UAS with the motor(s) engaged and with the
UAS facing in two orthogonal directions; peak interference intensities
ranged between 21.4 and 574.2 nT. For each system, the interference is a
combination of both ferromagnetic and electrical current sources. Major
sources of interference were identified such as servo(s) and the cables
carrying direct current between the motor battery and the electronic speed
controller. Magnetic intensity profiles were measured at various motor
current draws for each UAS, and a change in intensity was observed for
currents as low as 1 A.</p>
The traditional denoising methods in noise robust synthetic aperture radar (SAR) automatic target recognition research are independent of the recognition model, which limits the robust recognition performance. In this article, we present a robust SAR automatic target recognition method via adversarial learning, which could integrate data denoising, feature extraction, and classification into a unified framework for joint learning. Different from the common recognition methods of directly inputting the SAR data into the classifiers, we add a dual-generative-adversarial-network (GAN) model between the SAR data and the classifier for data translation from a noise-polluted style to a relatively clean style to reduce the noise from SAR data. In order to ensure the target information in the SAR data can be retained during the data style translation, reconstruction constraint and label constraint are also used in the dual-GAN model. Then, the more reliable transferred SAR data are fed into the classifier. The parameters of the dual-GAN and classifier are learned through joint optimization in our method. Thus, the data separability is guaranteed in the process of denoising and feature extraction, which greatly improves the recognition performance of the method. In addition, our method can be easily extended to a semisupervised method by using different objective functions for labeled and unlabeled training data, which is more suitable for practical application. Experimental results on MSTAR dataset and Gotcha dataset show that our method can get the encouraging performance in the case of low signal-to-noise ratio and small labeled data size.
Argon-ion polishing field emission-scanning electron microscopy (FE-SEM) is a common method to characterize the microscopic pore structure characteristics of shale reservoirs, but organic macerals cannot be directly identified by FE-SEM alone. Fluorescence microscopy is the main method for identifying macerals. Through a large number of localized FE-SEM and fluorescence microscopy observations, the microscopic characteristics of specific macerals under FE-SEM were summarized. The macerals visualized using FE-SEM can be interpreted based on features such as the external shape, hardness, brightness, color, relief, organic pore development characteristics and fissure development characteristics of the organic matter. Telinite, collotelinite, vitrodetrinite, fusinite, semifusinite, funginite, inertodetrinite, oil bitumen and pyrobitumen were identified.
رخسارههای کانالی از جمله پدیدههای چینهشناسی حائز اهمیت از منظر اکتشاف منابع هیدروکربنی هستند که با توجه به عمق تدفین و محتویات سیال، ممکن است قابلیت مخزنی داشته باشند یا بهعنوان مخاطره حفاری لحاظ شوند. لذا مکانیابی دقیق آنها قبل از تعیین هدف و طراحی مسیر حفاری ضروری است. با توجه به حجم بالای دادههای لرزهای و افزایش روزافزون تعداد نشانگرها، ترکیب نشانگرهای لرزهای با الگوریتمهای محاسباتی متفاوت، جزئیات بالاتری از رویدادهای لرزهای بدست میدهد. در این مطالعه از روشی نیمهخودکار مبنیبر تلفیق نشانگرهای لرزهای بر اساس شبکه عصبی پرسپترون چندلایه با الگوریتم پسانتشار، جهت شناسایی مرزهای کانالهای مدفون واقع در برشهای زمانی از دادههای لرزهای سه بعدی مصنوعی و واقعی حاوی کانال استفاده شده است. نتایج نشان داد که با رسیدن خطای میانگین مربعات عادی شده و درصد ردهبندی نادرست مجموعه آزمایشی و مجموعه آموزشی به کمترین مقدار خود، تصویر بهبود یافتهای از کانالهای موجود در دادههای لرزهای با تفکیکپذیری نسبتا بالا ارائه گردیده است. سپس نتایج حاصل از شناسایی مرز کانالها با استفاده از شبکه عصبی مصنوعی پرسپترون چندلایه با الگوریتم پسانتشار با نتایج حاصل از روشهای تحلیل مولفههای اصلی و k-میانگین و نیز ترکیب این دو روش بهصورت کمی و کیفی مقایسه شد. بررسیها نشان داد که طرحواره پیشنهادی ضمن تاثیرپذیری کمتر نسبت به نوفه پسزمینه، جزئیات دقیقتری از مرزهای کانالهای موجود در داده-های لرزهای ثبت نموده است. استخراج خودکار موقعیت فضایی کانال موجود در داده لرزهای سه بعدی واقعی با استفاده از فیلتر اتصال کوچکترین اجزای ساختاری، تصویر دقیقی از محدوده کانال مورد مطالعه ارائه داده است.
It is reported in this paper, the results of a study of the partitioning around medoids (PAM) clustering algorithm applied to four datasets, both standardized and not, and of varying sizes and numbers of clusters. The angular distance proximity measure in addition to the two more traditional proximity measures, namely the Euclidean distance and Manhattan distance, was used to compute object-object similarity. The data used in the study comprise three widely available datasets, and one that was constructed from publicly available climate data. Results replicate some of the well known facts about the PAM algorithm, namely that the quality of the clusters generated tend to be much better for small datasets, that the silhouette value is a good, even if not perfect, guide for the optimal number of clusters to generate, and that human intervention is required to interpret generated clusters. Additionally, results also indicate that the angular distance measure, which traditionally has not been widely used in clustering, outperforms both the Euclidean and Manhattan distance metrics in certain situations.
An analysis of measurements performed at L'Aquila (Italy) during a deep
minimum of solar and magnetospheric activity (2008–2010) allowed for the
evaluation of possible contamination of the ultralow-frequency (ULF) spectrum (<i>f</i> ≈
1–500 mHz) from artificial disturbances, practically in absence of natural
signals. In addition, the city evacuation and the interruption of all
industrial and social activities after the strong earthquake of 6 April 2009
allowed also for the examination of possible changes of the contamination level under
remarkably changed environmental conditions. Our analysis reveals a
persistent, season-independent, artificial signal, with the same
characteristics in the <i>H</i> and <i>Z</i> components, that affects during daytime
hours the entire spectrum; such contamination persists after the city
evacuation. We speculate that the DC electrified railway (located ≈
33 km from the Geomagnetic Observatory of L'Aquila, it maintained the same train traffic after
the earthquake) is responsible for the observed disturbances.