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Conventional multi-cylinder drying of paper and board involves a mixture of conductive drying from steam-heated dryer cylinders and convective drying by the flow of heated air over the surface of the paper web in the pockets. Pocket ventilation is a critical component in assisting heat and mass transfer during the drying process but is the primary contributor toward removing evaporated water from the web. Air temperature, velocity, and humidity are critical parameters involved in the convective drying process. This paper covers an experimental study involving the design and development of a small lab-scale setup for convective drying of various grades of paper and board, monitoring multiple parameters like paper temperature, moisture content, air humidity, temperature, and velocity measured in situ as the drying proceeds with continuous and accurate sampling capabilities for all parameters in the sample and the system. Instantaneous drying rates, heat, and mass transfer coefficients were also deduced for every time step till the paper completely dried. Furthermore, the coefficients obtained were also reported in the form of dimensionless correlations, and the results were compared against traditional correlations used in the modeling of paper drying. This data will be useful in process development, modeling, design, and the paper drying process simulation. [ABSTRACT FROM AUTHOR]

Modeling the pipe failure phenomenon is essential for any water utility. The linear extension of the Yule process (LEYP), proposed in 2009, was intended to be a synthesis of the pipe failure models available at the time. Since then, two major improvements have been proposed. First, in 2012 and 2014, several papers introduced time-dependent factors into the LEYP model. Second, in 2016, the LEYP with Selective Survival (LEYP2s) was proposed to deal with the selective survival bias. The objectives of this paper were (1) from a theoretical point of view, to show how temporal covariates also can be introduced into the LEYP2s model; and (2) from a practical point of view, to prove the feasibility and interest of a LEYP2s model with time-dependent variables, using a case study. The mathematical consequences of taking into account temporal factors within the LEYP2s model are presented, together with the solutions found that allow the calculation of the model likelihood and thus the estimation of the model parameters. These solutions were applied to the black polyethylene service line network of the city of Bordeaux, France. Three time-dependent covariates were considered: air temperature, pressure regime, and type of disinfectant. This case study proved the feasibility of a LEYP2s model with temporal variables. The results showed that the time-dependent factors considered did not improve the ranking of the service lines, but did improve the annual prediction of the network failure rate. [ABSTRACT FROM AUTHOR]

In order to understand the application of frost measurement of air source heat pump heat exchanger, a research on frost measurement of air source heat pump heat exchanger based on image recognition processing technology is put forward. In this paper, the scheme design of defrosting control system based on image processing technology is introduced, and the frost degree coefficient of outdoor heat exchanger fin surface is introduced to characterize the frost degree of fin surface. Secondly, the experimental environment room was built, and the conventional defrosting control method of the existing heat pump unit was tested on the spot. It was found that in the air temperature of -6~0 °C and the air relative humidity of 86~92%, the unit appeared the phenomenon of "defrosting in time" and "defrosting without frost". Finally, the change of frost layer on the surface of the outdoor heat exchanger fin during defrosting is analyzed, and the coefficient of frost degree on the surface of the fin is calculated and analyzed. At the beginning of defrosting, due to the large degree of frost, the P-value of the frost degree coefficient on the surface of the fin is close to 1, when the defrosting is carried out for 125 seconds, the P-value of the frost degree coefficient on the surface of the fin decreases sharply to 0.09, when the defrosting is completed, the P-value of the frost degree coefficient on the surface of the outdoor heat exchanger is maintained at about 0.04. The defrosting control method of air source heat pump based on image processing technology is proposed in this paper, and its technical feasibility and effect are verified by experimental tests, which lays a foundation for future popularization and application. [ABSTRACT FROM AUTHOR]

There have been fruitful publications on thermal comfort of urban residential buildings in China. However, there is a lack of reviews on this topic to perform a comprehensive analysis and find opportunities to meet occupants' thermal comfort needs while improving building energy efficiencies. This paper addresses this issue by presenting a systematic review on the advancements in research on thermal comfort in urban residential buildings in China. Firstly, two common thermal comfort research approaches, i.e., field studies and laboratory studies, are discussed. Secondly, eleven main thermal comfort evaluation indicators are summarized. Finally, this paper analyzes the thermal comfort survey data from different researchers, discusses the impacts of adaptive behaviors on human thermal comfort, and provides recommendations for future research on urban residential thermal comfort. It was found that people have higher and higher requirements for their indoor thermal environment as time goes by, especially in the winter; the thermoneutral temperature is higher in warmer climate regions in the summer but lower in the winter than in colder climate regions; the thermoneutral temperature tends to increase with the indoor air temperature due to an adaptation to the indoor thermal environment. The outcomes of this paper provide valuable information on thermal comfort behaviors of urban residents in different climate zones in China, which can serve as a resource for the academic community conducting future research on thermal comfort and assist policymakers in enhancing building energy efficiencies without compromising the occupants' comfort. [ABSTRACT FROM AUTHOR]

This paper describes the metrological procedure carried out for the evaluation of the building influence on air temperature measurements. This evaluation aims to produce reliable conclusions, information, and data to contribute to the WMO siting classification schemes for air temperature measurements. For this purpose, a field experiment was designed, deployed, and carried out. As a result, one-year-lasting air temperature measurements were collected and analyzed. In this field experiment, a 200 m wide building is the unique artificial heat source and the unique object projecting shades over a flat surface (no discernible slope) in an open space bigger than 40,000 m2, covered with short grass. Eight calibrated thermometers, equipped with the same model of artificially ventilated radiation shields, were set up at a height of 1.5 m from the ground and at different distances from a 200 m wide building. This configuration provides the observation of the horizontal air temperature radially distributed from the building and, as a conclusion, it enables the quantification of the building influences on air temperature measurements at different distances from the building. This document describes the field experiment, the analysis procedure, the evolution of the building influence on air temperature measurements along the day, and the impact of other meteorological parameters on this building effect. Two different building effects are observed: the positive building effect, where the air temperature decreases with the distance to the building, and the negative building effect, where the air temperature increases with the distance to the building. It is also noticed that the building influence is higher on clear days and the daily maximum building influence values are directly linked with the corresponding maximum solar irradiance. The influence of wind on the building effect is also analyzed, reaching the conclusion that due to characteristic of local winds, in terms of low speed and direction, the wind impact could be considered as negligible. The maximum values of building influence on air temperature measurements, the associated uncertainty analysis, and the conclusions are presented in this paper. All these points have been addressed using metrological principles with the purpose of giving consistency and robustness to the evidence presented here. [ABSTRACT FROM AUTHOR]

Temperature and wind observations from aircraft are regarded of major importance for aviation meteorology and numerical weather prediction (NWP). The European Meteorological Aircraft Derived Data Center (EMADDC) system processes aircraft surveillance data received from air traffic control (ATC) and converts it into observations of wind and temperature. Only so-called Mode-S Enhanced Surveillance data can be used, because this data contains the air vector and ground vector of the aircraft, from which a wind vector can be inferred. To acquire high quality observations, the data is processed in three steps: pre-processing, processing, and post-processing. The pre-processing is needed to obtain high-quality information and to calculate several correction values such as temperature corrections and heading corrections. Processing converts the aircraft data into meteorological information and finally post-processing guarantees that only high-quality information is made available. The quality of the observations produced is verified by comparing these observation to other upper air wind and temperature observations from radiosondes and comparing them with NWP data. This paper presents the EMADDC system, operational since 2019. [ABSTRACT FROM AUTHOR]

Cooling systems provide a safe thermal environment for the reliable operation of IT equipment in data centers (DCs) while generating significant energy consumption. Therefore, to achieve energy savings in cooling system control under dynamic thermal distribution in DCs, this paper proposes a multi-setpoint cooling control approach based on deep reinforcement learning (DRL). Firstly, a thermal model based on the XGBoost algorithm is constructed to precisely evaluate the thermal distribution in the rack room to guide real-time cooling control. Secondly, a multi-set point cooling control approach based on the deep Q-network algorithm (DQN-MSP) is designed to finely regulate the supply air temperature of each air conditioner by capturing the thermal fluctuations to ensure the dynamic balance of cooling supply and demand. Finally, we adopt the extended CloudSimPy simulation tool and the real workload trace of the PlanetLab system to evaluate the effectiveness and performance of the proposed approach. The simulation results show that the proposed control solution effectively reduces the cooling energy consumption by over 2.4% by raising the average air supply temperature of the air conditioner while satisfying the thermal constraints. [ABSTRACT FROM AUTHOR]

This paper compares the performance of a vertical-finned microchannel heat exchanger (VMHX) and a 2-row fin-tube heat exchanger (FTHX) under wet and frosting conditions. The VMHX features vertically oriented fins with an extension at the windward side and parallel microchannel tubes, allowing for efficient water drainage and better performance than conventional microchannel heat exchangers with parallel serpentine fins. However, the performance of the VMHX has not been sufficiently compared to that of FTHX. After conducting experiments under specific operating conditions, the VMHX exhibits better water drainage performance than the FTHX at higher inlet air velocities, higher maximum heat flow, and time-integrating heat flow under certain operating conditions. Although the heating time is relatively shorter than the 2-row FTHX, these results suggest that the VMHX may be a more suitable option for applications requiring efficient water drainage and heat transfer. Additionally, the operating conditions significantly affect the heat exchanger's performance. The performance of VMHX is analyzed under different inlet air velocities, inlet air temperatures, and evaporation temperatures. The results indicate that frost itself had no effect on the heat exchanger performance and that the decrease of the inlet air flow rate was the main factor that decreased the heat exchanger performance. Overall, the results of this study provide valuable insights into the performance of VMHX and FTHX under wet and frosting conditions and the impact of various operating parameters on VMHX performance. These findings can inform the development of more efficient and reliable heat exchangers for various applications. [ABSTRACT FROM AUTHOR]

The morphological complexity of urban environments results in a high spatial and temporal variability of the urban microclimate. The consequent demand for high-resolution atmospheric data remains a challenge for atmospheric research and operational application. The recent widespread availability and increasing adoption of low-cost mobile sensing offer the opportunity to integrate observations from conventional monitoring networks with microclimatic and air pollution data at a finer spatial and temporal scale. So far, the relatively low quality of the measurements and outdoor performance compared to conventional instrumentation has discouraged the full deployment of mobile sensors for routine monitoring. The present study addresses the performance of a commercial mobile sensor, the MeteoTracker (IoTopon Srl), recently launched on the market to quantify the microclimatic characteristics of the outdoor environment. The sensor follows the philosophy of the Internet of Things technology, being low cost, having an automatic data flow via personal smartphones and online data sharing, supporting user-friendly software, and having the potential to be deployed in large quantities. In this paper, the outdoor performance is evaluated through tests aimed at quantifying (i) the intra-sensor variability under similar atmospheric conditions and (ii) the outdoor accuracy compared to a reference weather station under sub-optimal (in a fixed location) and optimal (mobile) sensor usage. Data-driven corrections are developed and successfully applied to improve the MeteoTracker data quality. In particular, a recursive method for the simultaneous improvement of relative humidity, dew point, and humidex index proves to be crucial for increasing the data quality. The results mark an intra-sensor variability of approximately ± 0.5 °C for air temperature and ± 1.2 % for the corrected relative humidity, both of which are within the declared sensor accuracy. The sensor captures the same atmospheric variability as the reference sensor during both fixed and mobile tests, showing positive biases (overestimation) for both variables. Through the mobile test, the outdoor accuracy is observed to be between ± 0.3 to ± 0.5 °C for air temperature and between ± 3 % and ± 5 % for the relative humidity, ranking the MeteoTracker in the real accuracy range of similar commercial sensors from the literature and making it a valid solution for atmospheric monitoring. [ABSTRACT FROM AUTHOR]

This paper presents a method for determining the impact of dam reservoirs on the ice cover of rivers downstream of their locations based on a long measurement period (1950–2020) and synthetic-aperture radar (SAR) data. Two rivers and two sets of dam reservoirs located in the Carpathian Mountains (central Europe) were selected for this study. In order to estimate the influence of reservoirs, a logistic regression model was built to describe the relationship between the course of air temperature and the occurrence of ice cover (i.e., total ice cover and border ice) at water gauge cross sections upstream and downstream of the reservoirs. The influence of reservoirs was then defined as the differences between the number of days with ice cover predicted from air temperature and those observed at the water gauge cross sections. Additionally, the extent of the impact of the reservoirs was estimated based on SAR data (Sentinel-1) by identifying river sections downstream of the reservoirs on which total ice cover did not form despite the persistence of very low air temperatures. The study shows that dam reservoirs play an important role in decreasing the incidence of ice cover on rivers. The occurrence of ice cover as a result of reservoir operations could decrease by over 80 % in the sections immediately downstream of the reservoirs. The impact of the reservoir on river ice cover diminishes as the distance from the reservoir increases. Using SAR data, it was estimated that total ice cover did not form in sections 26–60 km downstream of the reservoirs, despite the presence of favorable thermal conditions. Based on the results of the study presented here, it can be assumed that in areas where many dam reservoirs are located, the ice regime of rivers is significantly transformed, which should be taken into account when studying river ice cover. This study also demonstrates that the logistic regression model and SAR data are useful tools for assessing the impact of dam reservoirs on river ice cover. [ABSTRACT FROM AUTHOR]

This study addresses a crucial gap in understanding the impact of urban morphologies on the canopy urban heat islands (CUHI) effect. The selection of reference stations lacks a unified standard, and their surface air temperature (SAT) sequences are also inevitably influenced by urbanization. However, synchronous observational data from relocated meteorological stations could provide high-quality sample data for studying CUHI. Utilizing remote sensing techniques, the findings of this paper revealed that the observation environment of stations after relocation exhibited remarkable representativeness, with their observation sequences accurately reflecting the local climatic background. The differences in synchronized observation sequences could characterize the CUHI intensity (CUHII). Among the various factors, land use parameters and landscape parameters played particularly significant roles. Furthermore, the fitting performance of the random forest (RF) model for both training and testing data was significantly superior to that of the linear model and support vector regression (SVR) model. Additionally, the influence of local circulation on CUHI could not be overlooked. The mechanisms by which urban morphologies affect CUHII under different circulation backgrounds deserve further investigation. [ABSTRACT FROM AUTHOR]

Drought is a natural phenomenon that occurred in Central Europe in the past, but it is becoming a more serious problem due to the changes in its occurrence and intensity related to climate change. The main aim of this paper was to determine changes in air temperature and precipitation amounts in Slovakia from 1931 to 2020 and to identify changes in drought occurrence and intensity using the Standardised Precipitation and Evapotranspiration Index (SPEI). Cluster analysis was used to determine regions with similar drought occurrences. For each of the identified five clusters, drought characteristics were determined and compared between two reference periods 1961–1990 and 1991–2020. While a decrease in the number of months with SPEI-1 below − 1 was observed in autumn, spring and summer months showed a drying tendency. Overall, we can say that we observed a shift in drought occurrence from autumn and winter months to spring and summer months. This is an important finding for the agriculture or forestry because it affects work management and planning. The clearest tendency in drought events was observed in the western part of Slovakia covering areas with agriculturally intensive land use. Besides prolonging drought events, there was also a higher accumulated deficit for each event. The increase in drought intensity was not spatially consistent over the cluster area, mostly reaching a slight decline of about − 0.1 to − 0.3, which means slight intensification of drought periods in 1991–2020. [ABSTRACT FROM AUTHOR]

We evaluated the performance of the Thornthwaite (ThW) method using two gridded climate datasets to estimate monthly average daily potential evapotranspiration (PET). The PET estimated from two gridded series were compared to PET and to reference evapotranspiration (ETo) determined, respectively, through the ThW and Penman-Monteith model parameterized on Food and Agriculture Organization–Irrigation and Drainage paper No 56 (PM-FAO56) using data from weather stations. The PET by ThM was based on monthly air temperature series (1961–2010) from two gridded datasets (Global Historical Climatology Network-GHCN and University of Delaware-UDel) and 21 weather stations of the National Institute of Meteorology (INMET) located in Southeastern Brazil. The ETo PM-FAO56 used monthly climate series (1961–2010) on sunshine duration, air temperature, relative humidity, and wind speed from weather stations of the INMET. The PET estimated using UDel gridded series was better overall performance than the GHCN series. Differences in altitude, latitude, and longitude were the main geographic factors determining the performance of the PET estimates using gridded climate series. Depending on the factors, some locations require bias correction, especially locations more than 10 km away from the grid point. The gridded datasets are an alternative for locations without climatic series data or with low-quality non-continuous data series. [ABSTRACT FROM AUTHOR]

Anthropogenic climate change is changing the Earth system processes that control the characteristics of natural hazards both globally and across Australia. Model projections of hazards under future climate change are necessary for effective adaptation. This paper presents BARPA-R (the Bureau of Meteorology Atmospheric Regional Projections for Australia), a regional climate model designed to downscale climate projections over the Australasian region with the purpose of investigating future hazards. BARPA-R, a limited-area model, has a 17 km horizontal grid spacing and makes use of the Met Office Unified Model (MetUM) atmospheric model and the Joint UK Land Environment Simulator (JULES) land surface model. To establish credibility and in compliance with the Coordinated Regional Climate Downscaling Experiment (CORDEX) experiment design, the BARPA-R framework has been used to downscale ERA5 reanalysis. Here, an assessment of this evaluation experiment is provided. Performance-based evaluation results are benchmarked against ERA5, with comparable performance between the free-running BARPA-R simulations and observationally constrained reanalysis interpreted as a good result. First, an examination of BARPA-R's representation of Australia's surface air temperature, precipitation, and 10 m winds finds good performance overall, with biases including a 1 ∘ C cold bias in daily maximum temperatures, reduced diurnal temperature range, and wet biases up to 25 mm per month in inland Australia. Recent trends in daily maximum temperatures are consistent with observational products, while trends in minimum temperatures show overestimated warming and trends in precipitation show underestimated wetting in northern Australia. Precipitation and temperature teleconnections are effectively represented in BARPA-R when present in the driving boundary conditions, while 10 m winds are improved over ERA5 in six out of eight of the Australian regions considered. Secondly, the paper considers the representation of large-scale atmospheric circulation features and weather systems. While generally well represented, convection-related features such as tropical cyclones, the South Pacific Convergence Zone (SPCZ), the Northwest Cloudband, and the monsoon westerlies show more divergence from observations and internal interannual variability than mid-latitude phenomena such as the westerly jets and extratropical cyclones. Having simulated a realistic Australasian climate, the BARPA-R framework will be used to downscale two climate change scenarios from seven CMIP6 global climate models (GCMs). [ABSTRACT FROM AUTHOR]

Atmospheric extreme events cause severe damage to human societies and ecosystems. The frequency and intensity of extremes and other associated events are continuously increasing due to climate change and global warming. The accurate prediction, characterization, and attribution of atmospheric extreme events is, therefore, a key research field in which many groups are currently working by applying different methodologies and computational tools. Machine learning and deep learning methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric extreme events. This paper reviews machine learning and deep learning approaches applied to the analysis, characterization, prediction, and attribution of the most important atmospheric extremes. A summary of the most used machine learning and deep learning techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. The critical literature review has been extended to extreme events related to rainfall and floods, heatwaves and extreme temperatures, droughts, severe weather events and fog, and low-visibility episodes. A case study focused on the analysis of extreme atmospheric temperature prediction with ML and DL techniques is also presented in the paper. Conclusions, perspectives, and outlooks on the field are finally drawn. [ABSTRACT FROM AUTHOR]

The environmental temperature and humidity are crucial factors for the normal growth and development of arbor tree cuttings by altering their hormone levels and influencing their physiological activities. Developing a temperature and humidity environmental model for arbor tree cuttings serves as a key technique to improve the adjustment performance of environmental parameters in facility agriculture systems and enhance the rooting rate of cuttings. This paper provides a comprehensive summary of current research on the inherent characteristics of cuttings and the factors influencing environmental temperature and humidity. It explores the mechanisms of interaction between the inherent characteristics of cuttings and the factors influencing environmental temperature and humidity. This paper investigates the interactive relationships among the factors affecting environmental temperature and humidity. It analyzes methods to improve the efficiency of constructing temperature and humidity environmental models for arbor tree cuttings. To enhance the transferability of the environmental model, the necessary physiological activities under the influence of plant hormones are generalized as common physiological traits in the growth and development of cuttings. In addition, this paper explores the factors influencing the air and substrate temperature and the humidity in facility agriculture systems as well as two types of facilities for controlling environmental temperature and humidity. Furthermore, it reviews the research progress in environmental models from both mechanistic and data-driven perspectives. This paper provides a comparative analysis of the characteristics associated with these two model categories. Building upon this, the paper summarizes and discusses methods employed in constructing temperature and humidity environmental models for arbor tree cuttings. In addition, it also anticipates the application of deep learning techniques in the construction of temperature and humidity environmental models for arbor cuttings, including utilizing machine vision technology to monitor their growth status. Finally, it proposes suggestions for building physiological models of fruit tree-like arbor cuttings at different growth stages. To enhance the transferability of environmental models, the integration of physiological models of cuttings, environmental models, and control system performance are suggested to create an environmental identification model. This paper aims to achieve control of the common physiological activities of cuttings. [ABSTRACT FROM AUTHOR]

A remarkable large-amplitude density modulation has been observed in ozone-mixed oxygen gas at atmospheric density and temperature conditions following irradiation with a nanosecond pulse of ultraviolet laser radiation. This paper outlines the experimental findings and describes a hydrodynamic simulation that reproduces several quantitative aspects of this phenomenon. It is important to note that these density waves are not simple sound waves; instead, they are combined with another type of density modulation wave. These density modulation wave profiles are expected to have applications for new optical components using a gas medium. [ABSTRACT FROM AUTHOR]

To adapt to Earth's rapidly changing climate, detailed modelling of thermal stress is needed. Dangerous stress levels are becoming more frequent, longer, and more severe. While traditional measurements of thermal stress have focused on air temperature and humidity, modern measures including radiation and wind speed are becoming widespread. However, projecting such indices has presented a challenging problem, due to the need for appropriate bias correction of multiple variables that vary on hourly timescales. In this paper, we aim to provide a detailed understanding of changing thermal stress patterns incorporating modern measurements, bias correction techniques, and hourly projections to assess the impact of climate change on thermal stress at human scales. To achieve these aims, we conduct a case study of projected thermal stress in central Hobart, Australia for 2040–2059, compared to the historical period 1990–2005. We present the first hourly metre-scale projections of thermal stress driven by multivariate bias-corrected data. We bias correct four variables from six dynamically downscaled General Circulation Models. These outputs drive the Solar and LongWave Environmental Irradiance Geometry model at metre scale, calculating mean radiant temperature and the Universal Thermal Climate Index. We demonstrate that multivariate bias correction can correct means on multiple time scales while accurately preserving mean seasonal trends. Changes in mean air temperature and UTCI by hour of the day and month of the year reveal diurnal and annual patterns in both temporal trends and model agreement. We present plots of future median stress values in the context of historical percentiles, revealing trends and patterns not evident in mean data. Our modelling illustrates a future Hobart that experiences higher and more consistent numbers of hours of heat stress arriving earlier in the year and extending further throughout the day. [ABSTRACT FROM AUTHOR]

In the traditional tobacco drying process, there is often a problem of uneven drying, which is closely related to drying conditions such as air velocity and temperature. To better understand the drying characteristics of tobacco, its drying kinetic performance were experimentally studied and predicted in this paper. In the drying experiment, the range of air temperature and velocity is 20–60℃ and 0.95–4.93 m/s, respectively. The results show that the effective diffusion coefficient increases with the increase of air temperature and decreases with the increase of air velocity. The effective moisture diffusivity( D eff ) ranges from 2.077 × 10–7 to 9.136 × 10–7 m2/s. Additionally, the activation energy (Ea) is between 14.292 and 21.032 kJ/mol according to Arrhenius law. Among the six commonly used empirical correlations, the logarithmic model has higher prediction accuracy, but it has a prediction deviation of more than 20% in the later stage of drying. Based on the logarithmic model and the two models, a new prediction model of tobacco drying characteristics was proposed with a maximum relative deviation error of less than 1%. [ABSTRACT FROM AUTHOR]

Few publications have considered the urban heat island (UHI) effect in small settlements. This paper, therefore, presents the findings of a UHI study conducted in the small, mid-latitude city of Inverness, Scotland (population: 63,220). The study aimed to provide an initial appraisal of the scale of UHI phenomena in Inverness and to understand the factors associated with its presence. Mobile vehicular traverses of a study transect were conducted on 30 dates during June, July & August 2019. Measurements of near-surface air temperature were recorded at 19 local climate zone observation points between 20:00 and 23:00 BST. Daily mean UHI intensity (UHII) during the study ranged from 0.7 to 3.5°C, with an overall mean UHII of 1.6°C. Land use characteristics impacted the UHII, with areas with higher fractions of impervious surface cover returning significantly higher air temperatures. UHII increased with the onset of sunset, and the highest UHII occurred on dates with up to 1–2 oktas of cloud cover, low relative humidity, and high cloud base height. The results align with previous studies and comparisons are drawn between Inverness and other settlements globally. Further research in Inverness is recommended to better understand UHI effects and influence national planning policy. [ABSTRACT FROM AUTHOR]

Abnormal changes in air temperature cause natural disasters such as droughts, hailstorms, and storms, thereby affecting the normal lives of human beings. Consequently, timely and accurate air temperature prediction is essential for human production and livelihood. Traditional air temperature prediction methods are less accurate and less consider the spatial relationship between air temperature in different regions. In this paper, we propose a new deep learning model, convolutional long short-term memory based on channel attention and spatial autocorrelation (ConvLSTM-CASA), which focuses on the spatial correlation between ambient air temperatures and can effectively capture the interaction of air temperatures in different regions. The results show that the ConvLSTM-CASA model has an average R2 of 0.954 and MSE of 5.245 for pentad-mean temperature prediction over the Yangtze River basin. Compared with baseline forecasting models, the MSE accuracy by the ConvLSTM-CASA model improved by 72.45%, 48.95%, 48.97%, 47.79%, and 22.63% over the decision tree regression (DTR), multiple linear regression (MLR), random forests (RF), long short-term memory (LSTM), and ConvLSTM models, respectively. The ConvLSTM-CASA model is expected to outperform the ConvLSTM model over 90% of the area, suggesting robust prediction skill improvement over space. The ConvLSTM-CASA model provides new insights for data-driven pentad-mean air temperature prediction by including elaborate channel and spatial feature modeling, which aid individuals in comprehending the intricate patterns of air temperature fluctuations. [ABSTRACT FROM AUTHOR]

The temperature profile is an important parameter of the atmospheric thermal state in atmospheric monitoring and weather forecasting. The hyperspectral infrared sounder of a geostationary satellite provides abundant spectral information and can retrieve the temperature profile. Based on the mediumwave channel data (independent variable and model input data) of FY-4A/GIIRS (geosynchronous interferometric infrared sounder) and ERA5 reanalysis data (dependent variable and model output data), the atmospheric temperature profile is retrieved by generalized ensemble learning. Firstly, the feature variables of the model are constructed. Because there are many GIIRS channels, a two-step feature selection method is adopted: step 1--establish a blacklist of GIIRS channels; step 2--select feature variables by using the method of importance permutation. Secondly, they are integrated based on optimizing and adjusting the hyperparameters of three basic machine learning models (Random Forest, XGBoost and LightGBM). Generalized ensemble learning nonlinear convex optimization is used to optimize the weight of each basic model. Finally, based on high-frequency GIIRS observations of Typhoon Lekima and Typhoon Higos, testing and method evaluation of the temperature profile retrievals are carried out. The results show that LightGBM achieves the best retrieval result among the three basic models, followed by Random Forest and finally XGBoost. The root-mean-square error of the whole temperature profile in the training dataset of generalized ensemble learning is less than 0.3 K, while that of the testing dataset is less than 1.4 K, and that between 150 hPa and 925 hPa is less than 1 K. The retrieval results correlate well with the radiosonde temperature profile. The performance of generalized ensemble learning is better than the performances of the three basic models, but it depends on the retrieval results of LightGBM. In the Lekima experimental case, compared to other channels selected for temperature retrieval models, the importance of mediumwave channels 9 and 307 of GIIRS ranks first and second, respectively. The method in this paper provides a new solution and technical support for retrieving atmospheric parameters from hyperspectral and other satellite data. [ABSTRACT FROM AUTHOR]

Negative air ions (NAIs) are crucial for assessing the impact of forests on wellbeing and enhancing the physical and mental health of individuals. They serve as pivotal indicators for assessing air quality. Comprehensive research into the distribution patterns of NAI concentrations, especially the correlation between NAI concentrations and meteorological elements in tourist environments, necessitates the accumulation of additional long-term monitoring data. In this paper, long-term on-site monitoring of NAI concentrations, air temperature, relative humidity, and other factors was conducted in real time over 24 h, from April 2020 to May 2022, to explore the temporal dynamic patterns of NAIs and their influencing factors. The results showed that (1) the daily dynamics of NAI concentrations followed a U-shaped curve. The peak concentrations usually occurred in the early morning (4:30–8:00) and evening (19:10–22:00), and the lowest concentrations usually occurred at noon (12:50–14:45). (2) At the monthly scale, NAI concentrations were relatively high in February, August, and September and low in January, June, and December. At the seasonal scale, NAI concentrations were significantly higher in winter than in other seasons, with higher concentrations occurring in the summer and autumn. (3) Relative humidity, air temperature, and air quality index (AQI) were the primary factors that influenced NAI concentrations. Relative humidity showed a significant positive correlation with NAI concentrations, while air temperature and AQI both exhibited a significant negative correlation with NAI concentrations. Higher air quality corresponds to higher NAI concentrations. Our research provides new insights into NAI temporal dynamics patterns and their driving factors, and it will aid in scheduling outdoor recreation and forest health activities. [ABSTRACT FROM AUTHOR]

Air temperature variability and prediction are important considering the potential implications on human health, water resource management, agriculture and land-atmosphere interaction. In this paper, we investigate the delayed influence of El Niño on spring Surface Air Temperature (SAT) variability over India using observations and assess its predictability in coupled climate models. Analysis suggests that El Niño, at its decaying phase, exerts a strong influence on the spring SAT over Southern Peninsular India (SPI). This warming is mainly induced by an anomalous anticyclone, which extends from the Western North Pacific (WNP) region to the SPI. The anomalous anticyclone-induced downdraft reduces the cloud cover and increases incoming shortwave radiation at surface, which in turn causes a dry atmospheric column and dry soil moisture over this region. As a result of this decreased soil moisture, latent heat flux into atmosphere reduces and enhances sensible heat transfer, further contributing to dry soil moisture. Both dry soil moisture and anomalous anticyclone contributes to enhanced SAT over SPI region. It is found that 6 years out of the top 10 warmest years over the SPI region correspond to the decaying phase of El Niño, suggesting the importance of the present study. It is also found that the intense SAT warming over SPI aids in increasing frequencies of Discomfort Index hours (exceeding 28 and 30 °C in a day) and the Universal Thermal Climate Index during El Niño decaying spring years. Further, we have examined the predictability of the spring SAT during decaying El Niño years based on the Asia-Pacific Economic Cooperation Climate Center (APCC) models. It is noted that most of the APCC models reasonably predicted the spatial distribution of warm SAT over India during the El Niño decaying spring season at 1 and 3 month lead. This study highlights the predictability of spring SAT a season in advance over India, which would help in planning better adaptation and mitigation strategies. [ABSTRACT FROM AUTHOR]

The sinter cooler, essential for cooling hot sintered ore to a specific temperature, has seen recent advancements with the introduction of a vertical sinter cooling furnace. This innovation aims to enhance energy efficiency, reduce emissions, and improve waste heat recovery. Despite significant research, a quantitative analysis of factors impacting its cooling and heat transfer efficiency is lacking. This study utilizes the Euler model and local non-equilibrium thermodynamic theory to identify key factors affecting the gas–solid cooperative cooling process in the vertical cooler. Through an orthogonal experimental approach, the paper determines the optimal structural and operational parameters for the furnace. Key findings include that a gas–solid ratio of 1200m^3/t, inlet air temperature of 50 ℃, cooling section height of 6m, and diameter of 13.25m maximize efficiency, achieving a weighted range normalization value of 0.962. This configuration meets sintered ore cooling requirements while optimizing waste heat recovery. The study reveals that the impact on heat transfer efficiency is influenced primarily by the gas–solid ratio, followed by the cooling section's height, diameter, and inlet air temperature. These insights are crucial for enhancing the vertical sinter cooler's design, contributing to more energy-efficient and environmentally friendly sintering processes. [ABSTRACT FROM AUTHOR]

The paper evaluates the usability of remote satellite-based and proximal ground-based agrometeorological data sources for precision agriculture and crop production in Croatia. The compared agrometeorological datasets stem from the open-access data sources Copernicus CDS and the Agri4Cast portal, and commercial in situ agrometeorological stations (PinovaMeteo) which monitor environmental parameters relevant to the physiological state of crops. The study compares relevant parameters for 10 different locations in Croatia for three consecutive years (2019, 2020, and 2021) to investigate whether model-based data from ERA5-Land and Agri4Cast are well-correlated with ground measurements from independent in situ stations (PinovaMeteo) for specific agrometeorological parameters (air and soil temperature, and precipitation). Our results indicate the following: both the ERA5-Land and Agri4Cast datasets show mostly strong positive correlations with ground observations for air temperature, modest correlations for soil temperature, but modest or even low correlations for precipitation. Analysis of the residuals indicates higher overall residual values, especially in areas with complex topography and near large bodies of water or the sea, and deviations of residuals that may limit the usability of satellite- and model-based data for decision-making in agriculture. [ABSTRACT FROM AUTHOR]

Aiming to reduce the energetic consumption and greenhouse emissions, integrating the energy-intensive dryer to a solar loop constitutes the optimum solution. This paper investigates the feasibility to integrate a bench scale flash dryer to a solar heating source. Initially, the choice of the best heating source was made and the design was realized. Then, the best configuration of the gas/liquid heat exchanger was selected to design the exchange surface. Finally, the solar flash dryer was mathematically modeled to simulate the dryer performances at various conditions. The parabolic through collectors were selected, with a nominal power of 29kWth, were coupled to a spiral finned tube exchanger with 24 kW capacity. The integration showed a significant dependency of the drying air temperature on the climatic conditions (DNI and incidence angle). The drying tests, realized at the most favorable and unfavorable conditions, showed an important decrease in the moisture fraction to be eliminated, increasing from 1.8% to 3.5% when the air temperature drops from 155 °C to 110 °C. It was hence necessary to develop a regulation system to ensure a continuous solid flow rate. [ABSTRACT FROM AUTHOR]

• A direct evaporative cooler (DEC) integrates with split air conditioner (SAC). • DECSAC significantly improves C O P by 10.54 %−70.14 %. • Total cost rate (T C R) of DECSAC reduces by 5.07 %−25.36 %. • Water consumption for 1 kWh energy saving varies between 5.5 – 9.8 L. • Simple payback period lies between 1.21 to 2.99 years with maximum I R R being 59 %. As concerns regarding energy consumption and environmental impact continue to grow, the need for efficient and sustainable cooling technologies becomes increasingly significant. Among various alternatives, direct evaporative cooling (DEC) has emerged as a promising solution due to its energy-efficient operation and low environmental footprint. This paper aims to provide a comprehensive thermo-economic assessment of a 5.25 kW capacity split air conditioner (SAC) integrated with direct evaporative cooling, focusing on evaluating its performance, energy consumption, coefficient of performance (C O P) , and economic viability. A numerical model is developed to determine the reduction in the condenser inlet air temperature due to direct evaporative cooling. The ambient temperature (30–45 ⁰C), relative humidity (20–80 %), and evaporator temperature (3–12 ⁰C) are taken as the design variables. Box–Behnken design (BBD) technique of response surface methodology is applied for multi-objective optimization. The C O P , total cost rate (T C R) , and total exergy destruction ( E ˙ D , t ) are set as the objective functions. Cooling capacity, life period, operation time, interest rate, maintenance factor, and electricity cost are taken as constants. The thermoeconomic optimization evolved maximized C O P enhancement of 68.94 %, and reduction in TCR and E D , t by 26.12 % and 57.23 %, respectively. The water consumption to energy saving ratio varies from 5.5 to 9.8 L/kWh for different months of the cooling season. The thermoeconomic performance and sustainability of DECSAC is observed to significantly improve compared to conventional SAC specifically in hot-dry climates. The simple payback of the proposed system ranges between 1.21 to 2.99 years depending upon the operating conditions. [ABSTRACT FROM AUTHOR]

Research on spatio-temporal geostatistical modeling remains a critical challenge in numerous scientific and engineering disciplines. This paper introduces a novel extension of dual kriging, called spatio-temporal dual kriging (ST-DK), in which drift functions with fixed and adaptive coefficients are established. The approach appears to be effective in modeling complex spatio-temporal dynamics, particularly when relevant auxiliary variables exert substantial influence on the target variable. To illustrate its performance, we compare the ST-DK model with the classical spatio-temporal regression kriging (ST-RK) and geographically and temporally weighted regression (GTWR) models for estimating temperature and air pressure data from Thailand in 2018. Our findings demonstrate that both the ST-DK and ST-RK models when utilizing adaptive coefficients outperform their fixed coefficient counterparts. Furthermore, the ST-DK method consistently exhibits superior performance compared to the ST-RK and GTWR methods. [ABSTRACT FROM AUTHOR]

This paper experimentally analyses an indoor environment assessment of a large group of respondents regarding their subjective perception of overall comfort, indoor air quality and humidity. The questionnaire survey was applied as a testing method together with measurements of the physical parameters conducted with a microclimate meter. Two types of environment were analysed: educational rooms and the climate chamber. The comparative analysis of the sensations experienced within them indicates that they generate quite similar responses; however, some discrepancies have been identified. The overall comfort of the climate chamber was typically assessed as being higher than that of the educational rooms at the same air temperature. The most favourable air temperature in the climate chamber was ca. 20.7 °C, while in the educational rooms it was ca. 22.3 °C. The most preferable conditions in the climate chamber occurred at a thermal sensation vote of −0.4 ("pleasantly slightly cool"), while in the educational rooms it occurred at +0.2 ("neutral/pleasantly slightly warm"). Quite strong correlations between overall comfort and indoor air quality as well as between humidity assessment and humidity preference votes were observed, which did not seem to depend on the type of environment. These findings are important because results from the simulated conditions are often used in the analyses of actual living/working environments. [ABSTRACT FROM AUTHOR]

The morphological complexity of urban environments results in a high spatial and temporal variability of the urban microclimate. The consequent demand for highly-resolution atmospheric data remains a challenge for atmospheric research and operational application. The recent widespread availability and increasing adoption of low-cost mobile sensing offers the opportunity to integrate observations from conventional monitoring networks with microclimatic and air pollution data at a finer spatial and temporal scale. So far, the relatively low quality of the measurements and outdoor performance compared to conventional instrumentation has discouraged the full deployment of mobile sensors for routine monitoring. The present study addresses the performance of a commercial mobile sensor, the MeteoTracker (IoTopon Srl), recently launched on the market to quantify the microclimatic characteristics of the outdoor environment. The sensor follows the philosophy of the Internet of Things technology, being low cost, having an automatic data flow via personal smartphones and online data sharing, supporting user-friendly software, and having the potential to be deployed in large quantities. In this paper, the outdoor performance is evaluated through tests aimed at quantifying (i) the intra-sensor variability under similar atmospheric conditions and (ii) the outdoor accuracy compared to a reference weather station under sub-optimal (in fixed location) and optimal (mobile) sensor usage. Data-driven corrections are developed and successfully applied to improve the MeteoTracker data quality. In particular, a recursive method for the simultaneous improvement of relative humidity, dew point, and humidex index proves crucial for increasing the data quality. The resultsmark an intra-sensor variability in the range of±0.5℃ for air temperature and±1.2%for the corrected relative humidity, both within the declared sensor accuracy. The sensor captures the same atmospheric variability as the reference sensor during both fixed and mobile tests, showing positive biases (overestimation) for both variables. Through the mobile test, the outdoor accuracy is observed between ±0.3℃ to ±0.5℃ for air temperature, between ±3% and ±5% for the relative humidity, ranking the MeteoTracker in the real accuracy range of similar commercial sensors from the literature and making it a valid solution for atmospheric monitoring. [ABSTRACT FROM AUTHOR]

Energy has an effective role in economic growth and development of societies. This paper is studying the impact of climate factors on performance of solar power plant using machine learning techniques for underlying relationship among factors that impact solar energy production and for forecasting monthly energy production. In this context this work provides two machine learning methods: ANN for forecasting energy production and decision tree useful in understanding the relationships in energy production data. Both structures have horizontal irradiation, sunlight duration, average monthly air temperature, average maximal air temperature, average minimal air temperature and average monthly wind speed as inputs parameters and the energy production as output. Results have shown that used machine learning models perform effectively, ANN predicted the energy production of the PV power plant with a correlation coefficient higher than 0.97. The results can help stakeholders in determining energy policy planning in order to overcome uncertainties associated with renewable energy resources. [ABSTRACT FROM AUTHOR]

In this paper, desorption isotherms of two jerky products were studied (whole-muscle jerky -- sample 1 and minced jerky -- sample 2). The work focused on the comparison of the Dynamic Dewpoint Isotherm (DDI) method and the Saturated Salt Slurry (SSS) method and testing the newly applied model for modelling desorption isotherms for dried meat. Data were statistically processed using 8 models [Guggenheim-Anderson-de Boer (GAB), Double Log Polynomial (DLP), Henderson, Chin, Smith, Oswin, Hasley, and newly applied model] and statistically evaluated using coefficient of determination (R2), root mean squared error (RMSE) and mean relative percentage deviation (P-value). The DLP model (25 °C) reached R² ≥ 0.999, P-value ≤ 1.84 for DDI, and R2 ≥ 0.998, P-value ≤ 4.37 for SSS method. The GAB model reached R² ≥ 0.997, P-value ≤ 2.58 for DDI, and for SSS method the GAB model reached R² ≥ 0.998, P-value ≤ 5.47. The new model reached P-value ≤ 5.73 for DDI and P-value ≤ 3.48 for SSS method. All models reached the P-value < 10% except for Smith and Chin models. The DDI method and newly applied model prove to be a suitable and precise approach to the evaluation of isotherms of dried meat products. [ABSTRACT FROM AUTHOR]

The research presented in this paper aims at providing a statistical model that is capable of estimating soil water content based on weather data. The model was tested using a long-time series of field experimental data from continuous monitoring at a test site in Oltrepò Pavese (northern Italy). An innovative statistical function was developed in order to predict the evolution of soil–water content from precipitation and air temperature. The data were analysed in a framework of robust statistics by using a combination of robust parametric and non-parametric models. Specifically, a statistical model, which includes the typical seasonal trend of field data, has been set up. The proposed model showed that relevant features present in the field of experimental data can be obtained and correctly described for predictive purposes. [ABSTRACT FROM AUTHOR]

Due to the increasing demand of climate change studies, traceability of measurements according to the International System of Units (SI) becomes fundamental to establish data quality and comparability in time. With the aim of guaranteeing the traceability, in October 2022, an on-site calibration of three thermometers operated by the Osservatorio meteorologico di Moncalieri was assessed. The calibration by comparison against traceable travelling standards was performed in a transportable thermal chamber manufactured by the Istituto Nazionale di Ricerca Metrologica (INRIM). Results showed that the main uncertainty contributions to the expanded uncertainty were the interpolation method, the chamber inhomogeneity and the repeatability of the sensors under calibration. This current calibration was compared with previous calibration in 2012 (on-site calibration) and in 2016 (laboratory calibration). The comparison analysis evidenced the drift effect on the thermometers and the importance of having an active calibration program to reduce this effect. Regarding the expanded uncertainty, both on-site calibrations presented the same order of magnitude and smaller than the correction values. This paper points out the advantage of performing an on-site calibration: since the calibration involves the same dataloggers and cables in the same environmental conditions, the calculated calibration curve represents more convincingly the real measurement conditions. [ABSTRACT FROM AUTHOR]

The mobile ocean weather observation system, named Marine Weather Observer (MWO), developed by the Institute of Atmospheric Physics (IAP), consists of a fully solar-powered, unoccupied vehicle and meteorological and hydrological instruments. One of the MWOs completed a long-term continuous observation, actively approaching the center of Typhoon Sinlaku from 24 July to 2 August 2020, over the South China Sea. The in situ and high-temporal-resolution (1 min) observations obtained from MWO were analyzed and evaluated through comparison with the observations made by two types of buoys during the evolution of Typhoon Sinlaku. First, the air pressure and wind speed measured by MWO are in good agreement with those measured by the buoys before the typhoon, reflecting the equivalent measurement capabilities of the two methods under normal sea conditions. The sea surface temperature (SST) between MWO and the mooring buoys is highly consistent throughout the observation period, indicating the high stability and accuracy of SST measurements from MWO during the typhoon evolution. The air temperature and relative humidity measured by MWO have significant diurnal variations, generally lower than those measured by the buoys, which may be related to the mounting height and sensitivity of sensors. When actively approaching the typhoon center, the air pressure from MWO can reflect some drastic and subtle changes, such as a sudden drop to 980 hPa, which is difficult to obtain by other observation methods. As a mobile meteorological and oceanographic observation station, MWO has shown its unique advantages over traditional observation methods, and the results preliminarily demonstrate the reliable observation capability of MWO in this paper. [ABSTRACT FROM AUTHOR]

In this work we analyse the impact of including a regional, high-resolution ocean model on simulated atmospheric climate in a coupled earth system model. The resolution of the regional, nested ocean model is approximately 0.2° compared to the ~1° resolution of the global ocean model within which it is embedded and this work complements previously published work on ocean circulation and marine heatwaves using this setup, referred to as the New Zealand Earth System Model, NZESM. After a brief discussion of the wider model setup, the persistent Southern Ocean warm bias in climate models and the validation data sets used, we show the effects of the altered ocean physics on air temperature, precipitation and evaporation, latent and sensible surface heat balances, westerly winds, the storm track and the effect on total cloud amount. Overall we find that the NZESM provides a better representation of regional atmospheric climate compared to its parent model - UKESM1 - although this improvement is not universal. For example, although the NZESM shows better agreement in surface air temperature within the nested ocean region, there is also some deterioration in the agreement at higher southern latitudes where the seasonal sea ice edge coincides with a transition from negative to positive correlation between air temperature and cloud amount. The lack of additional model tuning in the NZESM after the nested ocean model's inclusion largely accounts for the presence of these improvement-deterioration pairs with respect to observations. The reader is encouraged to read the paper of Behrens et al. (Behrens et al, 2020) before this one since it provides much additional information which will aid understanding. This study aims to provide a high-level reference ontology for how changing one aspect of the ocean physics in a coupled model can impact simulated atmospheric climate. [ABSTRACT FROM AUTHOR]

The Arctic climate is influenced by the interaction of shortwave (SW) and longwave (LW) radiation with the atmosphere and the surface. The comprehensive evolution of the Surface Radiative Fluxes (SRF) on different time scales is of paramount importance to understanding the complex mechanisms governing the Arctic climate. However, only a few sites located in the Arctic region provide long-term time series of SRF allowing for capturing of the seasonality of atmospheric and surface parameters and for carrying out validation of satellite products and/or reanalyses. This paper presents the daily and monthly SRF record collected at the Thule High Arctic Atmospheric Observatory (THAAO, 76.5 ∘ N, 68.8 ∘ W), in North-Western Greenland. The downwelling components of the SW (DSI) and the LW (DLI) irradiances have been measured at THAAO since 2009, whereas the collection of the upwelling SW (USI) and LW (ULI) irradiance was started in 2016, together with additional measurements, such as meteorological parameters and column water vapour. The datasets of DSI (Meloni et al., 2022a; 10.13127/thaao/dsi), USI (Meloni et al., 2022b; 10.13127/thaao/usi), DLI (Meloni et al., 2022c; 10.13127/thaao/dli), ULI (Meloni et al., 2022d; 10.13127/thaao/uli), and near-surface air temperature (Muscari et al., 2018; 10.13127/thaao/met), can be accessed through the THAAO web site (https://www.thuleatmos-it.it/data , last access: 16 January 2024). The DSI is absent (solar zenith angle ≥90 ∘) from 29 October to 13 February, assuming maxima in June (monthly mean of 277.0 Wm -2), when it is about half of the total incoming irradiance. The USI maximum occurs in May (132.4 Wm -2) owing to the combination of moderately high DSI values and high albedo. The shortwave surface albedo (A) assumes an average of 0.16 during summer, when the surface is free of snow. Differently, during months of snow-covered surface, when solar radiation allows A to be estimated, its values are greater than 0.6. A large interannual variability is observed in May and September, months characterized by rapidly changing surface conditions, having a link with air temperature anomalies. The DLI and ULI maxima occur in July and August, and the minima in February and March. ULI is always larger than DLI and shows a wider annual cycle. ULI is well described by a fourth-order polynomial fit to the air temperature (R2>0.99 for monthly data and R2>0.97 for daily data). The Surface Radiation Budget (SRB) is positive from April to August, when absorption of solar radiation exceeds the infrared net cooling, with a maximum value of 153.2 Wm -2 in June. From November to February, during the polar night, the LW net flux varies between -34.5 and -43.0 Wm -2. In March and September, the negative LW net flux overcomes the positive SW contribution, producing a negative SRB. The THAAO measurements show clear evidence of the influence of several regional weather/climate events, that appear strongly linked with SRF anomalies. These anomalies were found, for example, during summer 2012, when a large ice melting event took place over Greenland, and during winter 2019–2020, which was extraordinarily cold in the Arctic region. [ABSTRACT FROM AUTHOR]

This paper is devoted to the analysis of air temperature and humidity changes during the two solar eclipses of 26 December 2019 and 21 June 2020 in Saudi Arabia based on data we collected from two different sites. We highlight the complexity of humidity's response to a solar eclipse, which is quite different from temperature's response. During the December event, the Sun rose already partially eclipsed, while for the June eclipse, it was only partial at Riyadh. This difference apparently affected the observed response on the recorded variables: temperature, relative humidity (RH), and vapor pressure (VP) in the two events. Changes in these variables went unnoticed for the first eclipse since they were within the natural variability of the day; yet for the other, they showed evident alterations in the slopes of the major parameters, which we analyze and discuss. A decrease in temperature of 3.2 °C was detected in Riyadh. However, RH and VP showed an oscillation that we explain taking into account a similar effect reported in other eclipses. We measured a time lag of about 15 min from the eclipse central phase in the city. Related fluctuations and dynamics from the computed rates of the temporal variation of temperature and RH are scrutinized. Furthermore, an overdue significant review of terrestrial atmospheric parameters is also offered in the context of the eclipse meteorology, particularly related to desert atmospheres. We also try to identify the influence of solar eclipses in similar environments doing a broad inter-comparison with other observations of these variables in the Near East, northern Africa, and in the United States. These inter-comparisons reveal how complex and dissimilar the response of the lower atmosphere to a solar eclipse can be within a desert environment and other similar environments. [ABSTRACT FROM AUTHOR]

Previous studies have reported significant uncertainty in the relationship between El Niño-Southern Oscillation (ENSO) and the winter surface air temperature (SAT) over central North America (NA). This paper uses reanalysis data and statistical methods to investigate the possible modulation effect of the Victoria mode (VM) during the preceding spring on the winter ENSO–SAT relationship. The correlation between ENSO and the winter SAT over central NA was found to be significantly positive during the positive VM phase. However, this significant correlation is almost absent during the negative VM phase. Further analysis revealed that the zonal location of the sea surface temperature anomalies (SSTA) over the tropical Pacific is an essential aspect of this VM modulation. During the positive VM phase, the dominant positive SSTA in the central-eastern tropical Pacific contribute to the formation of the negative phase of the North Pacific Oscillation (NPO)-like teleconnection, which further enhances the anomalous southerly winds and SAT over central NA. In contrast, during the negative VM phase, the Aleutian low-like atmospheric response induced by the central tropical Pacific SSTA is limited to the central North Pacific, which has a limited effect on the variation of the central NA SAT. The VM modulation mechanism is shown to be well reproduced by historical simulations of the Coupled Model Intercomparison Project Phase 6. Our results suggest that, despite the uncertainty associated with the ENSO–SAT connection, ENSO may provide prediction skills for the winter SAT over central NA during the positive VM phase. [ABSTRACT FROM AUTHOR]

Nitrogen heterocyclic organics are promising hydrogen storage carriers with the advantage of being capable of large-scale transportation over long distances using existing oil and gas facilities. However, traditional methods for the hydrogenation and dehydrogenation of organic hydrogen carriers usually require high temperature and pressure conditions and external hydrogen supplies, which hinder their large-scale applications, as well as having certain safety risks. In this paper, a reversible electrochemical hydrogen storage system using quinoxaline as a hydrogen carrier was developed with potential for use at room temperature and atmospheric pressure. Experiments revealed that the hydrogenation conversion of quinoxaline reached 95% within 120 min at −0.20 V vs. RHE, and the dehydrogenation conversion of 1,2,3,4-tetrahydroquinoxaline reached 100% within 30 min at 1.30 V vs. RHE. This means that the system achieved quick and efficient reversible hydrogen storage. The Pd/NF dual-function electrode prepared by spontaneous redox reaction still showed high catalytic activity after 8 cycles, indicating its good long-term stability. The proton donor in the electrochemical hydrogenation process of quinoxaline was water, which completely eliminates the safety risk from the use of external hydrogen. This work provides a simple and reliable strategy for efficient hydrogen storage using nitrogen heterocyclic organics. [Display omitted] • The hydrogenation conversion of quinoxaline reached 95%. • The optimum dehydrogenation conversion of 1,2,3,4-tetrahydroquinoxaline was 100%. • Pd/NF electrode was operated as a dual-function electrocatalyst. • Efficient reversible hydrogen storage was achieved at 25 °C and atmospheric pressure. • The H∗ required for the electrochemical hydrogenation of quinoxaline came from H 2 O. [ABSTRACT FROM AUTHOR]

• A numerical model of a two-stroke marine diesel engine was developed and validated. • The effects of start of injection and scavenge air inlet temperature on exergy terms were investigated. • The exergy efficiency decreases from 43.45% to 41.36% as start of injection delays. • The exergy efficiency reduces from 42.83% to 41.44% as scavenge air inlet temperature increases. • The optimal strategies for minimizing exergy destruction and maximizing exergy. This paper presents a numerical approach of the influence of operational parameters on the exergy terms in a two-stroke marine diesel engine. Operational parameters are introduced as different start of injection (-4, -2, 0 and 2 °CA) and scavenge air inlet temperature (300, 312, 330 and 340 K) values. The energy analysis is conducted within a closed cycle by using a three-dimensional computational fluid dynamics code. The simulation results are compared with the corresponding experimental pressure data in the literature and show good agreement. Also, the exergy analysis is performed by a developed in-house computational code. Various exergy rates and cumulative exergies are detected and computed separately at operational parameters. The study reveals that varying the start of injection timing from −4 to 2 °CA at full load condition results in a decrease in exergy efficiency from 43.45 % to 41.36 %. Furthermore, an increase in scavenge air inlet temperature from 300 K to 340 K leads to a reduction in exergy efficiency from 42.83 % to 41.44 %. These findings underscore the critical influence of injection timing and scavenge air temperature on the exergy performance. In light of these results, this paper gains more insight into the impact of design parameters of a two-stroke marine diesel engine on exergy efficiency and presents a preliminary data for marine diesel engine manufacturers in the design of a two-stroke marine diesel engine that uses energy efficiently. [ABSTRACT FROM AUTHOR]

In recent years, electrochemical conversion of nitrogen to ammonia at low temperature and atmospheric pressure has become a hot research topic because of its advantages of low carbon emission, low energy consumption and high energy transfer rate. The development of catalysts with high catalytic activity and flexibility are an important topic in the current nitrogen reduction reaction. In this paper, CNT@MoS 2 composite catalysts were prepared by catalytic pyrolysis of biomass to prepare one-dimensional carbon nanotubes and then combined with MoS 2 by a hydro thermal method. It was found that 120 CNT@MoS 2 had better NRR activity and selectivity, NH 3 yield rate of 21.2 μg h−1 mg−1 was achieved at −0.44 V vs. The Faraday efficiency can reach 5.4%. The catalytic performance is more than 3 times higher than CNT. The mechanism of MoS 2 -catalyzed NRR was further investigated by DFT theoretical calculations. The results of the theoretical calculations are in good agreement with the experimental results, providing a reasonable explanation for the high reduction performance of the catalyst. This study offered a promising avenue for exploring and rationalizing the design of electrocatalytic materials for NRR. [Display omitted] [ABSTRACT FROM AUTHOR]

• Aluminum optical fiber accurately represents air temperature in radiative environments. • The use of optical fiber enables detailed reconstruction of air temperature. • 3D air temperature monitoring over prolonged durations. • The non-intrusive nature of thin optical fibers for air temperature measurement. Air temperature measurement is important in fields such as meteorology, comfort, thermal conditioning, and numerical model validation. However, in highly radiant environments, thermal radiation is a challenge and can lead to significant errors in air temperature measurements. Current approaches, including reducing sensor size or surface emissivity, using radiation shields, and applying correction models, partially address this issue but fail to completely suppress the errors caused by infrared radiation. In this paper, we propose a novel measurement method based on Raman scattering using thin aluminum-coated optical fibers. This method offers significant advantages over traditional sensors in highly radiant environments and provides more representative air temperature measurements. We demonstrate the effectiveness of this approach through comparative analysis and experimental results. An in-situ measurement using these optical fibers was carried out to reconstruct the 3D temperature distribution in a full-scale room specifically designed for thermo-aeraulic tests. [ABSTRACT FROM AUTHOR]

• Sand wedge deposits were identified in the Early Miocene Wuqia Group. • Sand wedges occurred in braided fluvial environment under arid and cold climate. • The northern Tibetan Plateau surpassed 1000 m elevation in the Early Miocene. The uplift of the Tibet Plateau and its climatic effects have long been a hot topic in academic research. However, there is still significant controversy regarding the uplift history and related climate changes along the northern margin of the plateau. In this paper, we present sand wedge deposits from the base of the Wuqia Group (∼20 Ma) on the southwestern Tarim Basin, representing the oldest reported sand wedges in the Tibetan Plateau. A comprehensive study was conducted on lithostratigraphy, sedimentary environment, and macroscopic–microscopic features of the sand wedge deposits. The results indicate that the lithostratigraphic succession preserving the sand wedge deposits consists mainly of lenticular conglomerates and cross-bedded gravelly sandstone interbedded with medium–thin-bedded mudstone, consistent with the braided fluvial environment. The sand wedge deposits commonly exhibit a V-shape geometry and are filled with gravelly sandstone, with the gravels arranged parallel to the walls of the wedge. The clasts of the wedge-filled sandstone are well-rounded and show a unimodal grain size distribution. The quartz surfaces commonly display dish-shaped and crescent-shaped impact features, indicating an eolian origin and an arid climate. Based on the relationship between the formation temperature of modern sand wedges, the atmospheric temperature gradient, and altitude, the reconstructed paleo-elevation of the sand wedge deposits at the base of the Wuqia Group in the southwestern Tarim Basin is approximately ∼ 1000 m. These results indicate that the northern margin of the Tibet Plateau had already reached an elevation of ∼ 1000 m during the Early Miocene, forming a seasonal arid and cold climate. [ABSTRACT FROM AUTHOR]

Using quarterly temperature and employment data between 1990 and 2021, this paper uncovers nuanced evidence on the impact of seasonal temperature within US counties: higher winter temperature increases private sector employment growth while higher summer temperature decreases it. The impacts of higher temperature in milder seasons, fall and spring, are statistically insignificant. Moreover, the negative impact of higher summer temperature persists while the positive impact of higher temperature in the winter is more short-lived. The negative effects of a hotter summer are pervasive and persistent in many sectors: most significantly in "Construction" and "Leisure and Hospitality" but also in "Trade, Transport, and Utilities" and "Financial Activities". In contrast, the positive effects of a warmer winter are less pervasive. The employment effect of a hotter summer has been more severe in recent decades. [ABSTRACT FROM AUTHOR]