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Central Chile (CC, latitudes 32–40°S) experienced very active fires in 2017 and 2023. These fires burned large areas and killed many people. These unprecedented fires for CC presented a need for more defined fire weather conditions on the synoptic scale. In this paper, fire weather conditions were analyzed using various satellite-derived fire data (hotspots, HSs), wind streamlines, distribution maps of wind flow and temperature, and various synoptic-scale weather maps. Results showed that local winds, known as Puelche, blew on the peak fire days (26 January 2017 and 3 February 2023). The number of HSs on these days was 2676 and 2746, respectively, about 90 times the average (30). The occurrence of Puelche winds was confirmed by streamlines from high-pressure systems offshore of Argentina to the study area in CC. The formation of strong winds and high-temperature areas associated with Puelche winds were identified on the Earth survey satellite maps. Strong winds of about 38 km h−1 and high temperatures above 32 °C with low relative humidity below 33% were actually observed at the weather station near the fire-prone areas. Lastly, some indications for Puelche winds outbreaks are summarized. This paper's results will be used to prevent future active fire occurrences in the CC. [ABSTRACT FROM AUTHOR]

Future climate change is expected to increase the risk and severity of wildland fires in tropical regions. Synoptic-scale fire weather conditions in Mexico were carefully analyzed using 20 years of satellite hotspot and rainfall data, hourly weather data, and various climate data. Fire analysis results showed that eighty-four percent of all fires in Mexico occurred south of 22° N. Southwest Mexico (SWM, N < 22°, 94–106° W) and Southeast Mexico (SEM, N < 22°, 86–94° W), account for 50% and 34% of all fires in Mexico. Synoptic-scale analysis results using hourly data showed that westerly wind sea breezes from the Pacific Ocean blow toward the coastal land areas of the SWM while easterly wind sea breezes from the Caribbean blow into the SEM. The most sensitive weather parameters were "relative humidity" for the SWM and "temperature" for the SEM. The fire-related indices selected were "precipitable water vapor anomaly" for the SWM and "temperature anomaly" for the SEM. The SWM fire index suggests that future fires will depend on dryness, while the SEM fire index suggests that future fires will depend on temperature trends. I do hope that this paper will improve local fire forecasts and help analyze future fire trends under global warming in Mexico. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP), the Third Pole of the world, has experienced significant warming over the past several decades. Previous studies have mostly relied on station-observed air temperature (Ta), reanalysis data, and remotely sensed land surface temperature (LST) to analyze the warming trend over the TP. However, the uneven distribution of stations, the poor spatial resolution of reanalysis data, and the differences between LST and Ta may lead to biased warming rates. This paper first maps Ta over the TP from 2001 to 2020 based on multi-source remote sensing data, and then quantifies the spatio-temporal variations of remotely sensed Ta and elevation dependent warming (EDW) of this region. The monthly mean Ta is estimated using machine learning (ML) method year by year, and its accuracy is validated based on station-observed Ta. The coefficient of determination (R2 ranges from 0.97 to 0.98 and the mean absolute error (MAE) ranges from 1.01 to1.04 °C. The remotely sensed Ta is used to analysis warming trend and EDW over the TP. The overall warming trend of the TP during 2001–2020 is 0.17 ℃/10a, and warming mainly distributed in the eastern TP, central TP and western Kunlun Mountains. Among the four seasons, autumn shows the most significant warming, tripling the annual warming rate. Winter shows a significant cooling trend, with the warming rate of -0.18 ℃/10a. The study also reveales the existence of EDW at both the annual and seasonal scales. This paper suggests the potential of remotely sensed Ta in global warming study, and also provides an improved understanding of climate warming over the TP. [ABSTRACT FROM AUTHOR]

Fog in mine air intake roadways is a non-negligible disaster that seriously reduces the visibility of the roadway, affects vehicle transportation and the safe passage of personnel, and jeopardizes the safe production of the mine. This paper applies both field testing and numerical simulation to explore fog formation conditions and the effects of inlet air temperature and humidity on the fog zone length within the Wangjialing Mine's air intake roadway in Shanxi, China. First, based on the consideration of the relationship between the moisture gain of surrounding rock and the temperature with humidity of the air flow, the fog generation and distribution law model of the air intake roadway was established. Based on this model, the critical inlet air temperature and the critical inlet air relative humidity for fogging in the Wangjialing Mine air intake roadway were determined. In addition, we found that the fogging point inside the roadway shifted forward continuously with the increase in inlet air temperature and inlet air relative humidity, and the length of the fog zone expands parabolically in response to these rising conditions. [ABSTRACT FROM AUTHOR]

In this paper, a sequence (1979–2022) of a long-term trial established in Lukavec in 1956 (Czech Republic) focusing on the effect of weather, various nitrogen (N) fertilization methods (control, PK, N1PK, N2PK, and N3PK) and preceding crops (cereals, legumes, and oil plants) on winter wheat grain yield is presented. The weather significantly changed at the site of the long-term trial. While the trend in the mean temperature significantly increased, precipitation did not change significantly over the long term. Four relationships between weather and grain yield were evaluated to be significant: (a) the mean temperature in February (r = −0.4) and the precipitation in (b) February (r = −0.4), (c) March (r = −0.4), and (d) May (r = 0.5). The yield trends for all the fertilizer treatments increased, including the unfertilized control. The N3PK treatment provided the highest mean grain yields, while the unfertilized control had the lowest yields. Comparing the preceding crops, the highest yields were harvested when the wheat followed the legumes. On the other hand, the cereals were evaluated as the least suitable preceding crop in terms of grain yield. According to the linear-plateau model, the optimal nitrogen (N) dose for modern wheat varieties, following legumes and under the trial's soil climate conditions, was 131 kg ha−1 N, corresponding to a mean grain yield of 8.2 t ha−1. [ABSTRACT FROM AUTHOR]

In the present paper, the effect of air density variations on cup anemometer performance is analyzed. The effect on the sensor's performance is mainly due to the difference between the altitude at which the cup anemometer is working and the altitude at which this instrument was calibrated. Data from the available literature are thoroughly analyzed, focusing on explaining the coupled effect of the air temperature on both the rotor's friction torque and the air density (that is, related to the aerodynamic torque on the rotor). As a result, the effect of air density variation at constant temperature (that is, leaving aside any variation of friction forces at the anemometer rotor shaft) on the sensor transfer function (i.e., on the calibration constants) is evaluated. The analysis carried out revealed a trend change in the variation with air density of the transfer function of the cup anemometer. For densities greater than 0.65, the calibration constants of the instrument have a variation with density that must necessarily change suddenly as the start-up speed, represented by the calibration constant B, becomes zero around this value of air density. To highlight the relevance of the present research, some estimations of the effect of wind speed measurement errors associated with air density changes on the Annual Energy Production (AEP) of wind turbines are included. A 1.5% decrease in the AEP forecast at air density corresponding to 2917 m above sea level is estimated for 3000–4500 kW wind turbines. [ABSTRACT FROM AUTHOR]

Tropical sea surface temperature (SST) variability, mainly driven by the El Niño–Southern Oscillation (ENSO), influences the atmospheric circulation and hence the transport of heat and chemical species in both the troposphere and stratosphere. This paper uses Met Office, ERA5 and MERRA2 reanalysis data to examine the impact of SST variability on the dynamics of the polar stratosphere and ozone layer over the period from 1980 to 2020. Particular attention is paid to studying the differences in the influence of different types of ENSO (East Pacific (EP) and Central Pacific (CP)) for the El Niño and La Niña phases. It is shown that during the CP El Niño, the zonal wind weakens more strongly and changes direction more often than during the EP El Niño, and the CP El Niño leads to a more rapid decay of the polar vortex (PV), an increase in stratospheric air temperature and an increase in the concentration and total column ozone than during EP El Niño. For the CP La Niña, the PV is more stable, which often leads to a significant decrease in Arctic ozone. During EP La Niña, powerful sudden stratospheric warming events are often observed, which lead to the destruction of PV and an increase in column ozone. [ABSTRACT FROM AUTHOR]

The experimental setup of HVAC testing requires easy but accurate instrumentation, and recent analyses focus on the control based on the mean radiant temperature in order to better perform respect to the users satisfaction. Indeed, the research contribution of this work is the use of Arduino to develop a PID control for an electric heater. As a main feature, this control system can operate by maintaining the set-point on the air temperature or on the mean radiant temperature of the environment where it is placed. The paper describes the design and development of the PID control, as well as the testing of the control system applied to an electric heater, to heat a room in a laboratory in Bologna (Italy). In an Appendix the Matlab script employed to store data on the local pc an to send data to Google Sheets is reported, together with the Google script code employed to write the data from Matlab to the online sheet. As result, the developed PID is accurate in maintaining the required set-point temperature, with minimal deviations from the set-point, in the interval ± 0.2 K. [ABSTRACT FROM AUTHOR]

In recent years, the water level of the Dongting Lake (DTL) has been continuously low, and the wetland area and landscape pattern have changed significantly. Considering the obvious spatial heterogeneity of water regime changes in different waters of the DTL, this paper takes two core areas of the South Dongting Lake Nature Reserve (SDLNR) as study areas and analyzes the spatial distribution characteristics of the wetland blue–green landscape patterns by using remote sensing image data and hydrological and meteorological data. The multi-scale correlation between runoff, precipitation, temperature, and evapotranspiration in the SDLNR was studied via cross-wavelet transform analysis. The results show the following: (1) The change in the blue–green spatial patterns in different regions in different periods is inconsistent, and this inconsistency is related to the topography, climate, and human activities in each region; (2) there are seasonal fluctuations in precipitation, air temperature, and evapotranspiration in the SDLNR. Among them, the annual mean temperature shows a rising trend and passes the significance test with 95% confidence, while the annual mean precipitation and annual mean evapotranspiration show no significant change trend; and (3) our Pearson correlation analysis and cross-wavelet change results show that precipitation and temperature are strongly correlated with runoff, with a resonance period of 8–16 months, while the correlation between evapotranspiration and runoff is not significant. We recommend that policymakers establish an effective early warning system and make plans to store water through micro-terrain transformation in possible climate change treatments and strategies. [ABSTRACT FROM AUTHOR]

This article addresses the simulation of urban air temperatures with a focus on evaluating the Urban Weather Generator (UWG) model over Toulouse, France. As urban temperatures, influenced by factors like urbanization, anthropogenic heat release, and complex urban geometry, exhibit an urban heat island (UHI) effect, understanding and mitigating UHI become crucial. With increasing global warming and urban populations, aiding urban planners necessitates accurate simulations requiring data at the canyon level. The paper evaluates UWG's performance in simulating air temperatures under realistic conditions, emphasizing an operational context and a non-specialist user's perspective. The evaluation includes selecting the most suitable meteorological station, assessing the impact of the rural station choice, and conducting a sensitivity analysis of input parameters. The validation demonstrates good agreement, with a mean bias error (MBE) of 0.02 °C and a root mean square error (RMSE) of 1.73 °C. However, we highlight the fact that UWG performs better in a densely urbanized area, and exhibits limitations in sensitivity to urban surface parameter variations, particularly in less urbanized areas. [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 paper represents an attempt to understand the impacts produced by the North Atlantic Oscillation on the ice cover regime of the largest coastal lake in Poland situated in the northern part of the country. The purpose of the study was to estimate the impact of the North Atlantic Oscillation on selected ice cover parameters. The study also attempts to examine other factors that may affect its significant impact on observed changes in the climate. Water temperature data are used to assess temperature conditions in aqueous ecosystems as well as determine the heat absorption capacity of these systems. This allows one to then determine cooling rates in the event of ice cover formation. The data analysis provided in the paper focuses on selected characteristics of ice cover conditions (i.e. dependent variables) including the number of days with ice and maximum ice thickness in relation to NAO index changes (i.e. independent variables) for the reference period 1960/61–2009/10. In addition, the paper divides the studied period into 10-year subperiods in order to capture the weakest and strongest phases of NAO and assess the extent to which they affect the duration of ice cover. A large increase in the mean air temperature (1.8°C) occurred in the years 1960–2010. Trendline analysis indicates that the number of days with ice cover declined by about 60 days and the maximum thickness of ice cover decreased by about 10 cm. Oceanographic research employs satellite image data in order to accurately assess human impact. Therefore, ice cover forecasts generated for consecutive winter seasons may become an important tool in this type of assessment.

With the onset of cold weather, heat transfer through the walls and especially the roofs of bird rooms, unlike in the summer period, increases significantly, which leads to a sharp decrease in warm indoor air. Therefore, in order to maintain the regulatory conditions of the ventilation system, it is reduced to a transitional mode, and then to a minimum ventilation mode. Low ventilation repeatability limits inside the building determine the minimum volume of air supplied by the ventilation system. The economic and production efficiency of poultry farming is related to the premises and climatic conditions, which can seriously affect the well-being of broilers. Heat stress contributes to behavioral, physiological and biochemical changes in broilers, which leads to economic losses. The purpose of this study is to influence the annual efficiency of the poultry house ventilation system, mainly in summer, in order to evaluate the efficiency during the reconstruction of the cooling system, that is, to improve the ventilation system of the room, as well as to give an economic assessment of waste disposal technology. Energy- and resource-saving, environmentally friendly technologies and technological tools, plastic-type recuperators, a bird droppings removal system, as well as lighting and irrigation processes were taken as the object of the study. In conducting our research, let’s use classical and special methods, including a decomposition method for analyzing the functional use of recycling technology, a logical and linguistic method for assessing the safety of nitrogen during disposal, microclimate technology, nutrition and storage. Based on the mathematical model, a computer research methodology was developed to improve the energy efficiency of the poultry house. Using a computer program, the number of LEDs (α = 200, I0 = 20 cd) for an enclosure measuring 66×12 m is N = 273 pieces, and the number of LEDs (α = 200, I0 = 20 cd) for a building measuring 78×18 m is N = 259 pieces. The design parameters of the illuminators are justified. [ABSTRACT FROM AUTHOR]

• There are obvious regional differences in the freezing and thawing processes of seasonally frozen ground across China. • Seasonally frozen ground is generally characterized by one-way freezing but by one-way or two-way thawing. • The soil freeze–thaw process is dominated primarily by air temperature. • Close associations of air temperature with soil freeze depth are greatly modulated by snow cover in snowy regions. Observations from 1,047 meteorological stations from September 1, 2006 to August 31, 2015 revealed regional differences in the freezing and thawing processes of seasonally frozen ground (SFG) across China. SFG generally undergoes a one-way freezing process (i.e., top-down), and the stations with a large freeze depth generally experienced long freeze durations. During the thawing process, soil is generally characterized by two-way thawing (i.e., top-down and bottom-up) in the region north of 35′N, especially north of 30′N (except in northeastern China). The onset of thawing from the bottom occurs earlier than that from the top at most stations in the two-way thawing region. The stations exhibiting one-way thawing (i.e., bottom-up) were mainly located on the southern edge of eastern China (east of 110°E) and in southern part of Xinjiang and southeast part of the Qinghai–Tibet Plateau. The freezing process lasts several days to more than four months longer than the soil thawing process, and this difference tends to be larger in high-latitude and high-altitude regions. All of the sites experienced a discontinuous freeze–thaw process, the station-average duration of which was less than a quarter of that of the continuous freeze–thaw process. Strong associations of soil freeze depth with air temperature (as characterized by the air freezing index and air thawing index) implied a dominant influence of air temperature on the soil freeze–thaw process. During the freezing process, this relationship was partially modulated by snow cover in snowy regions, such as northeast China, northwest China, and the eastern Tibetan Plateau. This paper provides the first overview of regional differences in the freezing and thawing processes of SFG over China, and the findings improve our understanding of the soil freeze–thaw process and provide important information to support research into regional landscapes, ecosystems, and hydrological processes. [ABSTRACT FROM AUTHOR]

Thermal Discomfort Index has traditionally relied on parameters such as air temperature and relative humidity, obtained either from meteorological ground stations or through land-physical approaches estimated independently by satellites. These methods often fall short in adequately capturing both seasonal and detailed local spatial variations. This study addresses these limitations by establishing the Surface Thermal Discomfort Index (STDI), a composite of the Meteorological Discomfort Index (MDI) and the Discomfort Index over the land surface (DI-Land). Focused on Ibu Kota Negara Nusantara (IKN) in East Kalimantan and neighboring cities, MDI is derived from reanalysis data (ERA5-Land), validated with ground station data, while DI-Land is produced primarily from Landsat-8. An equal weighting factor was applied to MDI and DI-Land for estimating STDI. Results indicate that STDI captures both seasonal and spatial variations, reaching peak level in May and October, and hitting a low point in July. The spatial distribution of STDI is influenced by landuse types. In 2023, IKN experienced an STDI of 26.2 °C, while Balikpapan and Samarinda recorded at 26.5 and 26.4 °C, respectively. Compared to previous study in Jakarta, IKN and neighboring cities's STDI are higher up to 0.2 °C, remaining within the partially comfortable range in the tropics. Projecting IKN's development until 2045, an annual MDI increase of 0.01 °C is anticipated. Moreover, a 4% rise in built-up areas is expected to elevate STDI by 0.1–0.2 °C. This study provides insights into the thermal discomfort status in cities across East Kalimantan, anticipating a gradual increase in discomfort levels during the development of IKN. [ABSTRACT FROM AUTHOR]

The focus and goal of this effort are to evaluate how the regime of climatic factors has changed and how it has affected the regime of river flow. The evaluation technique is based on the utilization of long-term and intraannual observational data on air temperature, precipitation, and water discharge. In order to analyze observational data, long-term series of observed parameters were plotted and statistical analysis was used. The study's principal findings include an examination of the dynamics of average annual values of air temperature, precipitation, and water flow from 1960 to 2019. According to the evaluation, there has been a 2.0-2.5°C increase in air temperature in recent years. The analysis of a long-term series of data revealed that the trend line of average precipitation values remained virtually constant over the whole observed time. An assessment of the fluctuations in the average annual water flows of the rivers of the Surkhandarya basin over a long period of time revealed no change in the average values of water discharges, either decreasing or increasing. However, assessments of the intra-annual distribution of atmospheric precipitation and water flow have revealed that there has been a shift in maximum precipitation values from winter to spring in recent years, as well as a shift in water flow from July-August to June-July. Simultaneously, a study of river feeding types revealed that in recent years, the indicator of feeding type began to fall from 0.30, corresponding to snow-glacial feeding, to 0.10, corresponding to snow-rain feeding. The main conclusion of this work is to establish the impact of climate change on the intra-annual regime of precipitation and water flow in rivers, as well as on changes in the type of river feeding. The main factor behind these changes is an increase in air temperature. [ABSTRACT FROM AUTHOR]

Several developing countries in Asia are relying on agricultural practices in which climate change is a challenging concern. The trends change in atmospheric parameters and vegetation indices are questioned and vague. Consequently, the trend variations of the environmental characteristics were introduced in the first decade of 21 century to better understand the trend changes and envisage the relationship between the atmospheric parameters and the vegetation cover. The Moderate Resolution Imaging Spectroradiometer and the Atmospheric Infrared Sounder provide several atmospheric parameters, e.g., Air Temperature, Aerosol Optical Depth (AOD), and Carbon Dioxide (CO2). In addition, NDVI was estimated and aimed at the role of the interactions among the atmospheric parameters based on statistical analysis. The results showed that AOD and NDVI were air temperature dependent; meanwhile, AOD, NDVI, and air temperature were exchanging and influencing one another. Furthermore, the average minimum air temperature of the region increased during this period and carbon dioxide was a time-dependant parameter that was rising at a steep slope, which may lead to environmental crises. The trend analysis outlines what policies, programs, and procedures should be used to balance the ecosystem and mitigate the climatic negative effects. The Current research strengthens on the social awareness and the international cooperation to reduce socio-economic and political harm and loss in the face of climate change. [ABSTRACT FROM AUTHOR]

• Numerical investigation of human breathing zones in a semi-outdoor environment. • Local flow characteristics and BZs were studied using different crowded human-block arrays. • Crowd level significantly influenced the airflow field and BZ configuration. • Air temperature and wind direction affected the horizontal and vertical propagation of the BZs. This paper presents a comprehensive numerical investigation to predict the human breathing zones (BZs) in crowded semi-outdoor environments. The computational domain consisted of a nine-human block array with integrated nasal cavities subjected to the lower part of the atmospheric boundary layer. Five crowding levels, seven wind directions, and inflow ambient air temperatures (ranging from 10 to 31 °C) were tested to examine the horizontal and vertical formations of the BZs. Validation and verification tests were performed through comparisons with experimental results, a grid independence test, and an evaluation of various randomized distribution scenarios to minimize the uncertainties of the computational fluid dynamics analyses. The horizontal extension of the BZs tripled as the crowding level increased from 0.325 to 4.0 m2/capita. However, the lateral extension was insensitive and remained within 10 cm of the nostrils. Human models can inhale air close to the cheek, neck, and shoulders when an oblique flow is assumed. As the air temperature increased, individuals tended to inhale air from the upper regions, which was influenced by the interrelated thermal properties of the human body. Consequently, under high-temperature conditions, there may be an increased probability of gas-phase contaminant inhalation over greater horizontal distances. [ABSTRACT FROM AUTHOR]

In response to the urgent global threat posed by human-induced extreme climate hazards, heatwaves are still systematically under-reported and under-researched in Thailand. This region is confronting a significant rise in heat-related mortality, which has resulted in hundreds of deaths, underscoring a pressing issue that needs to be addressed. This research article is one of the first to present a solution for assessing heatwave dynamics, using machine learning (ML) algorithms and geospatial technologies in this country. It analyzes heatwave metrics like heatwave number (HWN), heatwave frequency (HWF), heatwave duration (HWD), heatwave magnitude (HWM), and heatwave amplitude (HWA), combining satellite-derived land surface temperature (LST) data with ground-based air temperature ( T air ) observations from 1981 to 2019. The result reveals significant marked increases in both the frequency and intensity of daytime heatwaves in peri-urban areas, with the most pronounced changes being a 0.45-day/year in HWN, a 2.00-day/year in HWF, and a 0.27-day/year in HWD. This trend is notably less pronounced in urban areas. Conversely, rural regions are experiencing a significant escalation in nighttime heatwaves, with increases of 0.39 days/year in HWN, 1.44 days/year in HWF, and 0.14 days/year in HWD. Correlation analysis ( p < 0.05 ) reveals spatial heterogeneity in heatwave dynamics, with robust daytime correlations between T air and LST in rural (HWN, HWF, HWD, r > 0.90 ) and peri-urban (HWM, HWA, r > 0.65 ) regions. This study emphasizes the importance of considering microclimatic variations in heatwave analysis, offering insights for targeted intervention strategies. It demonstrates how enhancing remote sensing with ML can facilitate the spatial–temporal analysis of heatwaves across diverse environments. This approach identifies critical risk areas in Thailand, guiding resilience efforts and serving as a model for managing similar microclimates, extending the applicability of this study. Overall, the study provides policymakers and stakeholders with potent tools for climate action and effective heatwave management. Furthermore, this research contributes to mitigating the impacts of extreme climate events, promoting resilience, and fostering environmental sustainability. [ABSTRACT FROM AUTHOR]

Ice formation on roads leads to a higher incidence of accidents and increases winter de-icing/anti-icing costs. This study analyzed 3 years (2019–2021) of Road Weather Information System (RWIS) sub-hourly measurements collected by the Montana Department of Transportation (MDT) to understand the first-order factors of road ice formation and its mechanisms. First, road ice is formed only when the road pavement surface temperature is equal to or below the freezing point (i.e., 32 °F (i.e., 0 °C)), while the corresponding 2 m air temperature could be above 32 °F. Nevertheless, when the road pavement was below 32 °F ice often did not form on the roads. Therefore, one challenge is to know under what conditions road ice forms. Second, the pavement surface temperature is critical for road ice formation. The clear road (i.e., with no ice or snow) surface pavement temperature is generally warmer than the air temperature during both day and night. This feature is different from a natural land surface, where the land skin temperature is lower than the air temperature on cloud-free nights due to radiative cooling. Third, subsurface temperature, measured using a RWIS subsurface sensor below a road surface, did not vary as much as the pavement temperature and, thus, may not be a good index for road ice formation. Fourth, urban heat island effects lead to black ice formation more frequently than roads located in other regions. Fifth, evaporative cooling from the water surface near a road segment further reduces the outlying air temperature, a mechanism that increases heat loss for bridges or lake-side roads in addition to radiative cooling. Additionally, mechanical lifting via mountains and hills is also an efficient mechanism that makes the air condense and, consequently, form ice on the roads. Forecasting road ice formation is in high demand for road safety. These observed features may help to develop a road ice physical model consisting of functions of hyper-local weather conditions, local domain knowledge, the road texture, and geographical environment. [ABSTRACT FROM AUTHOR]

Daily time series with continuous records of mean air and soil temperature from 127 meteorological stations—fairly distributed over the country—were used to compute monthly temperature trends, as well as changes in the timing of the first and the last frost days over Romania since 1961. Results show that the frequency of the number of days with daily temperature averages below 0 °C in case of air and soil surface temperature is stable for most months, except for January, when (for both soil and air temperature), the number of days with a temperature below 0 °C is decreasing in the majority of the stations. The occurrence of the first day with (mean air and soil surface) temperatures below 0 °C, presents a delay in the south, south-east, and west, and an earlier occurrence in eastern and central regions. The occurrence of the last day with a mean air and soil surface temperature below 0 °C shows a stable trend for most stations (except for some small areas in the north, south-east and south-west of Romania). The regime of the land temperature is more stable, due to the physical characteristics of the soil, compared to the more versatile atmosphere. Linkages between thermal parameters and large-scale atmospheric circulation are also discussed. [ABSTRACT FROM AUTHOR]

Remote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), land use and cover (LULC) changes, and the identification of urban heat island (UHI) and surface urban heat island (SUHI) phenomena. This research focuses on the nexus between LULC alterations and variations in LST and air temperature (Tair), with a specific emphasis on the intensified SUHI effect in Kharkiv, Ukraine. Employing an integrated approach, this study analyzes time-series data from Landsat and MODIS satellites, alongside Tair climate records, utilizing machine learning techniques and linear regression analysis. Key findings indicate a statistically significant upward trend in Tair and LST during the summer months from 1984 to 2023, with a notable positive correlation between Tair and LST across both datasets. MODIS data exhibit a stronger correlation (R2 = 0.879) compared to Landsat (R2 = 0.663). The application of a supervised classification through Random Forest algorithms and vegetation indices on LULC data reveals significant alterations: a 70.3% increase in urban land and a decrement in vegetative cover comprising a 15.5% reduction in dense vegetation and a 62.9% decrease in sparse vegetation. Change detection analysis elucidates a 24.6% conversion of sparse vegetation into urban land, underscoring a pronounced trajectory towards urbanization. Temporal and seasonal LST variations across different LULC classes were analyzed using kernel density estimation (KDE) and boxplot analysis. Urban areas and sparse vegetation had the smallest average LST fluctuations, at 2.09 °C and 2.16 °C, respectively, but recorded the most extreme LST values. Water and dense vegetation classes exhibited slightly larger fluctuations of 2.30 °C and 2.24 °C, with the bare land class showing the highest fluctuation 2.46 °C, but fewer extremes. Quantitative analysis with the application of Kolmogorov-Smirnov tests across various LULC classes substantiated the normality of LST distributions p > 0.05 for both monthly and annual datasets. Conversely, the Shapiro-Wilk test validated the normal distribution hypothesis exclusively for monthly data, indicating deviations from normality in the annual data. Thresholded LST classifies urban and bare lands as the warmest classes at 39.51 °C and 38.20 °C, respectively, and classifies water at 35.96 °C, dense vegetation at 35.52 °C, and sparse vegetation 37.71 °C as the coldest, which is a trend that is consistent annually and monthly. The analysis of SUHI effects demonstrates an increasing trend in UHI intensity, with statistical trends indicating a growth in average SUHI values over time. This comprehensive study underscores the critical role of remote sensing in understanding and addressing the impacts of climate change and urbanization on local and global climates, emphasizing the need for sustainable urban planning and green infrastructure to mitigate UHI effects. [ABSTRACT FROM AUTHOR]

Near-surface air temperature (Ta) is a key variable in global climate studies. Global climate models such as ERA5 and CMIP6 predict various parameters at coarse spatial resolution (>9 km). As a result, local phenomena such as the urban heat islands are not reflected in the model's outputs. In this study, we address this limitation by downscaling the resolution of ERA5 (9 km) and CMIP6 (27 km) Ta to 1 km, employing two different machine learning algorithms (XGBoost and Deep Learning). Our models leverage a diverse set of features, including data from satellites (land surface temperature and normalized difference vegetation index), from ERA5 and CMIP6 climate models (e.g., solar and thermal radiation, wind), and from digital elevation models to develop accurate machine learning prediction models. These models were rigorously validated against observations from 98 meteorological stations in the East Mediterranean (Israel) using a standard cross-validation technique as well as a leave-one-group-out on the station ID evaluation methodology to avoid overfitting and dependence on geographic location. We demonstrate the sensitivity of the downscaled Ta to local land cover and topography, which is missing in the climate models. Our results demonstrate impressive accuracy with the Deep Learning-based models, obtaining Root Mean Squared Error (RMSE) values of 0.98 °C (ERA5) and 1.86 °C (CMIP6) for daily Ta and 2.20 °C (ERA5) for hourly Ta. Additionally, we explore the impact of the various input features and offer an extended application for future climate predictions. Finally, we propose an enhanced evaluation framework, which addresses the problem of model overfitting. This work provides practical tools and insights for building and evaluating Ta downscaling models. The code and data are publicly shared online. [ABSTRACT FROM AUTHOR]

Understanding the coupling effect of multiple factors on particle deposition above the near-wall heat source is vital for human health, manufacturing, treasure protection and so on. This study investigated the concentrations and decay rate loss coefficient of particles in an experimental chamber with different heat-source configurations. Thirty-nine cases were created by changing the surface temperature and the shape of the near-wall heat source as well as the angular velocity of the six-bladed fan. The results reveal that the temperature, relative humidity, air velocity and the shape of the near-wall heat sources all exhibit different degrees of influence on the particle deposition. Also, the impacts of these factors on particle deposition on the vertical wall above near-wall heat sources can be further influenced by the size and location of particles. [ABSTRACT FROM AUTHOR]

Temperature analysis is of special interest in polar areas because temperature is an essential variable in the energy exchange between the Earth's surface and atmosphere. Although land surface temperature (LST) obtained using satellites and air temperature (Ta) have different physical meanings and are measured with different techniques, LST has often been successfully employed to estimate Ta. For this reason, in this work, we estimated Ta from LST MODIS collection 6 (C6) and used other predictor variables. Daily mean Ta was calculated from Spanish State Meteorological Agency (AEMET) stations data on the Livingston and Deception Islands, and from the PERMASNOW project stations on Livingston Island; both islands being part of the South Shetland Islands (SSI) archipelago. In relation to our previous work carried out in the study area with collection 5 (C5) data, we obtained higher R2 values (R2CV = 0.8, in the unique model with Terra daytime data) and lower errors (RMSECV = 2.2 °C, MAECV = 1.6 °C). We corroborated significant improvements in MODIS C6 LST data. We analyzed emissivity as a possible factor of discrepancies between C5 and C6, but we did not find conclusive results, therefore we could not affirm that emissivity is the factor that causes differences between one collection and another. The results obtained with the applied filters indicated that MODIS data can be used to study Ta in the area, as these filters contribute to the reduction of uncertainties in the modeling of Ta from satellites. [ABSTRACT FROM AUTHOR]

The multidecade, seasonal, and interannual variability of hydrometeorological parameters of the Black Sea has been analyzed with reference to the population dynamics of invasive ctenophores. Polynomial approximations of average annual time series indicate a nonlinear character of the long-term variability of the studied parameters. Two 20- and 40-year intervals with multidirectional trends of parameter changes have been revealed. The effect of variability of abiotic factors on the two main ecosystem drivers, i.e., invasive ctenophore Mnemiopsis leidyi A. Agassiz, 1865 and its predator Beroe ovata Bruguière, 1789, has been analyzed. M. leidyi invaded the Black Sea in the 1980s, followed by the B. ovata invasion in the 1990s. The M. leidyi invasion caused degradation of the ecosystem at all trophic levels, including fish resources. The subsequent introduction of its predator, B. ovata, provided a significant restoration of the ecosystem and fish resources in the Black Sea; this process still continues in the Sea of Azov. The interannual dynamics of both ctenophores follows water temperature changes, but only if it varies within the limits favorable for these species. In the case of exceeding these limits, a sharp reduction of their population magnitudes occurs. [ABSTRACT FROM AUTHOR]

High-resolution air temperature distribution data are of crucial significance for studying climate change and agriculture in the Yellow River Basin. Obtaining accurate and high-resolution air temperature data has been a persistent challenge in research. This study selected the Yellow River Basin as its research area and assessed multiple variables, including the land surface temperature (LST), Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), slope, aspect, longitude, and latitude. We constructed three downscaling models, namely, ET, XGBoost, and LightGBM, and applied a stacking ensemble learning algorithm to integrate these three models. Through this approach, ERA5-Land reanalysis air temperature data were successfully downscaled from a spatial resolution of 0.1° to 1 km, and the downscaled results were validated using observed data from meteorological stations. The results indicate that the stacking ensemble model significantly outperforms the three independent machine learning models. The integrated model, combined with the selected set of multiple variables, provides a feasible approach for downsizing ERA5 air temperature data. The stacking ensemble model not only effectively enhances the spatial resolution of ERA5 reanalysis air temperature data but also improves downscaled results to a certain extent. The downscaled air temperature data exhibit richer spatial texture information, better revealing spatial variations in air temperature within the same land class. This research outcome provides robust technical support for obtaining high-resolution air temperature data in meteorologically sparse or topographically complex regions, contributing significantly to climate, ecosystem, and sustainable development research. [ABSTRACT FROM AUTHOR]

Economic development limits the living quality of rural residents. In particular, the residential buildings in northern China generally have poor thermal comfort in winter, which affects the physical and mental health of residents. Because of the separation of rooms, residents who live in cave dwellings often have to enter and leave rooms in the course of their daily lives, which leads to worse thermal feelings in winter. Because of the low price and the wind insulation and heat storage, sunrooms are widely used in renovations of rural houses. The traditional purpose of the addition of a sunroom is to provide a buffer room between outdoor and indoor spaces. This manuscript focuses on improving the degree of thermal comfort by means of a sunroom connecting all rooms. This study selected two families with the same number of members and similar daily activities as the study cases. One of the families had a sunroom built to connect its bedroom, living room, and washroom. The household's air temperature and human comfort were measured both on holidays and on workdays. It is demonstrated that adding a sunroom can significantly stabilize the thermal environment and increase the air temperature in both the bedroom and the living room. Adding a sunroom can increase the air temperature of a cave dwelling's main room by 1.0 °C on workdays and 4.3 °C on holidays. A cave dwelling with a sunroom can also provide residents with a decent level of human comfort for 24.4% of their daily time on workdays and 39.1% of the time during holidays. This research demonstrates that a sunroom can not only increase the air temperature in cave dwellings but also enhance the stability of human comfort. The conclusion provides new renovation ideas for improving the living comfort of cave dwellings. [ABSTRACT FROM AUTHOR]

Observations from 1,047 meteorological stations from September 1, 2006 to August 31, 2015 revealed regional differences in the freezing and thawing processes of seasonally frozen ground (SFG) across China. SFG generally undergoes a one-way freezing process (i.e., top-down), and the stations with a large freeze depth generally experienced long freeze durations. During the thawing process, soil is generally characterized by two-way thawing (i.e., top-down and bottom-up) in the region north of 35′N, especially north of 30′N (except in northeastern China). The onset of thawing from the bottom occurs earlier than that from the top at most stations in the two-way thawing region. The stations exhibiting one-way thawing (i.e., bottom-up) were mainly located on the southern edge of eastern China (east of 110°E) and in southern part of Xinjiang and southeast part of the Qinghai–Tibet Plateau. The freezing process lasts several days to more than four months longer than the soil thawing process, and this difference tends to be larger in high-latitude and high-altitude regions. All of the sites experienced a discontinuous freeze–thaw process, the station-average duration of which was less than a quarter of that of the continuous freeze–thaw process. Strong associations of soil freeze depth with air temperature (as characterized by the air freezing index and air thawing index) implied a dominant influence of air temperature on the soil freeze–thaw process. During the freezing process, this relationship was partially modulated by snow cover in snowy regions, such as northeast China, northwest China, and the eastern Tibetan Plateau. This paper provides the first overview of regional differences in the freezing and thawing processes of SFG over China, and the findings improve our understanding of the soil freeze–thaw process and provide important information to support research into regional landscapes, ecosystems, and hydrological processes.

This paper presents findings on the correlation between surface air temperature and lightning activity in Colombia. The air temperature data spans several decades, while the lightning data covers 17 years. The temperature records come from nine cities, and the lightning data covers about 50% of the territory. Despite differences in sampling rates and dataset sizes, the findings show a positive correlation between surface air temperature and lightning activity, suggesting a possible relationship between warmer conditions and increased lightning activity.