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Atmospheric rivers (ARs), responsible for extreme weather conditions, are mid-latitude systems that can cause significant damage to coastal areas. While forecasting ARs beyond two weeks remains a challenge, past research suggests potential benefits may come from properly accounting for the changes in sea surface temperature (SST) through air–sea interactions. In this paper, we investigate the impact of ARs on SST over the North Pacific by analyzing 25 years of ocean reanalysis data using an SST budget equation. We show that in the region of strong ocean modification, ocean dynamics can offset over 100% of the anomalous SST warming that would otherwise arise from atmospheric forcing. Among all ocean processes, ageostrophic advection and vertical mixing (diffusion and entrainment) are the most important factors in modifying the SST tendency response. The SST tendency response to ARs varies spatially. For example, in coastal California, the driver of enhanced SST warming is the reduction in ageostrophic advection due to anomalous southerly winds. Moreover, there is a large region where the SST shows a warming response to ARs due to the overall reduction in the total clouds and subsequent increase in total incoming shortwave radiation. Strong air-sea interactions during atmospheric rivers often lead to modest upper ocean heat changes. The authors show that interior ocean dynamics are compensating for these air-sea exchanges. These findings can help improve subseasonal forecasts. [ABSTRACT FROM AUTHOR]

This paper considers subsonic turbojet aircraft fuel consumption minimization problem during cruise phase, assuming fixed time of arrival. The problem takes into account real atmosphere data. We utilize tailwind/headwind component values at various flight levels, as well as air temperatures and atmospheric pressures at various altitudes. The solution to the altitude and speed flight profile optimization problem is through constrained coordinate descent method. The paper considers optimizing the fuel consumption of a medium-haul aircraft during the cruise phase using sample data set on temperature, pressure, and wind speed. The proposed approach achieves a decrease in fuel consumption of 1.2% when optimizing with regard to real atmosphere. [ABSTRACT FROM AUTHOR]

Bangladesh is one of the world's most susceptible countries to climate change. Global warming has significantly increased surface temperatures worldwide, including in Bangladesh. According to meteorological observations, the average temperature of the world has risen approximately 1.2 °C to 1.3 °C over the last century. Researchers and decision-makers have recently paid attention into the climate change studies. Climate models are used extensively throughout the nation in studies on global climate change to determine future estimates and uncertainties. This paper outlines a perceptible stacking ensemble learning model to estimate the temperature of a tropical region—Cox's Bazar, Bangladesh. The next day's temperature, maximum temperature, and minimum temperature are estimated based on the daily weather database collected from the weather station of Cox's Bazar for a period of 20 years between 2001 and 2021. Five machine learning (ML) models, namely linear regression (LR), ridge, support vector regression (SVR), random forest (RF), and light gradient boosting machine (LGBM) are selected out of twelve ML models and combined to integrate the outputs of each model to attain the desired predictive performance. Different statistical schemes based on time-lag values play a significant role in the feature engineering stage. Evaluation metrics like mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) are determined to compare the predictive performance of the models. The findings imply that the stacking approach presented in this paper prevails over the standalone models. Specifically, the study reached the highest attainable R2 values (0.925, 0.736, and 0.965) for forecasting temperature, maximum temperature, and minimum temperature. The statistical test and trend analysis provide additional evidence of the excellent performance of the suggested model. [ABSTRACT FROM AUTHOR]

The Wet bulb globe temperature (WBGT) index is a widely used tool for estimating human heat stress. It is calculated from three measured parameters: air temperature, natural wet bulb temperature and globe temperature. Very few papers have been published on the measurement uncertainty of this index. The uncertainty estimation has to account for correlations between the input quantities i.e. the three temperatures comprising the index. In the present short paper, three example measurements, under different conditions, were made to illustrate the calculation of the expanded uncertainty of the WBGT index. The analysis presented here complements the earlier work published in the International Journal of Thermophysics (Ekici in Int J Thermophys 38:85, 2017) and refers to the same type of measurement instruments. [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]

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]

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]

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]

In the last years, the Mediterranean basin has been widely recognized as one of the most vulnerable areas in the world to climate change; because of its high concentration of urban and industrial settlements, it is one of the most impacted areas of the world in terms of water scarcity. The present paper aims at reviewing the main observed and predicted effects of climate change on hydrological processes directly related to water availability in the Mediterranean Basin, mainly focusing on the last ten years of research. While an in-depth discussion about possible future water scarcity problem in the Mediterranean area and the sources of uncertainty affecting future climate projections and impact assessments is presented in a companion paper (Noto et al., 2022), this study highlights how most of the more recent studies for the Mediterranean region are concordant and recognize a general increasing future trajectory in both the mean and extreme values of air temperatures. On the contrary, there is much less agreement about the intensity and directions of future projections for other variables, such as precipitation, evapotranspiration, and runoff, whose changes are less evident and variable in space. [ABSTRACT FROM AUTHOR]

Persistent droughts are known to induce stress on agricultural crops and forest ecosystems. Severe long-lasting droughts lead to food insecurity. With rising air temperatures and declining precipitation totals, there is often a presumption that droughts are becoming more severe. To understand the complexity of interrelated physical processes on various temporal scales, it is important to analyse trends and mutual relationships between drought-related climatic variables. In this paper, we analyse monthly time series of near-surface air temperature, precipitation totals, potential evapotranspiration, climatic water balance, and two meteorological drought indices: the standardised precipitation index (SPI) and the standardised precipitation-evapotranspiration index (SPEI), at a total of 71 locations located within the western Carpathian Mountains (Slovakia) and the adjacent northern part of the Pannonian Plain. The analysed data cover the time period 1981–2019. Quantile regression was used to determine the slopes of temporal trends in the investigated climate variables. The mutual relationships between precipitation and air temperature were analysed on a seasonal basis. Negative coupling (~ − 5.1%/°C) between precipitation and air temperature was observed in the spring season. Positive coupling between precipitation and air temperature was observed in the remaining part of the year. Spatio-temporal analyses of SPEI revealed intensification of meteorological drought in the spring season that continues into summer at most of the analysed locations. The negative temporal trends in the SPEI suggest that the severity of meteorological drought has increased over the investigated period. [ABSTRACT FROM AUTHOR]

For the outside cabin heat exchanger of wind power generation, the overall numerical simulation plays an important role in its design and optimization. The periodic iterative simulation method proposed in this paper makes up for the deficiency of analogy research using the local model in the previous research of plate-fin heat exchanger so that the research conclusions can be directly applied to practical engineering. The effectiveness of this method is verified by comparing 10 groups of simulation results with experimental results. It is concluded that the maximum relative error of the periodic iterative simulation method is 0.9%, which meets the requirements of engineering calculation. The number of iterations can be reduced to two when only the outlet average temperature is involved, which is more convenient than the dozens of iterations. Furthermore, the effects of altitude, air velocity, air temperature, wave fin structure parameters, and generator power on the performance of heat exchanger are comprehensively analyzed. The results show that larger naturally captured air volume, lower ambient temperature, and lower altitude can effectively reduce the outlet temperature of ethylene glycol solution. When the amplitude and wavelength of the corrugated fin on the airside of the heat exchanger are 0.6 mm and 20 mm respectively, the naturally captured air volume is effectively increased, the overall heat exchange performance of the heat exchanger is further improved. [ABSTRACT FROM AUTHOR]

The growing relevance of the use of unmanned aerial vehicles (UAVs) for studying turbulence in the atmospheric boundary layer (ABL) is associated with the need to obtain new observational data at different heights in the boundary layer, as well as over heterogeneous landscapes. Such new data is needed for developing turbulence models for spatially inhomogeneous and unsteady conditions. This paper describes the new Tsimlyanin UAV and its meteorological payload, developed specifically for studying the turbulent structure of the ABL. Tsimlyanin differs from similar vehicles by its hybrid scheme, which combines the possibility of vertical take-off and landing and horizontal flight in airplane mode. The meteorological payload includes specially designed measuring devices, namely, a multihole air pressure probe and a low-inertia resistance thermometer. Also, analog-to-digital converters for several sensors, as well as the onboard data-acquisition system, are newly developed. This paper presents the results of measurements carried out using the UAV during test flights in Tsimlyansk in August 2020. A good agreement between UAV observations with respect to vertical profiles of air temperature, wind speed and direction, dispersion of vertical speed with the data of observations of other complexes (acoustic anemometers, an automatic meteorological station, sodars, and a temperature profiler) was obtained. The results demonstrate the high potential of this UAV for studying turbulence in the ABL. [ABSTRACT FROM AUTHOR]

This paper investigates the sensitivity of golfers' performance to meteorological conditions at the men's US Masters tournament over the 40-year period 1980–2019. The mean and standard deviation of round scores are related to local temperature, humidity, wind speed and direction, and concurrent and antecedent precipitation. Mean scores are more dependent on weather conditions than the variability of scores in a given round. The best predictor of mean scores is the wet-bulb temperature in rounds one and two, and the zonal wind speed in rounds three and four. Across both sets of rounds (1 and 2, and 3 and 4), the wet-bulb temperature is a better predictor of mean scores than the air temperature, which implies that atmospheric moisture content affects scores. In general, golfers take fewer shots and so perform better in warmer and calmer conditions. The synergestic effect of several weather variables explains over 44% of the variance in mean scores. Mean meteorological conditions during play are a much better predictor of the players' average performance than the standard deviation of the weather variables. The golfers' performance becomes more variable in cooler conditions with a wider range of scores. Precipitation during play and the dampness of the ground (as quantified by rainfall up to ten days before play) do not have a consistent and statistically significant effect on the competitors' performance. In short, this paper demonstrates that golf scores are dependent on weather conditions. [ABSTRACT FROM AUTHOR]

This paper investigates the process behind climate model biases, especially in rainfall, during the southwest (June to August; JJA) and northeast monsoon (December to February; DJF) seasons over the Philippines using the downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment-Southeast Asia (SEACLID/CORDEX-SEA). Biases in the surface and near-surface climate are assessed over land in eastern (EastPH) and western (WestPH) Philippines using observation and reanalysis datasets. Results show that models with well-represented sea surface temperature (SST) provided better added values after downscaling. Although the CMIP5-driven models tend to overestimate both surface energy and water partitioning, simulations with well-represented SST have better surface energy partitions. In contrast to the reference simulation driven by ERA-Interim reanalysis, the CMIP5-driven simulations tend to underestimate (overestimate) moisture flux convergence during the DJF (JJA) season, which might have contributed to the overall underestimation (overestimation) of rainfall in this season. Further investigation along a vertical transect shows the differences in governing mechanisms for each season. During DJF (JJA) season, the simulation with underestimated (overestimated) SST, near-surface air temperature, and mixing ratio results in underestimated (overestimated) rainfall over EastPH (WestPH). In addition, the CMIP5-driven models were found to overestimate low-level winds, which may have induced the deep convection regardless of season, suggesting the role of low-level winds in the convection scheme used in RegCM4 aside from topography and land cover. [ABSTRACT FROM AUTHOR]

This paper introduces a fast urban microscale meteorological model with a horizontal resolution of O(10) m, named URBAN (Urban Rapid & Building-Aware Neighborhood), which is capable of rapid assessment of meteorological fields over key urban areas, including wind speed, air temperature, humidity and thermal comfort index, with the execution time less than 10 minutes consuming 1 CPU core. URBAN uses a fast wind diagnostic method to construct three-dimensional (3-D) wind fields surrounding complex building clusters with their geometry resolved explicitly To enhance the accuracy of wind reconstruction and the continuity of the initial wind field around irregular buildings, we propose a new parameterization method based on stream functions, which can accurately characterize the influences of complex urban building clusters on the three-dimensional wind field The model can provide various results for the meteorological service of large outdoor activities, including conventional meteorological elements (wind, temperature, humidity, radiation, etc.) and the Universal Thermal Comfort Index, which is derived from the relationship between physiological processes and environmental meteorological conditions. In this paper, URBAN is applied to develop an automatic analysis and forecast system of microscale meteorological elements over the central Beijing region in summer during a large outdoor event. By comparing with the half-hourly observations from three auto weather stations (AWSs) in the region, the root-mean-square errors (RMSEs) of the modeled 10-meter-height wind speed, 2-meter-height air temperature and humidity are 0.98 m s−1,1.37 °C and 7.37%, respectively. [ABSTRACT FROM AUTHOR]

Varied origins have the ability to construct the continental red beds, such as paleoclimate, provenance, drainage status, etc. Reconstructing paleoclimate is the key to investigate the origin. Thus, this paper outlines the normal distribution constrained method (NDCM) to reconstruct paleoclimate quantitatively and accurately during the lower fourth sub-member of Shahejie (Es4x) in the Bonan Sag, which is in low requirement of data and environmental conditions. Based on the NDCM, the paleoclimate is still in a long-term arid background as the potential evapotranspiration rates (PERs) are larger than 6 in the transgressive system tract (TST) and regressive system tract (RST) during the period of Es4x. The decreasing PERs that range from 6.28 to 6.04, decreasing atmospheric temperature range from 14.37 to 13.95 °C and increasing mean precipitation of the wettest month and mean precipitation of the driest month indicate the paleoclimate is breaking away from the hot and arid background from TST to RST. Meanwhile, the paleoclimate fluctuation develops decreasing frequency and increasing amplitude which can be inferred from the increasing standard deviation (std), Th/K and Th/U curves. The hot and arid background is still the main origin of red color during the Es4x. Moreover, the other origin may also develop since there is no clear law between red coloration and the developing location of sediments. [ABSTRACT FROM AUTHOR]

In many media, especially in prime-time television shows, we often hear that the temperature is or will be higher than average or above average. It is seldom said which average it is, namely which period this comparison refers to. It is also rare to hear how much this above-average temperature is higher than one average. The question is what the climate average or climate normal is? The World Meteorological Organization (WMO) during the twentieth century defined a period of 30 years as the standard reference for calculating climate normal. The 30-year period, known as the climatological standard normal, serves as a benchmark against which current observations can be compared to the previous one. The last climatological standard normal period 1981–2010 is still in use while a new one is in preparation for a 30-year period that will cover the period from 1991 to 2020. It is expected that this change will be in use from the beginning of 2022. In this paper, based on the temperature change data obtained at the meteorological stations Split Marjan and Zagreb Grič, the differences in the conclusions about the last air temperature changes in the period of the last 10 years (2011–2020) are studied. The following four different 30-year climate normal periods are analysed: (1) 1961–1990; (2) 1971–2000; (3) 1981–2010; (4) 1991–2020. The analyses performed in this paper indicate the necessity of constant changing of the 30-year climate normal period used to estimate variations in recent air temperatures. Given the sudden rise in air temperature over the last 30 years, the period from 1991 to 2020 should be currently used for comparison and assessment of the most recent temperatures and other climatic parameters. [ABSTRACT FROM AUTHOR]

This paper discusses the radiation budget and its temporal variability over Poland. The data analysis is based on the MERRA-2 reanalysis for the years 1980–2020. During the last four decades, the enhancement of climate warming has been observed, which coincides with the changes in the radiation budget. Positive and statistically significant trends at the top of the atmosphere (TOA; 0.7 ± 0.2 W/m2/10 year) and on the Earth's surface (1.5 ± 0.2 W/m2/10 year) radiation budget (net downward flux) are mainly a consequence of changes in the amount of aerosol and greenhouse gases (GHG). According to MERRA-2, the AOD during this period decreased by − 0.19 (at 550 nm), which is 87% of the long-term (1980–2021) mean value (0.22). The reduction of AOD is due mainly to the decline of non-absorbing sulfate particles, which leads to a reduction of single-scattering albedo (SSA) by − 0.008 per decade and Angstrom exponent (AE) by − 0.06 per decade (both trends statistically significant). On the other hand, the GHG concentration increased by 4.9%/10 year and 3%/10 year, respectively, for CO2 and CH4. The total column of water vapor increased (1.3%/10 year), while ozone decreased (− 1%/10 year). Despite the fact that cloud cover and cloud optical depth (COD) decreased (− 1.8%/10 year and − 1.0%/10 year), the impact of cloud on temporal variability radiation budget is small. It can be explained by nearly compensated shortwave (SW) cooling and longwave (LW) heating effects. During the analysis period, near-surface air temperature increased by 2.0 °C. The estimated increase in SW net surface radiation (7.9 W/m2) leads to climate warming by 0.8 °C, which is a consequence mainly of the reduction of aerosol (0.4 °C) and cloud cover (0.2 °C). The impact of the change of SW radiation on air temperature is more pronounced during the warm season, while during the cold, air temperature change is controlled mainly by the variability of air mass advection. [ABSTRACT FROM AUTHOR]

Frost on agricultural products in spring imposes heavy financial losses to agriculture particularly in northwest of Iran's orchards. Frost early warning is an effective way in preventing frost risk in apple orchards. This paper aims to validate frost early warning system in apple orchards of northwestern Iran by predicting flowering date of apple tree and its combination with WRF (Weather Research and Forecasting) model simulations of the 2-m temperature. To this end, stepwise multiple linear regression model was used for predicting flowering date; therefore thermal variables data including mean temperature, mean maximum temperature, mean minimum temperature, last frost date, heat wave duration, heat wave intensity, GDD and maximum standard temperature data larger than 1 (Z > 1) for the period of 2007–2016 (from March 1st until flowering date) in Kahriz agrometeorological station in northwest of Iran were calculated and added to the model. Also, to evaluate the accuracy of WRF model simulations, the 72-h simulations of the 2-m air temperature for internal computational grid in northwestern Iran (West Azerbaijan Province) in 17 synoptic stations were compared to minimum air temperature observed in the stations. Results revealed that the proposed stepwise regression method is a relatively precise model with a mean absolute error of 2.7 days between the predicted and observed flowering dates. Findings were also indicative of an acceptable accuracy of 72-h minimum air temperature simulations of WRF model in the study area. [ABSTRACT FROM AUTHOR]

Heat transfer in a duct, between air and a battery pack numerically and using Comsol software, is the subject of this article. The duct has two separate air inlets and a battery pack in the middle. All batteries are made of lithium-ion and are placed in a PCM housing in a circular shape. The (Re) of air in the duct varied between 100 and 400, and the time of transient study was 200 min. Simultaneously, a cool environment is used to resemble a domestic building. Using Design Builder software, the thermal energy (TLEY) needed for this structure was determined yearly in various seasons. The TLEY produced in the batteries is used to heat the house, and then, it is calculated what percentage of the energy required by the house can be supplied by the battery. The results of this paper clearly indicate that the maximum and average battery temperature (T-Bt) cells in the duct increase at the beginning of the process and then decrease. After this period, depending on the amount of (Re) of air in the duct, no variations in the T-Bt are detected after a specific duration. The fixed duration of the battery temperature grew as the (Re) level increased, while the T-Bt decreased. The outlet air temperature and the average PCM fraction of the melt also remain constant after the initial decrease, after a certain time. Increasing the (Re) rate reduces the quantity of exhaust air temperature and the PCM fraction of the melt. Thus, ideally, 13.83% of the required TLEY can be supplied from the batteries in the required seasons. [ABSTRACT FROM AUTHOR]

Background: Reproductive behaviour under natural conditions is hard to record, especially in rare and cryptic species, such as the Casuhatien anole (Pristidactylus casuhatiensis). This medium size lizard is endemic to the Sierras Australes Bonaerenses, a rocky outcrop in the Southern Pampas of Argentina. Methods: During spring and summer from 2018 to 2022, we surveyed five hills at above 600 m altitude, where specimens were known to occur. Every time we detected a courtship or copulation event, we took photographs of the specimens for their individual identification, and recorded the body temperature of the participating individuals as well as air temperature, wind speed and relative humidity. We recorded the duration in minutes of each phase of the event (immobilization, juxtaposition of the cloacae followed by copulation, and finally, restraint and separation) and described the main habitat features of the site where it happened. Results: We detected 12 reproductive events; all of them occurred in October and November, on Funke, Puntudo and Tres Picos hills, mostly between 1300 and 1700 h. The mean duration of each copulation phase was 6 min (95% CI = ± 1.4, n = 10), 4.3 min (95% CI = ± 1.8, n = 10) and 3.8 min (95% CI = ± 3.42, n = 10), respectively. Conclusion: The reproductive activity of the Casuhatien anole is concentrated in the spring, particularly in the early afternoon, following the patterns described for other species of lizards. During these events, the greatest exposure of individuals could make them more vulnerable. This, together with other features related to their reproductive behaviour revealed in this paper may contribute to the planning of conservation actions. [ABSTRACT FROM AUTHOR]

This paper studies the spatiotemporal variability of the IRt index (defined as the difference between the percentage of precipitation corresponding to warm days and cold days in year t) calculated at 35 stations in the Iberian Peninsula during 1951–2019. Spatial variability is estimated using cluster analysis (Euclidean distance, Ward's method), which allows regionalization of the data set for each year's season. For each cluster, the < IR > index is defined as the average of the indexes corresponding to each cluster element. The temporal evolution of the < IR > indices is analyzed by studying the appearance of trends using the Mann–Kendall test and the possible existence of various subperiods using the cumulative sum of deviations and the t-test for the difference between the means. The role of EA, EA/WR, NAO, SCAN and WeMO teleconnection patterns in modulating the relationship between temperature and precipitation is analyzed by applying multiple regression analysis. The results show a trend towards positive (or less negative) values in the relationship between temperatures and precipitation (of varying magnitude depending on the region and season) and the contribution of atmospheric dynamics to these changes. [ABSTRACT FROM AUTHOR]

More than 85% of the Ethiopian population's livelihood depends on rain-fed agriculture. Therefore, accurate climate estimation is quite useful to improve food security. However, the poor spatial resolution of satellite open access Outgoing Long-wave Radiation (OLR) data and the complicated relationship with the temperature and precipitation over variable topography features, the climate estimation is below the satisfactory level. Hence, to address this problem, this paper aimed to model OLR based on moisture parameters related to the air temperature in Ethiopia. European Center for Medium-Range Weather Forecast (ECMWF) and satellite data from 1981 to 2020 has been used to develop a new OLR model. The atmospheric moisture budget equation was applied for the new OLR mode development. The results indicated that the seasonal variation moisture coefficient (α) for winter, spring, summer, and autumn is found 0.99, − 0.29, 0.91, and 0.98 m3w−1 which is satisfied the model requirement. During winter, spring, summer, and autumn, the average value of OLR are found 269.60, 246.10, 247.90, and 255.60 Wm−2 from satellite direct observation respectively; while from the developed model is found at 268.79, 244.01, 248.97 and 254.93 Wm−2 respectively. Based on error matrices the developed mode is well agreed with satellite direct observation at a specific point and it is helpful to estimate OLR in the required spatial resolution. Generally the increment of the local moisture which is decreased OLR and increases the air temperature. Hence, stakeholders must pay high attention to the new OLR model adaptations for flood prevention and drought monitoring in Ethiopia. [ABSTRACT FROM AUTHOR]

The present paper analyzes long term (1960–2021) of the minimum, maximum, and average temperatures in Central Anatolia Region, corresponding middle part of Turkey, aiming to reveal how strongly the temperatures increase, which is a precondition for sustainable development in this region. For this purpose, temporal trends, variability, and anomalies in temperatures of 27 meteorological stations were detected using Mann–Kendall test (M-K), coefficient of variations, and Gaussian filter, respectively. Results show a statistically significant increasing trend in annual average temperatures at approximately 92% of all stations. This shows that the increasing trend in spring and summer temperatures plays an important role in amplifying the warming trend of annual temperatures. The coefficients of variation in annual average, maximum, and seasonal temperatures increase from west to east. Significant strong increasing trend (at 0.001 level) in winter temperatures was detected only at Seydişehir. While positive anomalies have been observed in the northwestern part of the region since 2007, they have been observed in other parts since 1995–1996. Since the strong increase in summer temperatures in Aksaray, Cihanbeyli, and Seydişehir is also observed in autumn, warm conditions continue until the end of autumn in these settlements. For this reason, four seasons do not prominently occur for all three settlements. Generally, annual maximum and minimum temperatures illustrate statistically significant increasing trends for all stations and 74% of all stations, respectively. According to the M-K test results, climate of the region has warmed on average by 1.44 °C in last 31 years. [ABSTRACT FROM AUTHOR]

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Recently, ammonia (NH3), which has a higher energy density than hydrogen, has gained attention for zero-carbon emission goals in the transportation sector. However, in a conventional internal combustion engine (ICE), NH3 combustion mechanism is still under investigation. In this paper, to further expand the knowledge on the adoption of NH3 in ICEs, authors conducted NH3/gasoline co-combustion experiments in a modified, 17.7:1 compression ratio, naturally aspirated spark-assisted CI engine with sub-chamber. The sub-chamber was chosen in order to enhance the combustion speed of NH3. In addition, the sub-chamber was equipped with glow and spark plugs to overcome the high auto-ignition temperature of NH3. Engine performance and NOX emissions were studied under three different intake air temperatures. During the experiments, NH3 content was increased gradually where the engine was run under lean conditions. Although higher NH3 content was achieved compared to our previous work, increasing the intake air temperature resulted in decreased charging efficiency. In addition, corrosion was found on the piston ring after 120 h of operation, negatively affecting the engine performance. Furthermore, NH3/gasoline co-combustion duration was shortened drastically with the influence of the sub-chamber, where the longest combustion duration under the present conditions was found to be 17°CA. [ABSTRACT FROM AUTHOR]

In this paper, we analyse sea ice freezing in the presence of a two-phase (mushy) layer filled with ice structures and salt water. An exact analytical solution of moving boundary problem is constructed with allowance for turbulent motion of sea water at the phase interface between the mushy layer and ocean. As a result, the temperature and salinity distributions as well as the sea ice fraction are found analytically. The laws of motion for the phase transition boundaries "sea ice–mushy layer" and "mushy layer–ocean" are derived as well. In addition, such structural characteristics of sea ice as its permeability and primary interdendritic spacing are calculated. The heat fluxes (thermal flux emanating from ice surface, thermal flux caused by ice freezing in a mush and residual heat flux caused by ocean cooling) governing the heat budget and influencing climate dynamics and circular air currents over a freezing ocean are analytically deduced too. We analyse how sea ice freezes in the presence of a mushy layer when fluctuations in the atmospheric temperature and friction velocity occur. For this purpose, we introduce corresponding fluctuations based on the Langevin equation and Ornstein–Uhlenbeck stochastic processes. Two types of noises, additive and multiplicative, are considered. The analytical solution obtained is then analysed with allowance for stochastic fluctuations in the atmospheric temperature and friction velocity. One of the main results is that the permeability of sea ice and average interdendritic spacing of ice crystals in the two-phase layer increase as the noise intensity increases. Another important conclusion is that the mushy layer, representing the layer of ice and salt water, becomes thicker and the ocean freezes faster when fluctuations in the atmospheric temperature and friction velocity occur. [ABSTRACT FROM AUTHOR]

A stochastic weather generator is proposed in the paper. The model is designed for the numerical simulation of joint time series of surface air temperature, wind speed, and relative air humidity with a three-hour resolution at weather stations located in the Russian Arctic. The proposed weather generator allows taking into account the non-Gaussian form of one-dimensional distributions of meteorological parameters and the diurnal variations inherent to real series. The results of verifying the proposed model are presented, and the prospects of its further application are briefly described. [ABSTRACT FROM AUTHOR]

Spatiotemporal prediction in two-dimensional (2D) space has been widely studied in computer science and geosciences, such as video prediction and weather forecasting. However, many spatiotemporal evolutions take place in three-dimensional (3D) space, e.g., atmospheric temperature changes of multiple isobaric surfaces. For such a 3D multi-plane prediction task, existing methods usually fail to model the information interactions between different 2D planes. In this paper, we propose a novel neural network-based method to tackle 3D multi-plane spatiotemporal prediction tasks. Specifically, compared with prediction in 2D space, information interactions in 3D space are more complicated because of the emergence of a new dimension. To clarify these interactions, we first propose to separate them into three types, i.e., inter-level interaction, intra-level interaction, and unknown residual interaction. This separation is novel and comprehensive, which covers vertical, horizontal, and potentially unknown factors. Based on the separation, we design a new spatiotemporal module for prediction. It contains three units to capture the three separated interactions, respectively: (1) an inter-level interaction unit, which is the first one dedicated to modeling the information interaction between different levels. The inter-level interaction, as a new feature in 3D space, has not been effectively studied. Hence, in this unit, we propose to model it by leveraging the spatial associations between adjacent levels and developing a new dynamics simulation. (2) An intra-level interaction unit is utilized to model the information interaction within the same level. With the benefit of interaction separation, we can leverage the neural partial differential equations to formulate spatiotemporal dynamics and exploit prior physical knowledge in data. (3) Furthermore, a residual unit is employed to capture the remaining unknown and uncertain factors, which can help further improve the expressiveness of our model. As for a prediction, we employ the three units to capture various dynamics, and combine them to obtain a comprehensive one. The final dynamics will be decoded for generating prediction via a convolutional neural network. We conduct extensive experiments on a dataset of atmospheric temperature changes. The experimental results show that our method obtains a significant improvement and achieves a new state-of-the-art performance. [ABSTRACT FROM AUTHOR]

In this paper, the removal efficiency of Cerium (Ce(ΙΙΙ)) and lanthanum (La(ΙΙΙ)) ions from aqueous solution using Amberlite XAD-7 resin impregnated with DEHPA(XAD7-DEHPA) was studied in the batch system. The adsorbent (XAD7–DEHPA) was characterized by SEM–EDX, FTIR and BET analysis Techniques. The response surface methodology based on the central composite design was applied to model and optimize the removal process and evaluate operating parameters like adsorbent dose (0.05–0.065), initial pH (2–6) and temperature (15–55). Variance analysis showed that the adsorbent dose, pH and temperature were the most effective parameters in the adsorption of Ce(ΙIΙ)and La(IΙI) respectively. The results showed that the optimum adsorption condition was achieved at pH = 6, the optimum amount of absorbent and the equilibrium time equal to 0.6 gr and 180 min, respectively. According to the results, the adsorption percentage of Ce(ΙIΙ) and La(ΙΙΙ) ions onto the aforementioned resin were 99.99% and 78.76% respectively. Langmuir, Freundlich, Temkin and sips isotherm models were applied to describe the equilibrium data. From the results, Langmuir isotherm (R2 (Ce) = 0.999, R2 (La) = 0.998) was found to better correlate the experimental rate data. The maximum adsorption capacity of the adsorbent (XAD7–DEHPA) for both Ce(IΙI) and La(III) was found to be 8.28 and 5.52 mg g−1 respectively. The kinetic data were fitted to pseudo-first-order, pseudo-second-order and Intra particle diffusion models. Based on the results, the pseudo-first-order model and Intra particle diffusion model described the experimental data as well. In general, the results showed that (XAD7–DEHPA) resin is an effective adsorbent for the removal of Ce(IΙI) and La(III) ions from aqueous solutions due to its high ability to selectively remove these metals as well as its reusability. [ABSTRACT FROM AUTHOR]

In the present research paper, the thermal performance of helical ground air heat exchanger (HGAHE) has been experimentally analyzed under Saharan climate conditions during summer season. A flexible PVC pipe with a diameter of 0.06 m and a length of 30 m has been arranged as a helical-coil layout to design the HGAHE, and it is inserted into a borehole of 5 m depth. The study acknowledges that a drop as high as 11.0 °C in air temperature can be achieved with an air velocity of 10 m s−1 and an inlet air temperature of 38 °C. The study also revealed that, by incrementing the air velocity from 10 to 20 m s−1, the heat exchange rate of the HGAHE system increases by 158% after 3 h of operation time; however, the efficiency of the HGAHE decreases by 21.51%. Moreover, thermal performance derating factor (TPDF) increments with increasing the operation time and air velocity. After 3 h of operation time, the highest TPDF is attained as 0.27 with a velocity of 20 m s−1, whereas the lowest TPDF is observed as 0.14 for a velocity of 10 m s−1. [ABSTRACT FROM AUTHOR]

In the context of the ongoing United Nations Framework Convention on Climate Change (UNFCCC) process, it seems important to focus attention not only on global mean surface air temperature (GSMT) but also on the climate of specific regions in order to gain insights into the dynamics of the changes, the timescales of the periodic components, the local trends and the relationships between climatic variables in the region of interest. This is important for scientists as well as for policymakers. This paper provides an analysis of the changes in local air temperature and precipitation depth in exceptionally long observational records and examines the relationships between these two variables. The focus is on monthly values. Temperature maximum, minimum, range, and cumulative precipitation depth are considered. The wavelet analysis shows that the scale of variation is different for temperature and precipitation and that the behavior of the temperature range values diverges from the behavior of the minimum and maximum values. The timescale of important changes in the long-term trend is, however, similar. Results also suggest that the main mode of variability is persistent through time in the series of temperature maximum, minimum, and range but not in precipitation depth. This is a clear evidence of climate change. All series show variances that change over time and are, as expected, nonstationary. The analysis of the wavelet coherence shows that the relationship between precipitation and temperature evolves through time, and its intensity varies considering different time scales. The association between these climatic variables is particularly strong in the last decade. Is it noteworthy that the analysis of the coherence suggests that temperature is leading to rain and not the other way around. This highlights the impact of global warming on the hydrologic cycle and on related human activities. [ABSTRACT FROM AUTHOR]

Identifying and predicting the impacts of climate change are crucial for various purposes, such as maintaining biodiversity, agricultural production, ecological security, and environmental conservation in different regions. In this paper, we used the surface pressure (SP), surface temperature (ST), 2-m air temperature (AT), 2-m dewpoint temperature (DT), 10-m wind speed (WS), precipitation (PRE), relative humidity (RH), actual evapotranspiration (ETa), potential evapotranspiration (ETP), total solar radiation (TRs), net solar radiation (NRs), UV intensity (UVI), sunshine duration (SD), convective available potential energy (CAPE) as factors in our climate modeling. The spatiotemporal distribution characteristics of the climate factors were analyzed and identified based on historical data for China from 1950 to 2020 using factor analysis and a grey model (GM (1,1)), and their future change characteristics were predicted. The results show that there is a strong correlation between climate factors. ST, AT, DT, PRE, RH, and ETa are the main factors that have the potential to cause heavy rain, thunderstorms, and other severe weather. Meanwhile, PRE, RH, TRs, NRs, UVI, and SD are among the major factors linked to climate change. Specifically, SP, ST, AT, and WS are among the minor factors in most areas. The top ten provinces in terms of combined factor scores are Heilongjiang, Neimenggu, Qinghai, Beijing, Shandong, Xizang, Shanxi, Tianjin, Guangdong, and Henan. The trend of climate factors in China is expected to remain relatively stable over the next 30 years, with a noteworthy decrease observed in CAPE compared to the past 71 years. Our findings can help to better mitigate the risks associated with climate change and enhance resilience; they also provide a scientific basis for environmental, ecological, and agricultural systems to cope with climate change. [ABSTRACT FROM AUTHOR]

Based on modern observations, historical proxy data, and climate model simulations, this paper provides a comprehensive overview of the past, present and future evolution characteristics of the Atlantic Meridional Overturning Circulation (AMOC), as well as its impact on the surface air temperature (SAT) at regional and hemispherical scales. The reconstruction results based on the proxy data indicate that the AMOC has weakened since the late 19th century and experienced overall weakening throughout the 20th century with low confidence. Direct observations show that the AMOC weakened during 2004–2016, but it is not possible to distinguish between its decadal variability and long-term trend. Climate models predict that if greenhouse gas emissions continue to increase, AMOC will weaken in the future, but there will not be a sudden collapse before 2100. For the thermodynamic effects of AMOC, the increased surface heat flux release and meridional heat transport (MHT) over the North Atlantic associated with the strong AMOC cause an increase in the hemispherical SAT. At the millennial scale, climate cooling (warming) periods correspond to a weakened (strengthened) AMOC. The enhanced MHT of a strong AMOC can affect Arctic warming and thus influence regional SAT anomalies and SAT extremes through mutual feedback between Arctic sea ice and AMOC. In terms of dynamic effects, a strong AMOC modulates the Rossby wave trains originating from the North Atlantic and spreading across mid-to-high latitudes in the Northern Hemisphere and causes an increase in the variabilities in the circulation anomalies over the Ural and Siberian regions. Ultimately, a strong AMOC significantly affects the frequencies of extreme cold and warm events in the mid-to-high latitude regions over Eurasia. In addition, AMOC can also influence regional and global SAT anomalies through its dynamic adjustment of planetary-scale circulation. Decadal variation in AMOC is closely related to the Atlantic Multidecadal Oscillation (AMO). During positive phases of AMO and AMOC, enhanced surface heat fluxes over the North Atlantic lead to abnormal warming in the Northern Hemisphere, while during negative phases, the reverse case occurs. Under high emission scenarios in the future, the possibility of AMOC collapse increases due to freshwater forcing. However, most advanced climate models underestimate the strength of the AMOC and its impact on the AMO and relevant climate change, which presents a major challenge for future understanding and prediction of the AMOC and its climate effects. [ABSTRACT FROM AUTHOR]

This paper aims to describe and evaluate the proposed calibration model based on a neural network for post-processing of two essential meteorological parameters, namely near-surface air temperature (2 m) and 24 h accumulated precipitation. The main idea behind this work is to improve short-term (up to 3 days) forecasts delivered by a global numerical weather prediction (NWP) model called ECMWF (European Centre for Medium-Range Weather Forecasts). In comparison to the existing local weather models that typically provide weather forecasts for limited geographic areas (e.g., within one country but they are more accurate), ECMWF offers a prediction of the weather phenomena across the world. Another significant benefit of this global NWP model includes the fact, that by using it in several well-known online applications, forecasts are freely available while local models outputs are often paid. Our proposed ECMWF-enhancing model uses a combination of raw ECMWF data and additional input parameters we have identified as useful for ECMWF error estimation and its subsequent correction. The ground truth data used for the training phase of our model consists of real observations from weather stations located in 10 cities across two European countries. The results obtained from cross-validation indicate that our parametric model outperforms the accuracy of a standard ECMWF prediction and gets closer to the forecast precision of the local NWP models. [ABSTRACT FROM AUTHOR]

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The impact of extreme weather conditions on humans is one of the most important topics in biometeorology studies. The main objective of this study is to analyze the relationship between temperature-related weather conditions and natural mortality in the five most populated cities of Romania, namely, Bucharest, Cluj-Napoca, Constanța, Iași, and Timișoara. The results of this study aim to bridge a gap in national research. In the present paper, we used daily natural mortality data and daily minimum and maximum air temperatures. The distributed lag nonlinear model (DLNM) allowed us to identify weather conditions associated with natural mortality. The most important results are as follows: (i) a higher daily mortality is related to a high frequency of heat stress conditions; (ii) a higher maximum temperature increases the relative risk (RR) of natural mortality; (iii) the maximum number of fatalities is recorded on the first day of high-temperature events; and (iv) individuals much more easily adapt to cold stress conditions. The main conclusion in this study is that the inhabitants of the most populated cities in Romania are more sensitive to high-temperature stress than to low-temperature stress. [ABSTRACT FROM AUTHOR]

This paper is devoted to studying the response of different types of vegetation cover in the West Siberian Plain to temperature changes observed in recent decades. The paper analyzes trends in the series of the normalized difference vegetation index (NDVI) and air temperature in the vicinities of meteorological stations of the West Siberian Plain, which are characterized by different types of vegetation. Phases of growth (1982–1997) and stabilization (1997–2015) of NDVI values have been revealed against the background of the observed effects of global-warming slowdown, decrease of solar activity, and transition from the positive phase of the ocean-surface temperature to the negative phase in the El Niño region (ONI). It is shown that the spring months make the main contribution to variations in the seasonal NDVI value. A correlation analysis of spring NDVI and air-temperature values during the periods of NDVI growth and stabilization has been carried out. It is shown that the pattern of response of vegetation to the observed increase in temperature depends on its type and location. Thus, the relationship between the NDVI and air temperature increases in the tundra zone, decreases in very swampy areas of forest tundra and northern taiga, generally decreases in the Ob–Yenisei interfluve from forest tundra to subtaiga, and increases on the left bank of the Ob River. [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]

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 order to study the improvement of the mechanical properties of basalt fiber geogrids on semi-rigid base asphalt pavement and the prevention and control effect of reflection cracks, this paper laid the basalt fiber geogrid and test sensor in the different positions of the semi-rigid base of Yanchong Expressway experimental Sect. (625 m long) are divided into three working conditions: I, II, and III according to different reinforcement levels. Through the test, the temperature and strain at the corresponding positions of the top and bottom of the semi-rigid base course change with time, and the crack performance of the basalt fiber geogrid-reinforced semi-rigid base course is analyzed under the action of temperature and the linear regression equations between the temperature and the strain of the base course. Atmospheric temperature between substrate strain and substrate temperature is obtained. The field test results show that adding basalt fiber geogrid to the semi-rigid base can prevent reflection cracks and improve the anti-crack performance of the semi-rigid asphalt pavement, the best anti-crack effect in the middle position of the semi-rigid base. [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]

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]

Time series forecasting of meteorological variables, such as the hourly air temperature, has multiple benefits for industry, agriculture, and the environment. Due to the high accuracy required for the associated short-term predictions, traditional methods cannot satisfy the requirements and generally ignore spatial dependencies. This paper proposes a deep Graph Convolutional Long Short Term Memory Neural Network (GCN-LSTM) technique to tackle the time series prediction problem in air temperature forecasting. In the proposed methodology, temporal and spatial-based imputation approaches have been employed to recover the weather variables missing values. The proposed approach is validated using real, open weather data from 37 meteorological stations in Spain. Performed analysis indicates that GCN-LSTM showed superior performance when compared with various state-of-the-art Deep Learning based models found in the literature, resulting in a more robust and computationally efficient model for forecasting air temperature in many meteorological stations simultaneously. [ABSTRACT FROM AUTHOR]

Evaporation is one of the major components of the hydrological cycle and its accurate estimation affects water resource management, irrigation planning, and environmental studies. In this paper, an explicit mathematical function was proposed based on the response surface function (RSF) to predict pan evaporation. The second-order RSFs were found to be more efficient and accurate than the linear RSF. The ability of the proposed RSF-based method was investigated with a wavelet-based adaptive neuro-fuzzy inference system (WANFIS) and wavelet-based support vector regression (WSVR) to estimate the daily evaporation of the pan. Daily climatic variables such as wind speed, sunshine hour, air temperature, and relative humidity from two climate stations (Chahnimeh and Pishien reservoirs) in Iran were used as inputs in all models. The modelling results were evaluated using RMSE, d, NSE, CI, and R2 in both regions. RSF, WANFIS, and WSVR all could be successfully employed in modelling the evaporation estimation. WANFIS model shows the best results and its estimated statistics factors in Chahnimeh are RMSE = 3.05, NSE = 0.90, d = 0.98, CI = 0.88, and R2 = 0.98 and for Pishien are RMSE = 1.51, NSE = 0.84, d = 0.96, CI = 0.81, and R2 = 0.92. Also, the RSF model provides satisfactory results similar to the WSVR model. [ABSTRACT FROM AUTHOR]

General circulation models (GCM) show projections of climate variables that when downscaled can be applied to analyse future behaviour in different areas or places. Using them is possible not just to obtain expected values of climate variables but also to calculate their distributions and use those values to assess the effects of climate change at a local level. However, these calculations depend on the GCM selected. In this paper, daily maximum near-surface air temperatures from 21 climate models under representative concentration pathway (RCP) scenarios RCP 4.5 and RCP 8.5 and historic daily maximum temperatures (1990–2019) from nine cities in southern Spain are used with two objectives: first, to investigate past behaviour broken down into a deterministic part and a stochastic part; second, to compare historical data (2006–2019) with the information extracted from the 21 GCMs based on calculating goodness of fit in the period for both deterministic and stochastic parts. The methodology proposed may be useful in selecting a model or a range of models for use in a specific study. The results show positive historical and future trends in maximum daily temperature for these cities. The GCMs with the best fit for each city in this specific case are also presented. [ABSTRACT FROM AUTHOR]

The aim of this paper is to analyze the existence of cold-air pools on anticyclonic nights in a narrow urban valley where three hospital units and a road with intense traffic are located and where the slopes associated with proximity to a medium-size mountain introduce different factors from that observed in other valleys where this phenomenon forms on cold, calm nights. On these nights, it is observed that cold-air pools develop in the valley just after sunset, in opposition to the ridge that dominates the valley (thermal belts), and it is found that their formation, development, and disappearance after the first hours of the day is a daily phenomenon. Using fixed data loggers and portable data loggers on the surface and at altitude coupled to a UAV (drone), it was possible to calculate the variation of the air temperature (as well as to locate the inversion layer) in different sectors defined along the valley. The finding of temperature oscillation along the longitudinal profile and at altitude, reflects a double source of feeding: in-situ irradiation, and the air draining down the slopes of the massif. [ABSTRACT FROM AUTHOR]

In recent years, the winter surface air temperature (SAT) anomaly over North American midlatitudes shows a "warm west/cold east" (WWCE) dipole pattern with warm (cold) extremes occurring in the west (east) of North America. In this paper, the Pacific sea surface temperature (SST) condition linked to the WWCE SAT dipole is investigated. It is found that the winter WWCE dipole can be crudely considered as being a result of the winter mean of sub-seasonal WWCE events. While the sub-seasonal WWCE dipole is related to the positive Pacific North American pattern (PNA+), the impact of the PNA+ on the WWCE dipole depends on the different type of El Niño SST pattern and the phase of Pacific decadal Oscillation (PDO). For a Central-Pacific (CP) type El Niño, the positive (negative) height anomaly center of PNA+ is located in the western (eastern) North America to cause a strong WWCE dipole, though the positive PDO favors the WWCE dipole. In contrast, the WWCE dipole is suppressed under an Eastern-Pacific (EP) type El Niño because the anticyclonic anomaly of the PNA+ dominates the whole North America. Moreover, the physical cause of why different types of El Niño influence PNA+ is further examined. It is found that different types of El Niño can significantly influence the midlatitude location of PNA+ through changing North Pacific midlatitude westerly winds due to changes in the Pacific Hadley cell. For the CP-type El Niño, the eastward migration of PNA+ is suppressed to favor its anticyclonic (cyclonic) anomaly appearing in the western (eastern) North America due to reduced midlatitude westerly winds via weakened Pacific Hadley cell. But for the EP-type El Niño, midlatitude westerly wind is intensified to cause the midlatitude anticyclonic anomaly of the PNA+ appearing over the whole North America due to enhanced Pacific Hadley cell. [ABSTRACT FROM AUTHOR]

Based on the formaldehyde (HCHO) inversion data of OMI satellite sensor from 2009 to 2019, this paper discusses the spatial and temporal distribution of HCHO column concentration over the Qinghai-Tibet Plateau in the past 11 years and explores the factors affecting the dynamic distribution of atmospheric HCHO column concentration over the Qinghai-Tibet Plateau in combination with meteorological, aerosol, ozone, and other data. The results show that the average concentration of HCHO column in the Qinghai-Tibet Plateau is 8.84 × 1015 molec/cm2 in the past 11 years, which is relatively low, and the annual variation rate of HCHO column is 7.79%, showing a slight upward trend. The seasonal changes show a trend of Autumn < Winter < Spring < Summer. The monthly variation is more obvious than the seasonal variation. The spatial distribution showed a decreasing trend from southeast to northwest. Residual analysis showed that 83.77% of the area was frequently affected by natural factors. Correlation analysis found that the natural factors including precipitation, air temperature, and atmospheric activities for a greater influence on the column experiment the distribution of the concentration of the Qinghai-Tibet Plateau and human factors, such as biomass, the distribution of fossil fuel combustion, and emissions to HCHO, play a promoting role. Through the backward trajectory analysis, it can be seen that the transboundary atmospheric transport activity has a prominent contribution to the distribution of HCHO in the southern region of the Qinghai-Tibet Plateau. Hurst index study found that in the future, the concentration of HCHO column in 53.03% of the Qinghai-Tibet Plateau will decrease, but in some areas, such as the eastern and northern parts of the Qinghai-Tibet Plateau, it will increase. [ABSTRACT FROM AUTHOR]