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Fraunhofer wind turbine dataset contains monitoring data from a 750 W wind turbine, including accelerometers and tachometer, to capture structural response, bearing vibrations and rotational velocity. Additionally, temperatures, wind speed and wind direction have been measured, while weather conditions have been acquired from selected sources. Various damage scenarios, including mass imbalance, and aerodynamic imbalance as well as damages on bearings' outer race, inner race and roller element have been implemented. The availability of time series data makes the dataset well suited for both machine learning and signal processing-based condition monitoring applications. The availability of heterogeneous sensors has created a dataset particularly suited for information fusion, data fusion, multi-sensor approaches, and holistic monitoring. Experiments were conducted in real-world conditions outside of a controlled laboratory environment, thereby introducing challenges such as variable rotor speed, noise, overloads, and other environmental factors. Consequently, the dataset is qualified for tasks involving uncertainty quantification and signal pre-processing. This document will detail the test equipment, experimental procedures, simulated damage cases and measurement parameters. [ABSTRACT FROM AUTHOR]

Weather conditions and agronomical factors are known to affect Fusarium spp. growth and ultimately deoxynivalenol (DON) contamination in oat. This study aimed to develop predictive models for the contamination of spring oat at harvest with DON on a regional basis in Sweden using machine-learning algorithms. Three models were developed as regional risk-assessment tools for farmers, crop collectors, and food safety inspectors, respectively. Data included: weather data from different oat growing periods, agronomical data, site-specific data, and DON contamination data from the previous year. Results showed that: (1) RF models were able to predict DON contamination at harvest with a total classification accuracy of minimal 0.72; (2) good predictions could already be made in June; (3) rainfall, relative humidity, and wind speed in different oat growing stages, followed by crop variety and elevation were the most important features for predicting DON contamination in spring oats at harvest. [ABSTRACT FROM AUTHOR]

This study compares the physical processes responsible for the heavy and light rain variations from the perspective of spring precipitation over Southern China. The results indicate that, heavy rain variation has a closer connection with the western North Pacific anticyclone. Intensified western North Pacific anticyclone and associated warm and humid air transport, coupled with intensified East Asian subtropical jet, favor significant moisture convergence and enhanced convective feedback over the key region, which causes increased heavy rain rather than light rain over there. In comparison, light rain variation shows a close relationship with the anomalous low over Lake Balkhash, which causes lower-tropospheric cyclone over the key region. On the one hand, anomalous cyclone favors lower-tropospheric cooling; concurred with cyclone-induced increased moisture, the lower-tropospheric relative humidity increases over there. On the other hand, the lower-tropospheric cooling center shifts northward with height and causes enhanced atmospheric baroclinity over there. Such atmospheric conditions are conducive to the occurrence of low cloud and more light rain. In addition, intensified East Asian subtropical jet associated with Lake Balkhash anomalous low also provides favorable dynamic lifting condition. Moreover, heavy and light rain variations are more related to El Niño-Southern Oscillation and North Atlantic horseshoe sea surface temperature in preceding winter, respectively. In addition, heavy rain shows a closer relationship with total precipitation amount variation, whereas light rain is more related to extreme consecutive dry days variation. [ABSTRACT FROM AUTHOR]

Image restoration in adverse weather conditions is a difficult task in computer vision. In this paper, we propose a novel transformer-based framework called GridFormer which serves as a backbone for image restoration under adverse weather conditions. GridFormer is designed in a grid structure using a residual dense transformer block, and it introduces two core designs. First, it uses an enhanced attention mechanism in the transformer layer. The mechanism includes stages of the sampler and compact self-attention to improve efficiency, and a local enhancement stage to strengthen local information. Second, we introduce a residual dense transformer block (RDTB) as the final GridFormer layer. This design further improves the network's ability to learn effective features from both preceding and current local features. The GridFormer framework achieves state-of-the-art results on five diverse image restoration tasks in adverse weather conditions, including image deraining, dehazing, deraining & dehazing, desnowing, and multi-weather restoration. The source code and pre-trained models will be released. [ABSTRACT FROM AUTHOR]

Otomycosis is a commonly encountered otological disorder in otorhinolaryngology practice. It has several predisposing factors such as habitual use of cotton buds, water in the ear, hot humid atmospheric condition or high temperature. Diagnosis of otomycosis frequently relies on the patient's history, otoscopic examination and ascertained clinical features and therefore posing diagnostic and therapeutic challenges. Data on otomycosis in Tanzania are scarce. A hospital based descriptive cross-sectional study was conducted at a private hospital from January to June 2022 in Dar es Salaam, Tanzania. All the clinically diagnosed cases were enrolled in the study with typical symptoms and characteristic fungal debris in the external auditory canal. Data was collected using semi-structured questionnaires and analyzed using Statistical Package for Social Sciences version 23. A total of 250 patients were recruited in this study. Majority were females, 153(81.2%) while males were 97(38.8%). Otomycosis was found to be predominantly unilateral in 203 (81.2%) patients. The commonest presenting complaint was ear itching (94.0%) followed by earache (78.6%), hearing loss (75.6%), ear fullness (74.4%), ear discharge (61.2%) and ringing sensation (14.0%). Based on the type of fungal debris on Otoendoscopy, whitish debris was more predominant in 191 (76.4%) patients followed by yellowish debris, 37 (14.8%), blackish debris, 15 (6.0%) and mixed (whitish-black) debris in 7 (2.8%) patients. The commonest risk factor was earbud use (40.8%) and the least one was comorbid conditions (4.4%). Females were more affected by otomycosis. The commonest otological complaint was ear itching while the least one was ringing sensation. Unilateral presentation of otomycosis predominated and the left ear was more affected. The commonest type of fungal debris was whitish debris and the least one was the mixed type (whitish-black). The commonest risk factor for otomycosis was earbud use and the least was comorbid conditions. [ABSTRACT FROM AUTHOR]

Warm-sector rainstorms are highly localized events, with weather systems and triggering mechanisms are not obvious, leading to limited forecasting capabilities in numerical models. Based on the ensemble Kalman filter (PSU-EnKF) assimilation system and the regional mesoscale model WRF, this study conducted a simulation experiment assimilating all-sky infrared (IR) radiance for a warm-sector rainstorm in East China and investigated the positive impact of assimilating the Himawari-8 moisture channel all-sky IR radiance on the forecast of the rainstorm. Results indicate that hourly cycling assimilation of all-sky IR radiance can significantly improve the forecast accuracy of this warm-sector rainstorm. There is a notable increase in the Threat Score (TS), with the simulated location and intensity of the 3-hour precipitation aligning more closely with observations. These improvements result from the assimilation of cloud-affected radiance, which introduces more mesoscale convective information into the model's initial fields. The adjustments include enhancements to the moisture field, such as increased humidity and moisture transport, and modifications to the wind field, including the intrusion of mid-level cold air and the strengthening of low-level convergent shear. These factors are critical in improving the forecast of this warm-sector rainstorm event. [ABSTRACT FROM AUTHOR]

The study investigates the impact of two different wind forcings on the surface ocean conditions of the Arabian Sea (AS) using the Regional Ocean Modelling System (ROMS) simulations. The wind forcings used in the ROMS simulations are the climatological Comprehensive Ocean-Atmosphere Data Set (COADS) winds and the Quik Scatterometer (QuikSCAT) satellite sea-winds. These two wind forcings are implemented and evaluated individually to assess their respective influences on the surface ocean conditions such as temperature, salinity, currents, heat fluxes, fresh water flux and diffusivity over the AS. The QuikSCAT winds are seen stronger (up to 2 m/s) over the AS and the wind stress curl is higher (up to 1.5 × 10− 7 N/m2) in the equatorial region as compared to the COADS winds. Both the simulations have replicated the overall pattern of the surface ocean conditions very well. The stronger QuikSCAT winds simulation has shown higher surface salinity (~ 2 psu) and freshwater fluxes compared to the weaker COADS winds simulation. Again, the strong winds from QuikSCAT have also increased the surface current (up to 0.2 m/s) and horizontal diffusivity in the southern and western boundary of the AS. The implementation of wind forcing provides valuable insights into the dynamics of the regional ocean system and its response to atmospheric conditions. Therefore, the implementation of COADS and QuikSCAT winds in the ROMS simulations will enhance our ability to accurately simulate and predict the behaviour of the surface ocean conditions of the AS. [ABSTRACT FROM AUTHOR]

The frequency of extreme climatic and weather events has increased since 1950 due to global changes induced by human influence. These events can have significant impacts at the individual, population, and community levels across various taxonomic groups. They can be particularly detrimental to bird populations during their breeding season, affecting specific breeding parameters. This study originated from an exceptionally rare breeding season of a resident Great Tit population recorded in spring 2002 when an unusual mortality of nestlings was observed. We quantified weather conditions (temperature, rainfall) from the end of April, a few days before we started detecting failed nests, and compared them with previous and subsequent years. In early May, when many pairs were incubating or caring for newly hatched chicks, we detected unusually low ambient temperatures and unusually high rainfall. This event likely caused that many first clutches failed completely, and that, compared with previous and subsequent years, fledging and breeding success was relatively low in those which were successful. There was also an increased proportion of replacement clutches that year. Anyway, the overall production of fledglings per breeding pair over the breeding season was 2.39, lower than other years. Although recruitment rate in 2003 was similar to other years, the lower production of fledglings in 2002 probably resulted in a decrease in the number of breeding pairs in the following years. [ABSTRACT FROM AUTHOR]

Extreme cold temperatures were observed in July and August 2023, coinciding with the WINFLY (winter fly-in) period of mid to late August into September 2023, meaning aircraft operations into McMurdo Station and Phoenix Airfield were adversely impacted. Specifically, with temperatures below −50°C, safe flight operation was not possible because of the risk of failing hydraulics and fuel turning to gel onboard the aircraft. The cold temperatures were measured across a broad area of the Antarctic, from East Antarctica toward the Ross Ice Shelf, and stretching across West Antarctica to the Antarctic Peninsula. A review of automatic weather station measurements and staffed station observations revealed a series of sites recording new record low temperatures. Four separate cold phases were identified, each a few days in duration and occurring from mid-July to the end of August 2023. A brief analysis of 500-hPa geopotential height anomalies shows how the mid-tropospheric atmospheric environment evolves in relation to these extreme cold temperatures. The monthly 500-hPa geopotential height anomalies show strong negative anomalies in August. Examination of composite geopotential height anomalies during each of the four cold phases suggests various factors leading to cold temperatures, including both southerly off-content flow and calm atmospheric conditions. Understanding the atmospheric environment that leads to such extreme cold temperatures can improve prediction of such events and benefit Antarctic operations and the study of Antarctic meteorology and climatology. [ABSTRACT FROM AUTHOR]

An unstably stratified flow entering into a stably stratified flow is referred to as penetrative convection, which is crucial to many physical processes and has been thought of as a key factor for extreme weather conditions. Past theoretical, numerical, and experimental studies on penetrative convection are reviewed, along with field studies providing insights into turbulence modeling. The physical factors that initiate penetrative convection, including internal heat sources, nonlinear constitutive relationships, centrifugal forces and other complicated factors are summarized. Cutting-edge methods for understanding transport mechanisms and statistical properties of penetrative turbulence are also documented, e.g., the variational approach and quasilinear approach, which derive scaling laws embedded in penetrative turbulence. Exploring these scaling laws in penetrative convection can improve our understanding of large-scale geophysical and astrophysical motions. To better the model of penetrative turbulence towards a practical situation, new directions, e.g., penetrative convection in spheres, and radiation-forced convection, are proposed. [ABSTRACT FROM AUTHOR]

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The occurrence of extreme hot and dry summer conditions in the Pacific Northwest region of North America (PNW) has been known to be influenced by climate modes of variability such as the El Niño-Southern Oscillation and other variations in tropospheric circulation such as stationary waves and blocking. However, the extent to which the subseasonal remote tropical driver influences summer heat extremes and fire weather conditions across the PNW remains elusive. Our investigation reveals that the occurrence of heat extremes and associated fire-conducive weather conditions in the PNW is significantly heightened during the boreal summer intraseasonal oscillation (BSISO) phases 6-7, by ~50–120% relative to the seasonal probability. The promotion of these heat extremes is primarily attributed to the enhanced diabatic heating over the tropical central-to-eastern North Pacific, which generates a wave train traveling downstream toward North America, resulting in a prominent high-pressure system over the PNW. The ridge, subsequently, promotes surface warming over the region primarily through increased surface radiative heating and enhanced adiabatic warming. The results suggest a potential pathway to improving subseasonal-to-seasonal predictions of heatwaves and wildfire risks in the PNW by improving the representation of BSISO heating over the tropical-to-eastern North Pacific. [ABSTRACT FROM AUTHOR]

The analysis of climatic drivers is of utmost importance for defining new hydrological parameters. The territory of Serbia covers the area of 88,361 km2 and all its rivers belong to three big basins, the Aegean, the Black sea and the Adriatic. Extreme weather conditions have been determining the properties of drainage basins within the last sixty year (1953–2023). This research, conducted for the first time in the territory of Serbia, has shown comparative data on the changes of drainage basins and the changes in the type of river networks for the period of last sixty years. The research also includes the changes of the topography, the terrain and the surface of the basins. The main methodology is based on GIS methods, geostatistical method and remote sensing. Adjusted Strahler's order was also used for the purpose of this research. The obtained results on the topology of river networks were classified into six different types, them being parallel, dendritic, mixed, radial, fan-shaped and rectangular. There has been an increase by 8% in parallel, mixed and dendritic type. The most significant changes in the last sixty years were observed within the Aegean and Adriatic sea basin. The surface of the Black sea basin increased by 1.2%, with the addition of a higher number of tributaries, whereas the basins of Adriatic and the Aegean sea had a decrease by 0.3% and 0.9% respectively. The number of tributaries decreased by 5.5% within the Adriatic sea basin, and by 8.8% within the Aegean sea basin. [ABSTRACT FROM AUTHOR]

The analysis of urban thermal environment based on Local Climate Zone (LCZ) is helpful to understand the fine structure of urban heat island (UHI), so as to provide a scientific basis for urban ecological environment management. This research focused on the three biggest cities, Shanghai, Nanjing and Hangzhou, in Yangtze River Delta (YRD) and the UHI characteristics in a heatwave month (July 2017) were investigated. Based on the observations of automatic weather stations, the spatiotemporal characteristics of air temperature and canopy urban heat island intensity (UHII) of each LCZ in three cities under different weather conditions were compared and analyzed by using the LCZ clustering method, and the effects of water bodies, urban greening and sea breeze on urban heat island were discussed. Results show that the air temperature and urban heat island intensity of different LCZs would vary due to the differences in urban geometry, building materials, the proportion of impervious surface and anthropogenic heat. The LCZ based UHII in the three YRD typical cities showed similar characteristics: compact high-rise (LCZ 1), compact mid-rise (LCZ 2) and open mid-rise (LCZ 5) had higher UHII while sparsely built (LCZ 9) had lower UHII. The diurnal variation of UHII in the three cities are different: the UHII diurnal curves of Nanjing and Hangzhou were "U" type, while that of Shanghai was shallow "W" type, which was because Shanghai was vulnerable to sea breeze during the summer day. In addition to land and sea location, large water bodies and urban greening would also impact the spatiotemporal patterns of urban thermal environment. [ABSTRACT FROM AUTHOR]

Origin-destination demand prediction is a critical task in the field of intelligent transportation systems. However, accurately modeling the complex spatial-temporal dependencies presents significant challenges, which arises from various factors, including spatial, temporal, and external influences such as geographical features, weather conditions, and traffic incidents. Moreover, capturing multi-scale dependencies of local and global spatial dependencies, as well as short and long-term temporal dependencies, further complicates the task. To address these challenges, a novel framework called the Spatial-Temporal Memory Enhanced Multi-Level Attention Network (ST-MEN) is proposed. The framework consists of several key components. Firstly, an external attention mechanism is incorporated to efficiently process external factors into the prediction process. Secondly, a dynamic spatial feature extraction module is designed that effectively captures the spatial dependencies among nodes. By incorporating two skip-connections, this module preserves the original node information while aggregating information from other nodes. Finally, a temporal feature extraction module is proposed that captures both continuous and discrete temporal dependencies using a hierarchical memory network. In addition, multi-scale features cascade fusion is incorporated to enhance the performance of the proposed model. To evaluate the effectiveness of the proposed model, extensively experiments are conducted on two real-world datasets. The experimental results demonstrate that the ST-MEN model achieves excellent prediction accuracy, where the maximum improvement can reach to 19.1%. [ABSTRACT FROM AUTHOR]

The hourly to monthly resolution Sr/Ca profiles of modern juvenile giant clam (Tridacna spp.) shells from the northern South China Sea were obtained using ICP-OES, LA-ICP-MS and NanoSIMS. The results showed the variability of Sr/Ca profiles determined by different analytical methods were consistent on monthly time scale. The hourly resolved Sr/Ca determined by NanoSIMS showed pronounced daily cycles which may be associated with the diurnal cycle of the physiology, environment and/or weather condition. By comparison of daily resolution Sr/Ca and contemporaneous instrumental environment data, no robust link was observed between environmental parameters and juvenile Tridacna Sr/Ca, and only weak correlation was found between daily outgoing longwave radiation (OLR) (r = − 0.167, p < 0.01) and Sr/Ca. However, the effective solar radiation cannot explain the variability of nightly Sr/Ca, and some other factors are more likely to influence the Sr/Ca of juvenile Tridacna. Moreover, no consistent correlation was observed between Sr/Ca and growth rate of Tridacna. The mechanism of juvenile Tridacna Sr/Ca is still mysterious, conducting artificial culture experiments seems the best way to unravel the mechanisms behind Sr2+ incorporation into Tridacna shells. [ABSTRACT FROM AUTHOR]

Millimetre-waves (mmWaves), a critical part of 5G, have not been extensively studied in the sub-Saharan tropical environment. This study was conducted to model the outdoor propagation path loss of mmWaves in four Nigerian cities at 28, 38, 60, and 73 GHz for 5G mobile systems: Abuja, Lagos, Ibadan, and Port Harcourt. The study utilised simulation data from a Composite Raytracing-Image-Method propagation model in MATLAB 2023a with OpenStreetMap 3D building maps to formulate the Close-In free-space Reference Distance (CI), Floating Intercept (FI), and Alpha–Beta-Gamma (ABG) large-scale path loss models. This model considered the specific atmospheric conditions, terrain, building materials and structures, and geographical layouts of each site. The study found that the path loss exponent ranged from 2.712 to 3.819 in line-of-sight scenarios, with shadow fading standard deviation (σCI) ranging from 11.778 to 37.199. In non-line-of-sight scenarios, the path loss exponent ranged from 3.375 to 5.033, with σCI ranging from 12.441 to 44.181 across the four frequencies studied. The ABG model consistently provided the most accurate predictions compared to CI and FI, particularly the mean absolute error and root mean square error values. However, performance varied across cities, frequencies, and scenarios, with Port Harcourt exhibiting the highest path loss values across all frequencies and scenarios, followed by Lagos. Furthermore, the omnidirectional large-scale path loss increases marginally as the frequency increases, except at 60 GHz, which had a higher path loss per distance than 73 GHz. Finally, the findings showed that the propagation characteristics of mmWaves for 5G networks in sub-Saharan tropical environments, such as Nigeria, exhibit significant variability based on factors such as atmospheric conditions, terrain, building materials, and geographical layouts. The formulated path loss models can be used to predict the coverage area and signal quality of millimetre-wave communication systems at these frequencies and locations. Article Highlights: Millimetre-waves path loss in Nigerian cities varies significantly with frequency, environmental and structural factors. Path loss increases with frequency, but 60 GHz suffers more loss over distance than 73 GHz due to oxygen absorption. The ABG path loss model outperforms others, providing more accurate predictions for sub-Saharan tropical environments. [ABSTRACT FROM AUTHOR]

Eastern Taiwan overlies a suture zone between the Eurasian Plate and the Philippine Sea Plate and is characterized by frequent earthquakes, often resulting in significant disasters. Notably, the region exhibits characteristics such as a high frequency of earthquakes and a short recurrence period for intense seismic events. While prior research has explored seismic b values across various periods in Taiwan, detailed investigations of the b value in the eastern region are lacking. This study employs the earthquake catalog compiled by the Taiwan Central Weather Administration to analyze spatial–temporal variations in b values in eastern Taiwan. The analysis encompasses seismic events occurring between January 1996 and June 2019. The seismic catalog is divided into three distinct time periods related to large seismic events: period I, 1996–2003 (the Chengkung earthquake); period II, 2003–2013 (the Ruisui earthquake); and period III, 2013–2019 (the Hualien earthquake). Our results indicate that most seismic events with a magnitude greater than 6 are associated with low b values. The overall b value increases during period II and then decreases substantially during period III. Although the estimated b values changed slightly, but the uncertainty in b values remained stable in this study. The epicenters of large earthquakes often overlap with areas with lower b values, especially in plate suture zones, which means that areas with lower b values usually have a higher probability of larger earthquakes. Given the extremely high potential for a catastrophic earthquake, mitigating measures should be adopted at all times. [ABSTRACT FROM AUTHOR]

Weather forecasting is crucial for scientific research and society. Recently, deep learning (DL) methods have achieved significant advancements in medium-range weather forecasting. However, they generally depend on the initial fields generated by the computationally expensive four-dimensional variational (4DVar) data assimilation (DA) technique, which limits their real-time applicability in multivariate three-dimensional (3D) weather forecasting. Here we propose 4DVarFormer by exploring the potential of integrating the 4DVar constraint into an attention-based neural network. 4DVarFormer eliminates the need for background error covariance statistics and the complex adjoint model development. It can generate multivariate 3D weather states within 0.37 s. Moreover, 4DVarFormer can capture inter-variable relationships, allowing the assimilation of observed variables to correct unobserved variables. Hence, medium-range forecasts initiated by 4DVarFormer outperform those of DL-based DA methods and achieve performance comparable to the forecasts initiated by ERA5 reanalyses. These promising findings contribute to future advancements in integrated end-to-end DL weather forecasting systems. [ABSTRACT FROM AUTHOR]

Free space optical (FSO) systems offer an attractive and cost-effective solution for providing communication services in remote regions, as they allow secure transmission without the need for licensing and with lower deployment costs. However, the performance of FSO systems can be significantly impacted by atmospheric turbulences, creating considerable challenges to their deployment. To meet the expanding bandwidth requirements in optical networks, dense wavelength division multiplexing (DWDM) has emerged as a viable option. The development of a 400-Gbps DWDM-FSO system with advanced modulation formats is the subject of this paper. To ensure efficient energy conservation in such a system, power consumption needs to be minimized while maintaining performance level; this calls for optimization of different components within the system. The system is made up of 10 channels and each channel can transmit data at 40 Gbps. Various modulation schemes like carrier-suppressed return-to-zero, modified duo binary return-to-zero, differential phase shift keying, and duo binary return-to-zero are studied for their impact on system performance parameters Q-factor and bit error rate (BER) in C-band around 1550 nm wavelengths. The assessment is also extended to the effects that changing FSO length, input power, and data rate have on these two parameters as well as an evaluation regarding how differing atmospheric conditions influence the FSO system's effectiveness. [ABSTRACT FROM AUTHOR]

Recent development of artificial intelligence (AI) technology has resulted in the fruition of machine learning-based weather prediction (MLWP) systems. Five prominent global MLWP model, Pangu-Weather, FourCastNet v2 (FCN2), GraphCast, FuXi, and FengWu, emerged. This study conducts a homogeneous comparison of these models utilizing identical initial conditions from ERA5. The performance is evaluated in the Eastern Asia and Western Pacific from June to November 2023. The evaluation comprises Root Mean Square Error and Anomaly Correlation Coefficients within the designated region, typhoon track and intensity predictions, and a case study for Typhoon Haikui. Results indicate that FengWu emerges as the best-performing model, followed by FuXi and GraphCast, with FCN2 and Pangu-Weather ranking lower. A multi-model ensemble, constructed by averaging predictions from the five models, demonstrates superior performance, rivaling that of FengWu. For the 11 typhoons in 2023, FengWu demonstrates the most accurate track prediction; however, it also has the largest intensity errors. [ABSTRACT FROM AUTHOR]

Little is known about the effect of future weather and climate on municipal water demand in coastal communities with tourist-centric economies. To address this knowledge gap, we used an econometric model of monthly water demand that allowed for non-linear responses to weather variables to estimate temperature-response functions for demand from a sample of communities in the Oregon Mid-Coast. A main result is that local temperature was not a significant driver of variability in monthly water demand but that temperature in the Willamette Valley—the source of most tourists to the Oregon coast—was. We assumed that the increase in demand in response to higher Willamette Valley temperature arose from an increase in tourists escaping the heat in the Willamette Valley for cooler conditions on the coast. Applying the temperature response functions to scenarios of future climate to the year 2070 led to projected increases in water demand independent of other factors. Whether future tourism is either constrained by the local resident population that serves tourism or is constrained by the potential tourist population in the Willamette Valley, the climate-change contribution to projected water demand is generally of comparable magnitude to—if not greater than—the contribution from resident population change alone over the next 50 years. For communities where the population is projected to decline, the climate effect may more than offset the effect of declining population, resulting in a net positive change in demand. [ABSTRACT FROM AUTHOR]

In computer vision, image dehazing is a low-level task that employs algorithms to analyze and remove haze from images, resulting in haze-free visuals. The aim of Salient Object Detection (SOD) is to locate the most visually prominent areas in images. However, most SOD techniques applied to visible images struggle in complex scenarios characterized by similarities between the foreground and background, cluttered backgrounds, adverse weather conditions, and low lighting. Identifying objects in hazy images is challenging due to the degradation of visibility caused by atmospheric conditions, leading to diminished visibility and reduced contrast. This paper introduces an innovative approach called Dehaze-SOD, a unique integrated model that addresses two vital tasks: dehazing and salient object detection. The key novelty of Dehaze-SOD lies in its dual functionality, seamlessly integrating dehazing and salient object identification into a unified framework. This is achieved using a conditional Generative Adversarial Network (cGAN) comprising two distinct subnetworks: one for image dehazing and another for salient object detection. The first module, designed with residual blocks, Dark Channel Prior (DCP), total variation, and the multiscale Retinex algorithm, processes the input hazy images. The second module employs an enhanced EfficientNet architecture with added attention mechanisms and pixel-wise refinement to further improve the dehazing process. The outputs from these subnetworks are combined to produce dehazed images, which are then fed into our proposed encoder–decoder framework for salient object detection. The cGAN is trained with two modules working together: the generator aims to produce haze-free images, whereas the discriminator distinguishes between the generated haze-free images and real haze-free images. Dehaze-SOD demonstrates superior performance compared to state-of-the-art dehazing methods in terms of color fidelity, visibility enhancement, and haze removal. The proposed method effectively produces high-quality, haze-free images from various hazy inputs and accurately detects salient objects within them. This makes Dehaze-SOD a promising tool for improving salient object detection in challenging hazy conditions. The effectiveness of our approach has been validated using benchmark evaluation metrics such as mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). [ABSTRACT FROM AUTHOR]

In order to determine the critical elements influencing the frequency and severity of two-wheeler-related traffic crashes, this study explores these incidents along eleven years in Vienna. Applying sophisticated multivariate statistical approaches, a comprehensive dataset is used and includes variables about rider demographics, weather conditions, vehicle features, and crash circumstances to reveal intricate correlations and interactions among these elements. Is there significant and distinctive difference based on gender and age with specific conditions under which crashes are occurring influencing different injury degree. Multiple regression undoubtedly points fields for action in statistically based findings providing the most important answer to this research: why there are so many crashes and what is leading cause of injured two-wheelers. The research yields insightful information that politicians and practitioners of road safety may use to improve two-wheeler safety regulations and lower the number of serious injuries and fatalities. Highlights: The stunning fact that just one rider in every fourteen may be uninjured Every second two-wheel rider sustain light injury and every eight severe Lane type, crash circumstance and consequence proves to be significant predictors for crash [ABSTRACT FROM AUTHOR]

Mycoplasma pneumoniae (M. pneumoniae) is a major cause of upper and lower respiratory tract infections and respiratory tract disease in humans. While accumulated pieces of epidemiological evidence suggest an association between meteorological factors and the risk of M. pneumoniae pneumonia, comprehensive nationwide studies on this topic are lacking. We aimed to systematically assess the effect of meteorological factors such as mean temperature and relative humidity on the incidence of M. pneumoniae pneumonia in Japan over a 15-year period from 2005 to 2019. The exposure − response relationships between incidence of M. pneumoniae pneumonia, mean temperature, and relative humidity in all 47 Japanese prefectures (covering whole country) for 2005 − 2019 were quantified by using a distributed lag non-linear model for each prefecture and the estimates from all the prefectures were then pooled using a multivariate mete-regression model to derive nationwide average associations. The study encompassed a total of 162,845 M. pneumoniae pneumonia cases. Our findings indicate that seasonal variations in weekly mean temperature and relative humidity were positively associated with the incidence of M. pneumoniae pneumonia. Specifically, when considering − 1.3 °C as the reference, the relative risk (RR) peaked at 16.8 °C (with RRs of 1.50, 95% confidence interval (CI): 1.32–1.70). Similarly, when using 45.5% relative humidity as the reference, the RR reached its peak at 87.7% (with RRs of 1.49, 95% CI: 1.33–1.67). These results emphasize the necessity of implementing climate change adaptation strategies and public health interventions in regions vulnerable to M. pneumoniae pneumonia. [ABSTRACT FROM AUTHOR]

This study used the time series data of Ganzhou city to explore the individual and interaction effects of temperature and humidity on COPD death, and identify vulnerable subgroups of the population. We collected daily COPD mortality and meteorological data in Ganzhou from 2016 to 2019. The nonlinear distribution lag model was used to examine the associations and interaction between daily mean temperature and humidity and COPD mortality. For the total population, male and 65 years old or above, the relative risk (RR) for COPD mortality could be significant at extremely low temperature (3.3 ℃), reaching 1.799 (95% confidence interval [CI]: 1.216, 2.662), 1.894 (95% CI: 1.164, 3.084) and 1.779 (95% CI:1.185, 2.670). Also, at extremely low humidity (47.8%), the risk reached 1.888 (95% CI: 1.217, 2.930), 1.837 (95% CI: 1.066, 3.165) and 2.166 (95% CI: 1.375, 3.414). The cumulative COPD death risk for females was 3.524 (95% CI: 1.340, 9.267) at high temperature (30.7 ℃), 1.953(95% CI: 1.036, 3.683) at low humidity (47.8%) and 1.726 (95% CI: 1.048, 2.845) at high humidity (96.7%). For the total COPD deaths and subgroups, the interaction effects between daily temperature and humidity were not significant (p > 0.05). Both extremely low temperature and low humidity increased the risk of COPD death in Ganzhou city, especially for males and people over 65 years old. Females were more sensitive to extremely high temperature and humidity. Patients with COPD should pay attention to self-protection under extreme temperature and humidity weather conditions. [ABSTRACT FROM AUTHOR]

Governance in China is often characterized as dualistic. On the one hand, the state invests in stable, rule-based institutions to support effective policy implementation. Yet the state also engages in sudden regulatory campaigns, overstepping its own laws to implement rapid changes in key sectors. Businesses in China have developed coping mechanisms to weather these uncertainties; they cultivate political ties or learn to accommodate unexpected disruptions. Yet in the sphere of environmental policy implementation, businesses across the spectrum are starting to complain that local environmental enforcement feels unpredictable, even arbitrary. What constitutes unpredictable enforcement in an environment already characterized by high levels of regulatory uncertainty? What changes in environmental enforcement are driving these complaints? Using original data on nine years of city-level enforcement measures, I show that both campaign-style and rule-based enforcement have been increasing in intensity and frequency since the mid-2010s. Through qualitative evidence, I show how these two approaches work at cross purposes, generating mixed signals on which strategies businesses should use to reduce pollution or to mitigate regulatory uncertainty. Data shows that this is a nationwide pattern, which explains why business—even experienced, well-connected businesses—are complaining about arbitrary state action. This study draws attention to emerging stress tests of China's dualistic governance, while delving into what these changes portend for state-business relations in China. [ABSTRACT FROM AUTHOR]

The onset of the rainy season in Vietnam displays distinct characteristics compared to other Asian summer monsoon (ASM) regions due to its elongated shape running from south to north, coupled with its complex terrain and the influences of multiple weather systems. Determining the onset is challenging, although numerous empirical indices exist. Here, we proposed an alternative approach to determine the onset by examining the large-scale weather patterns associated with the abrupt increase in rainfall in the country. In this new approach, the evolutions of the ASM system were interpreted as consecutive occurrences of multiple weather patterns, detected by a clustering algorithm Self Organizing Maps (SOMs). Specifically, SOMs were applied on outgoing longwave radiation, 850 hPa wind, and geopotential height over the ASM regions from April to July. The primary features of these atmospheric variables were generalized to 16 patterns. A high-resolution Vietnam Gridded Precipitation (VnGP) dataset was employed to investigate the linkage between these large-scale patterns and local-scale rainfall. The large-scale patterns associated with the onset were distinguished, which are characterized by the development of westerly winds and large-scale convections. Then, the onset of summer rainfall in Vietnam is identified based on the initial appearance of wet-condition weather patterns. This approach offers several advantages: it is objective and does not require additional thresholds of rain or wind to determine the monsoon onset. It also more effectively captures the characteristics of large-scale circulation and deep convection compared to relying on predefined monsoon indices alone. Additionally, this approach helped to elucidate two types of weather patterns associated with the onset: those associated with the northeastward development of deep convection and westerly wind from the Indian Ocean to the Indochina Peninsula and activities of the Boreal Summer Intraseasonal Oscillation. [ABSTRACT FROM AUTHOR]

Precipitation is momentous role in the hydrological cycle, crop irrigation, drinking, and ecosystem maintenance. Excessive and inadequate precipitation leads to flooding, landslides, loss of human lives and properties, and drought. Therefore, precipitation prediction is necessary for planning and protecting water resources and handling natural disasters like floods and drought. This paper proposed a cloud segmentation and precipitation prediction using Machine Learning (ML) algorithms. ML cloud segmentation model is implemented to segment the INSAT-3D Thermal Infra-Red (TIR) image into No-cloud, Low-Level (L-L), Mid-Level (M-L), and High-Level (H-L) clouds and determine the percentage of different clouds over the study area. Fuzzy C-means(FCM) clustering, Gaussian Naïve Bayes (GNB), K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) are the ML algorithms considered for cloud segmentation. The precipitation intensity was predicted on the weather dataset using ML regression models: KNN, DT, and RF. The estimated cloud percentage is validated with cloud cover attributes of the weather dataset. Qualitative and quantitative results of cloud segmentation show that XGBoost, LightGBM, and CatBoost are the best performing models. Similarly, quantitative results of precipitation prediction show that RF and KNN are the best and least performing ML regression models. Also, the performance of the ML regression model is improved by including dimensionality reduction techniques such as Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), and Independent Component Analysis (ICA). [ABSTRACT FROM AUTHOR]

This study evaluated the simulation performance of mesoscale convective system (MCS)-induced precipitation, focusing on three selected cases that originated from the Yellow Sea and propagated toward the Korean Peninsula. The evaluation was conducted for the European Centre for Medium-Range Weather Forecasts (ECMWF) and National Centers for Environmental Prediction (NCEP) analysis data, as well as the simulation result using them as initial and lateral boundary conditions for the Weather Research and Forecasting model. Particularly, temperature and humidity profiles from 3D dropsonde observations from the National Center for Meteorological Science of the Korea Meteorological Administration served as validation data. Results showed that the ECMWF analysis consistently had smaller errors compared to the NCEP analysis, which exhibited a cold and dry bias in the lower levels below 850 hPa. The model, in terms of the precipitation simulations, particularly for high-intensity precipitation over the Yellow Sea, demonstrated higher accuracy when applying ECMWF analysis data as the initial condition. This advantage also positively influenced the simulation of rainfall events on the Korean Peninsula by reasonably inducing convective-favorable thermodynamic features (i.e., warm and humid lower-level atmosphere) over the Yellow Sea. In conclusion, this study provides specific information about two global analysis datasets and their impacts on MCS-induced heavy rainfall simulation by employing dropsonde observation data. Furthermore, it suggests the need to enhance the initial field for MCS-induced heavy rainfall simulation and the applicability of assimilating dropsonde data for this purpose in the future. [ABSTRACT FROM AUTHOR]

Person re-identification (ReID) has made great strides thanks to the data-driven deep learning techniques. However, the existing benchmark datasets lack diversity, and models trained on these data cannot generalize well to dynamic wild scenarios. To meet the goal of improving the explicit generalization of ReID models, we develop a new Open-World, Diverse, Cross-Spatial-Temporal dataset named OWD with several distinct features. (1) Diverse collection scenes: multiple independent open-world and highly dynamic collecting scenes, including streets, intersections, shopping malls, etc. (2) Diverse lighting variations: long time spans from daytime to nighttime with abundant illumination changes. (3) Diverse person status: multiple camera networks in all seasons with normal/adverse weather conditions and diverse pedestrian appearances (e.g., clothes, personal belongings, poses, etc.). (4) Protected privacy: invisible faces for privacy critical applications. To improve the implicit generalization of ReID, we further propose a Latent Domain Expansion (LDE) method to develop the potential of source data, which decouples discriminative identity-relevant and trustworthy domain-relevant features and implicitly enforces domain-randomized identity feature space expansion with richer domain diversity to facilitate domain-invariant representations. Our comprehensive evaluations with most benchmark datasets in the community are crucial for progress, although this work is far from the grand goal toward open-world and dynamic wild applications. The project page is https://github.com/fxw13/OWD. [ABSTRACT FROM AUTHOR]

This paper investigates the incidence of limited attention in a high-stakes business setting: a bar owner may be unable to purge transitory shocks from noisy profit signals when deciding whether to exit. Combining a 24-year monthly panel on the alcohol revenues from every bar in Texas with weather data, we find suggestive evidence that inexperienced, distantly located owners may overreact to the transitory component of revenue relative to the persistent component. This apparent asymmetric response is muted under higher revenue fluctuations. We formulate and estimate a structural model to endogenize attention allocation by owners with different thinking cost. Under the assumptions of the model, we find that 3.9% bars make incorrect exit decisions due to limited attention. As exits are irreversible, permanent decisions, small mistakes at the margin interpreting profit signals can lead to large welfare losses for entrepreneurs. [ABSTRACT FROM AUTHOR]

Study of large rail traffic databases presents formidable challenges for transport system specialists, more particularly while keeping both space and time factors together with the possibility of showing influencing factors related to the users and the transport network environment. To perform such a study, a bibliographic analysis in both statistics and transport revealed that geometrical methods for feature extraction and dimension reduction can be seen as suitable. Since there are several methods/options with, in principle, required input data, this article aims at comparing Principal Component Analysis (PCA) and Correspondence Analysis (CA) for traffic frequency data, both methods being actually used with such data. The procedure stands as follows. First a grand matrix is built where the rows correspond to time windows and the columns to all the possible origin-destination links. Then this large frequency matrix is studied using PCA and CA. The next part of the procedure consists in studying the effects of influencing factors with the possibility of keeping the quantitative scales with PCA or using fuzzy segmentation with CA, the corresponding data being considered as supplementary column points. The procedure is applied on a rail transport network including 10 stations (one corresponding to the airport) and one-hour time windows for 4 months, the available influencing factors being the temperature, rain level and gas price. The comparative analysis shows that CA graphical outputs are more complicated than PCA ones, but reveal more specific results, e.g. the network user behavior related to the airport, while PCA mainly opposes link clusters with low vs. high frequencies. Fuzzy windowing performed using actual and simulated data reduces the loss of information when averaging, e.g. over time, and can show non-linear relational phenomena. The possibility of displaying new traffic data in real time is also considered. [ABSTRACT FROM AUTHOR]

Drought is a global threat caused by the persistent challenges of climate change. It is important to identify drought conditions based on weather variables and their patterns. In this study, we enhanced the Standardized Precipitation Index (SPI) by integrating ground temperature data to develop a more comprehensive metric for evaluating drought severity: the Multivariate Standardized Drought Index. Our metric offers a dual assessment of drought severity, taking into account both the intensity of the drought and its duration. We employ this evaluation in a primary paddy cultivation region of Sri Lanka, with the aim of shedding light on the prevailing drought conditions affecting paddy crops due to insufficient water supply and prolonged periods of elevated temperatures. Additionally, we calibrate our metric by aligning it with historical drought records and subsequently compare the outcomes with those derived from the conventional SPI. [ABSTRACT FROM AUTHOR]

In face of future climatic conditions, methods to ensure the success of forest plantation in warm and dry conditions are required. Mechanical site preparation (MSP) prior to planting is widely used around the world to enhance seedling establishment success. Our study aimed at identifying, among a set of MSP methods that are used in practical forestry, which methods ensure plantation success under dry weather conditions. We evaluated the combined effects of summer drought (estimated using the Standard Precipitation Index) and MSP severity (estimated using vegetation cover in the immediate seedling vicinity one year after MSP) on 1-year seedling survival. We used a network of 20 experimental sites established in France over a 10-year-period, and where seedlings were planted after various MSP. In all treatments (severe MSP, moderate MSP, no MSP), seedling survival was higher in years with rainy summers than in years with dry summers. In rainy years, both moderate and severe MSP methods slightly improved the seedling survival rate (95%) compared to the unprepared control (88%). In dry years, seedling survival was similar after moderate MSP or with no MSP (50 and 54%, respectively), whereas it was much higher after severe MSP (79%). In practical forestry, severe MSP appears as an option to enhance early seedling survival, especially when summer precipitations are lower than the seasonal average, whereas moderate MSP does not significantly improve seedling survival compared to an unprepared control, in all summer weather conditions. [ABSTRACT FROM AUTHOR]

During the past decades, the focus in maritime supply chains has shifted from the individual company to an integrated chain. It is of utmost importance that the various chain actors are well aligned with each other, so as to make transport operations as smooth as possible, ideally also without Estimated Time of Arrival (ETA) deviations. To examine which parameters have to be tackled primarily so as to reduce ETA deviations, this paper examines for each individual ship type at the port of Antwerp which parameters significantly influence ETA deviations. First and foremost, it is observed that seagoing vessels exhibit significantly greater deviations than inland vessels. We also find that chemtankers do not appear to be significantly influenced by their dimensions and geographic parameters. Container ships, on the other hand, are insensitive to both geographical and meteorological parameters. General tankers, on the other hand, show the greatest deviations. The output of the synthesizing Principal Component Analysis indicates that for all ship types the dimensions and meteorological parameters have the greatest explanatory power in the context of deviation predictions. The equations developed on this basis are able to calculate the chance of ETA deviations for each vessel type in the dataset. This result is therefore of great relevance to all actors in the maritime supply chains, as it allows planning much better and avoiding unnecessary asset and staff expenses. [ABSTRACT FROM AUTHOR]

Rain is a common weather phenomenon, and the challenge of removing rain streaks from a single image is crucial due to its detrimental impact on image quality and the extraction of valuable background information. Existing methods commonly rely on specific assumptions regarding rain models, which restricts their ability to accommodate a wide range of real-world scenarios. To overcome this limitation, these methods often require complex optimization techniques or stepwise refinement strategies. In this paper, we propose a novel wide rectangular regional block and dual attention complementary enhancement deraining kernel prediction subnet to meet the challenge. The network called WRRDANet consists of a kernel prediction subnet and pixel-wise dilation filtering. In the kernel prediction subnet, we capture more specific contextual background information and complex pixel-wise kernels. Afterward, the learned pixel-wise multi-scale kernels from the kernel prediction subnet are used to perform dilation filtering on the original rainy image, effectively restoring richer background details by expanding the scope of deraining to a larger extent. We conducted a comprehensive evaluation using synthetic and real rainfall datasets to demonstrate the effectiveness of our approach. The results, both qualitatively and quantitatively, indicate that our approach outperforms other popular rain removal methods. [ABSTRACT FROM AUTHOR]

In recent years, the concentrations of ozone and the pollution days with ozone as the primary pollutant have been increasing year by year. The sources of regional ozone mainly depend on local photochemical formation and transboundary transport. The latter is influenced by different weather circulations. How to effectively reduce the inter-regional emission to control ozone pollution under different atmospheric circulation is rarely reported. In this study, we classify the atmospheric circulation of ozone pollution days from 2014 to 2019 over Central China based on the Lamb–Jenkinson method and the global analysis data of the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5) operation. The effectiveness of emission control to alleviate ozone pollution under different atmospheric circulation is simulated by the WRF-Chem model. Among the 26 types of circulation patterns, 9 types of pollution days account for 79.5% of the total pollution days and further classified into 5 types. The local types (A and C type) are characterized by low surface wind speed and stable weather conditions over Central China due to a high-pressure system or a southwest vortex low-pressure system, blocking the diffusion of pollutants. Sensitivity simulations of A-type show that this heavy pollution process is mainly contributed by local emission sources. Removing the anthropogenic emission of pollutants over Central China would reduce the ozone concentration by 39.1%. The other three circulation patterns show pollution of transport characteristics affected by easterly, northerly, or southerly winds (N-EC, EC, S-EC-type). Under the EC-type, removing anthropogenic pollutants of East China would reduce the ozone concentration by 22.7% in Central China. [ABSTRACT FROM AUTHOR]

Temperature-dependent rate coefficients for the reactions of 2-methyl tetrahydrofuran (MTHF) with Cl atoms in the temperature range of 268–343 K at atmospheric pressure were measured using the relative-rate method. Ethylene and propane were used as reference compounds. Quantitative analysis of the post-photolysis reaction mixture was conducted using a gas chromatograph paired with a flame ionization detector (GC-FID). A gas chromatograph connected to a mass spectrometer (GC–MS) was employed for the purpose of qualitative analysis. In the experimental temperature range, the derived Arrhenius expression for the title reaction is represented by the equation k M T H F + C l Expt 268 - 343 K = (1.48 ± 0.13) × 10 - 12 × e x p 1474.51 ± 25.16 T cm3 molecule−1 s−1. In addition to our experimental findings, we conducted computational calculations employing the CCSD(T)//BHandHLYP/6–31 + G(d,p) level of theory to complement our study. The canonical transition state theory (CTST) was utilized to compute the rate coefficients at 250–400 K and 760 Torr. The Arrhenius expression for the theoretically calculated "k" values is found to be k M T H F + C l Theory 250 - 400 K = (1.51 ± 0.10) × 10 - 12 × e x p 1544.97 ± 22.14 T cm3 molecule−1 s−1. The local reactivity parameters, such as Fukui functions ( f r 0 ), local softness ( s r 0 ), and global softness (S ) were also calculated theoretically to understand the site-specific reactivity trend of MTHF towards Cl atoms. The atmospheric implications, branching ratios, degradation mechanism, and feasibility of the reaction are discussed in this study. [ABSTRACT FROM AUTHOR]

The quantitative characterization and prediction of localized severe weather events that emerge as coherences generated by the highly non-linear interacting multivariate dynamics of global weather systems poses a significant challenge whose solution is increasingly important in the face of climate change where weather extremes are on the rise. As weather measurement systems (multiband satellite, radar, etc) continue to dramatically improve, increasingly complex time-dependent multivariate 3D datasets offer the potential to inform such problems but pose an increasingly daunting computational challenge. Here we describe the application to global weather systems of a novel computational method called the Entropy Field Decomposition (EFD) capable of efficiently characterizing coherent spatiotemporal structures in non-linear multivariate interacting physical systems. Using the EFD derived system configurations, we demonstrate the application of a second novel computational method called Space-Time Information Trajectories (STITs) that reveal how spatiotemporal coherences are dynamically connected. The method is demonstrated on the specific phenomenon known as atmospheric rivers (ARs) which are a prime example of a highly coherent, in both space and time, severe weather phenomenon whose generation and persistence are influenced by weather dynamics on a wide range of spatial and temporal scales. The EFD reveals how the interacting wind vector field and humidity scalar field couple to produce ARs, while the resulting STITS reveal the linkage between ARs and large-scale planetary circulations. The focus on ARs is also motivated by their devastating social and economic effects that have made them the subject of increasing scientific investigation to which the EFD may offer new insights. The application of EFD and STITs to the broader range of severe weather events is discussed. [ABSTRACT FROM AUTHOR]

The success of online social platforms hinges on their ability to predict and understand user behavior at scale. Here, we present data suggesting that context-aware modeling approaches may offer a holistic yet lightweight and potentially privacy-preserving representation of user engagement on online social platforms. Leveraging deep LSTM neural networks to analyze more than 100 million Snapchat sessions from almost 80.000 users, we demonstrate that patterns of active and passive use are predictable from past behavior ( R 2 =0.345) and that the integration of context features substantially improves predictive performance compared to the behavioral baseline model ( R 2 =0.522). Features related to smartphone connectivity status, location, temporal context, and weather were found to capture non-redundant variance in user engagement relative to features derived from histories of in-app behaviors. Further, we show that a large proportion of variance can be accounted for with minimal behavioral histories if momentary context is considered ( R 2 =0.442). These results indicate the potential of context-aware approaches for making models more efficient and privacy-preserving by reducing the need for long data histories. Finally, we employ model explainability techniques to glean preliminary insights into the underlying behavioral mechanisms. Our findings are consistent with the notion of context-contingent, habit-driven patterns of active and passive use, highlighting the value of contextualized representations of user behavior for predicting user engagement on online social platforms. [ABSTRACT FROM AUTHOR]

The stochastic synthesis of extreme, rare climate scenarios is vital for risk and resilience models aware of climate change, directly impacting society in different sectors. However, creating high-quality variations of under-represented samples remains a challenge for several generative models. This paper investigates quantizing reconstruction losses for helping variational autoencoders (VAE) better synthesize extreme weather fields from conventional historical training sets. Building on the classical VAE formulation using reconstruction and latent space regularization losses, we propose various histogram-based penalties to the reconstruction loss that explicitly reinforces the model to synthesize under-represented values better. We evaluate our work using precipitation weather fields, where models usually strive to synthesize well extreme precipitation samples. We demonstrate that bringing histogram awareness to the reconstruction loss improves standard VAE performance substantially, especially for extreme weather events. [ABSTRACT FROM AUTHOR]

A long-term climate dataset of daily global middle-troposphere satellite microwave temperature sounding observations of brightness temperature is first utilized to statistically analyze atmospheric variability in 22 individual winters from 1998–1999 to 2019–2020. Analysis of the temporal variability in the observations reveals a significant synoptic variability with 3–5 and 10-day time periods, an intraseasonal low-frequency variability that occurs on the time scales of 1–3 months, an interannual variability dominated by ENSO-related phenomena in the tropical Pacific, as well as a few sharp bands at one year, half year, 3–4 months. The same dataset is then used for examining if satellite microwave brightness temperature observations can directly capture teleconnections during Northern Hemisphere winters. Temporal correlation coefficients of the observed monthly mean brightness temperature variability between all possible pairs of grid points in the Northern Hemisphere exhibit five well-known teleconnection patterns that were identified within global reanalysis datasets in early studies in the 80's. They are the eastern Atlantic pattern, the Pacific/North American pattern, the western Atlantic pattern, the western Pacific pattern, and the Eurasian pattern. Our results suggest a detectable weather and climate of the atmosphere and call a direct application of satellite observations for studying weather and climate. [ABSTRACT FROM AUTHOR]

Arctic regions are warming significantly faster than other parts of the globe, leading to changes in snow, ice and weather conditions, ecosystems and local cultures. These changes have brought worry and concern and triggered feelings of loss among Arctic Indigenous Peoples and local communities. Recently, research has started to address emotional and social dimensions of climate change, framed through the concept of ecological grief. In this study, we examine sociocultural impacts of climate change and expressions of ecological grief among members of reindeer herding communities in the Sámi Homeland in Finland. Results indicate that ecological grief is felt in connection to major environmental concerns in the area: changes in winter weather and extreme weather events, Atlantic salmon decline and land use changes, which all have cultural and social consequences. Our results indicate that ecological grief is strongly associated with ecological losses, but also with political decisions regarding natural resource governance. [ABSTRACT FROM AUTHOR]

Wind turbines installed in regions with high altitudes and low temperatures on winter season, ice can accumulate on the blade surfaces during operating condition. A small amount of ice accretion on the blade surface can result in several crucial issues, such as AEP (annual energy production) reduction due to degrade aerodynamic performance, and safety concerns related to ice falling down. Predicting and elucidating icing accretion phenomena under various weather conditions are significantly essential in the aspect of operating and maintenance. The primary objective of this study was to assess the impact of ice accretion on the aerodynamic characteristics of wind turbine blades CFD (computational fluid dynamics) analysis, including ice thickness and shape, under varying weather conditions. Therefore, this study focused on identifying the aerodynamic characteristics affected by ice accretion, including ice thickness and shape under various weather conditions through Fensap. Through this methodology, the presence of ice accumulation on the blade surfaces was investigated, and its adverse impact on aerodynamic performance has been assessed. [ABSTRACT FROM AUTHOR]

Our insights into the responses of immature stages of vernal solitary bees to the thermal challenges of spring weather mostly come from studies of cavity-nesting megachilid bees in constant temperature incubators. Most of these studies reported either the average timing of adult emergence or the general pace of immature development. In this study, we tracked the longevity and developmental fates of eggs laid by nearly 200 female Osmia lignaria (Hymenoptera: Megachilidae) occupying observation nests set outdoors in a nature preserve. In the spring of 2022, the nesting population was challenged by a prolonged period of unseasonably chilly wet weather. As a consequence, fewer nest cells were provisioned, and hatching among the 2416 eggs was delayed for as much as a month. Surprisingly, these delays incurred no ill-effects on either egg survival or subsequent larval development, both of which were comparable to the much warmer spring of 2021 (3630 eggs). The population’s 33% reproductive shortfall in 2022 was instead attributable to limited hours warm enough for flight and foraging, in part evidenced by the paucity of multi-cell days. Eggs and larvae of this bee, and probably other vernal solitary bees, readily tolerate prolonged episodes of cold springtime weather. [ABSTRACT FROM AUTHOR]

The analysis of extreme precipitation events plays a crucial role in the management of water resources, infrastructure, public water supply, agriculture, fire control, and public health. For an accurate characterization of precipitation events using historical series, the observed variations must be solely attributable to weather and climate conditions. This study aimed to identify homogeneous gauge stations in Paraná State, southern Brazil, based on four statistical tests (SNHT, Buishand, Pettitt, and Von Neumann) and conduct a homogeneity analysis of daily rainfall data. Missing values were imputed into the time series, and only stations with up to 25% of data gaps were included. Of the 482 stations analyzed in the state, 73.7% (n = 355) demonstrated homogeneity, 11.6% (n = 56) were considered doubtful, and 14.7% (n = 71) were deemed suspect. The highest number of homogeneity breaks was recorded from 1990 to 2005. The number of breaks during this period was estimated at 75 (59.1%) by SNHT, 86 (67.7%) by the Buishand test, and 89 (70.1%) by the Pettitt test. The year with the highest number of homogeneity breaks was 1998, with 81 breaks identified by the Pettitt test. These breaks may be related to El Niño and La Niña phenomena, given that a large sample of rainfall stations was analyzed in the study. [ABSTRACT FROM AUTHOR]

Cloud cover, the percentage of the sky obscured by clouds, has significant effects on agriculture, global warming, and solar energy. This research has investigated machine learning techniques to accurately predict cloud cover in Bangladesh, comparing the effectiveness of various algorithms such as k-nearest neighbors, decision trees, Naïve Bayes, support vector machine, logistic regression, gradient boosting machine, and random forest using data from the Bangladesh Meteorological Department from 1980 to 2022. Validation is conducted using confusion matrix analysis, which rigorously assesses validity and identifies errors. The study presents a methodology for using training and test sets to objectively estimate performance metrics, including precision, sensitivity, accuracy, positive and negative predictive values, F 1 score, and Cohen's kappa coefficient. Results are based on the analysis of training and test data sets. The mentioned machine learning techniques utilize cross-validation. Random forest shows the highest performance. Cross-validation adjusts the results, confirming the random forest's top accuracy, kappa value, and F 1 score. Next in order, the support vector machine performs well. Among decision tree algorithms, it was discovered that the CART model performed better than C5.0. The Naïve Bayes algorithm performs the least well. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of recorded variations (anomalies) in the potential gradient of electrical field in the atmosphere caused by the propagation of eruption plumes discharged by eruptions of Shiveluch and Bezymianny volcanoes in Kamchatka. The anomalies were recorded at various distances from eruption centers and under different conditions of atmospheric stratification. These conditions have enabled us to show that the eruption plumes of Shiveluch and Bezymianny possessed a 3D electrostatic structure that is consistent with a known phenomenological model derived on the basis of surveys conducted on various volcanoes worldwide. According to this model, the top of an eruption plume contains a positive volumetric electrostatic charge, while the respective charges are negative in the middle, and positive in the lower part of the plume. [ABSTRACT FROM AUTHOR]

Free-space optical (FSO) communication has become a focus of both study and commercial interest. FSO transfers data between sites in free space, typically the Earth's atmosphere or space, using laser-emitted light beams, in contrast to standard radio frequency (RF) wireless communication. The possibility for high data transmission speeds, the lack of licencing constraints, versatility, affordability, simplicity of deployment, and its environmentally friendly communication method which provides an alternative to crowded radio frequency-based communications are the driving forces behind this growing interest. To determine the feasibility of FSO communication in the cities of Alexandria and Aswan in Egypt, we utilized the micrometeorology model to analyze how weather conditions and atmospheric turbulence impact on FSO linkages based on real-time weather data. In both the winter and summer seasons, we evaluated the bit error rate, beam spreading, scintillation, and transmission range of FSO. Moreover, bit error rate performance was improved by the use of the multi-hop Decode and Forward relay. [ABSTRACT FROM AUTHOR]