Air Temperature Data Research Articles

Avoiding mortality: timing prescribed burns in ornate box turtle habitat

AbstractFire is a vital management tool for maintaining prairie ecosystems. Prescribed burns control invasive species, regulate succession, stimulate plant growth, and are a cheap and effective method for removing excess biomass; however, fire can also inadvertently cause wildlife mortality, placing land managers in a challenging situation. Turtles are especially at risk of mortality from fire because of their low mobility and population sensitivity to reductions in adult survival. We studied ornate box turtles (Terrapene ornata) at 3 sites in Illinois, USA, from 2019–2022 to determine the best predictors of above‐ground activity so land managers can conduct prescribed burns when turtles are underground. We used turtle shell temperature, air temperature, soil temperature, and precipitation data to develop a predictive model of above‐ground activity. The best model for predicting above‐ground activity included an interaction between day of year and current air temperature. Earlier in spring and later in fall, above‐ground activity is more likely at higher air temperatures compared to later in spring and earlier in fall when the same likelihood of above‐ground activity is predicted at lower air temperatures. In spring, we recommend burning in Illinois ornate box turtle habitat before 1 April when air temperature is <10°C and in fall after 1 November when air temperature is <15°C. Above these temperature thresholds, there is a >5% likelihood that turtles in northern populations are above ground.

Evolution of surface ozone pollution pattern in eastern China and its relationship with different intensity heatwaves

With climate warming, eastern China has experienced a significant increase in temperature accompanied by intensified ozone pollution. We aimed to investigate the spatiotemporal patterns and relationships between ozone levels and temperature in eastern China using observation-based ozone data from 418 air quality monitoring stations and temperature data from ERA5. The summer maximum temperature and annual ozone concentration in eastern China increased significantly between 2015 and 2022, with increases rate of 10% and 2.84 μg/m3 yr−1, respectively. The baseline ozone concentration was increasing over time. The average difference in MDA8 O3 concentration in spring, summer, and autumn decreased, with more ozone pollution spreading into spring and autumn, indicating a trend of prolonging the ozone season. During the June-July-August (JJA) period of 2015–2022, heatwaves increased significantly in eastern China. The frequency of heatwave events >10 days played a vital role in exacerbating ozone pollution. During the JJA period, the increase rate in MDA8 O3 concentration was 9.31 μg/m3 yr−1 during heatwave periods, significantly higher than that during non-heatwave periods (4.01 μg/m3 yr−1). The correlation between MDA8 O3 concentration and temperature was as high as 0.99, indicating that temperature was vital in ozone formation during the JJA period in eastern China. This study suggests that more stringent actions are needed to control ozone-precursor compounds during frequent summertime heatwaves in eastern China.

Air Temperature Variations in Multiple Layers of the Indonesia Earthquake Based on the Tidal Forces

The air temperature changes in the Palu MW7.5 earthquake in Indonesia on 28 September 2018 were analyzed, based on the additive tectonic stress caused by celestial tidal-generating forces (ATSCTF) and air temperature data from the National Center for Environmental Prediction (NCEP). This paper explored the variation characteristics of three-dimensional stratified of air temperature caused by seismic activity and the coupling relationship between air temperature changes and the tidal force. The background information for air temperature calculation was obtained from the tidal force changes, and the air temperature increment method was used to study the temperature evolution process of different periods in the study area. The results found that the tidal force acting on the critical state earthquake faults may be an important external factor inducing earthquakes, and there was indeed a significant air temperature increase anomaly during the Palu MW7.5 earthquake. The paper also summarized the abnormal characteristics of air temperature caused by seismic activity: the air temperature closer to the land’s surface has greater anomaly amplitude and a wider anomaly area than that of the upper air.

The Influence of pH Dynamics on Modeled Ammonia Emission Patterns of a Naturally Ventilated Dairy Cattle Building

Ammonia emission rates from naturally ventilated livestock housing systems can be estimated in multiple ways. By coupling different modeling approaches towards a semi-mechanistic barn-scale ammonia emission model, we investigated the influence of urine puddle pH dynamics on the (sub)daily and seasonal pattern of ammonia emissions. We compared the simulated ammonia emission patterns using about ten months of on-farm measurements obtained from a naturally ventilated dairy cattle building with a scraped solid floor in Northern Germany. The dataset included gas concentration measurements as well as wind data (ranging from 0 m s−1 to about 8.6 m s−1) and air temperature data (ranging from about −4 ∘C to about 32 ∘C), the average number of housed cows (about 380) and information on the average cow mass (about 700 kg). In addition, the average dry matter intake, total gross energy intake and nitrogen intake were used to model the ammonia emission potential. In the emission modeling, we considered two potential types of pH dynamics in the urine puddles: a saturating scenario and a peaking scenario. For both of them, 21 different combinations of initial pH and maximum pH were considered within a range of 6.5 to 11. We showed that the non-linear interaction of the puddle pH and temperature caused specific emission patterns, where the degree of influence of the two parameters changed over the course of the emission process. Low initial pH values together with high asymptotic pH values were associated with the largest emissions. Considering the same asymptotic pH value, the higher the initial pH value, the lower the observed emissions; especially when assuming peak pH dynamics, the emission values were significantly lower. In natural pH settings (i.e., low to intermediate initial pH and intermediate asymptotic pH), the winter emissions were considerably lower than the summer emissions (i.e., the winter emission was about half of the summer emission, as observed in the on-farm studies). In contrast, artificial pH settings with high pH values led to markedly lower emissions in the summer (i.e., the summer emission was about the same as winter emission), reducing the total annual emission value. Our sensitivity study indicated that the urine puddle alkalizing dynamics play a key role in the overall emission model accuracy in order to capture seasonal and diurnal variability of the ammonia emission of naturally ventilated dairy cattle barns in mechanistic modeling approaches. Thus, future studies should investigate the range of pH dynamics that naturally occur in urine puddles in cattle barns depending on the flooring material, the entry of litter or feed leftovers, the cleaning and cooling system (e.g., in terms of use of water) and so on in order to further refine the emission model.

КЛИМАТИЧЕСКИЙ ПРОФИЛЬ ВОДОСБОРОВ ПРАВОБЕРЕЖНЫХ ПРИТОКОВ РЕК БУКТЫРМИНСКОГО ВОДОХРАНИЛИЩА

In this article, the analysis of the climatic profile of the catchment area of the right tributaries of the Buktyrma reservoir was carried out. The climatic data of air temperature, precipitation for the modern period from 1990 to 2021, and wind speed and direction for the period 1966...2000 were taken as a basis. The results of the work showed that, in the territory under consideration, the average annual air temperature ranges from -1.8 to +3.8 0C. Precipitation in most of the territories is unevenly distributed over time. In the warm season, 60...75% of the annual precipitation falls, in the cold season up to 15%. In monthly precipitation distributions, the maximum is observed in June and July, and the minimum in January. The annual precipitation at the high-altitude weather stations Markakol Reserve (1372 m), Katon-Karagai (1081 m) and Leninogorsk (809 m) for the period 1991...2020 is 441...634 mm, and at the weather stations located in the low-mountain part of the considered territory Kurshim (433 m) and Ulken-Naryn (402 m) varies from 256 mm to 395 mm. The values of the average monthly wind speed, calculated from the daily data series, in the annual context showed a high repeatability of the winds of the eastern direction. In the long-term section, the minimum wind speed is observed at the Markakol Reserve station 0.6 m/s (January), and the maximum 4.2 m/s (April) at the Katon-Karagai station

Evaluation des structures locales de stockage des grains et des graines dans le Département des Bamboutos, Cameroun

Post-harvest food losses remain a major problem in sub-Saharan Africa. A study of local storage structures aimed at carrying out a diagnostic analysis of post-harvest losses in grains and seeds in the Bamboutos department, West region, Cameroon. A questionnaire survey was carried out among 240 producers in 12 villages, spread over 3 subdivisions. Air temperature and relative humidity data were collected over 80 days at two storage stores in this study area. Twelve samples of food infested by insect were taken and deposited in the entomology laboratory of the Faculty of Agronomy and Agricultural Sciences at Dschang University for identification. It emerges from this investigation that corn is the only grain stored by producers. Beans and peanuts are mostly stored seeds, while pistachios and soybeans are seeds stored by few farmers. Three structures are used for grain storage: granaries (86.25%), cribs (10.42%) and stores (3.33%). Among the containers, two are mainly used for seed storage: barrels (58.38%) and bags (39.25%); buckets with lids are used by a few farmers (2.36%). The storage period of corn is between 8 and 12 months. The storage period of peanuts is between 6 and 8 months and beans between 4 and 5 months. The use of chemicals, inert matter (ash) and plant substance (neem) are the three methods of protection used in Bamboutos. Bamboutos storage stores promote the development of molds and insects. Three major groups of biological agents are responsible for spoilage: insects (Callosobruchus maculatus F., Sitophilus zeamais M. and Acanthoscelides obtectus S.), rodents (Rattus norvegicus, Rattus rattus and Mus musculus) and molds. Insects are the pests that cause the most damage to stored products according to 55.42% of producers. According to 23.58% of producers, it is rodents and 15% molds. Farmers estimate that the damage to the stock is between 7% and 30% after destocking of maize. This damage is between 2% and 5% after removal of beans from storage and less than 3% after removal of peanuts. Farmers underestimate the damage caused by pests on corn, which is around 40% after 10 months of storage in granaries. It is therefore essential for Cameroonian farmers to master and respect the storage techniques according to the storage structures and the foodstuffs stored.

To aspirate or not to aspirate – Impact of active versus passive ventilation on urban heat (island) indicators

The present paper reports on the impact of ventilation mode – active versus passive – of instrument screens for air temperature measurements on canopy urban heat (island) indicators. This is done using air temperature data gathered in the Antwerp (Belgium) area in July 2013, in an experimental set-up composed of an urban and a nearby rural climate station, each equipped with both actively and passively ventilated temperature sensors. The resulting data shows an air temperature bias between the passively and actively ventilated measurements of up to approximately 2 °C, the highest values occurring under conditions of a high solar radiation load and low wind speed. Yet, the canopy urban heat island (UHI) increment, i.e., the urban-rural air temperature difference, is hardly affected by the ventilation method, which is ascribed to the fact that high UHI increments occur mainly at night when, in absence of solar radiation, active ventilation has a lesser influence on measured temperature. Conversely, urban heat indicators involving daytime air temperature, such as heatwave degree days (HWDD) or Steadman's apparent temperature, tend to produce overly high values when use is made of passively ventilated temperature measurements.

Diurnal urban heat risk assessment using extreme air temperatures and real-time population data in Seoul

Previous heat risk assessments have limitations in obtaining accurate heat hazard sources and capturing population distributions, which change over time. This study proposes a diurnal heat risk assessment framework incorporating spatiotemporal air temperature and real-time population data. Daytime and nighttime heat risk maps were generated using hazard, exposure, and vulnerability components in Seoul during the summer of 2018. The hazard was derived from the daily extreme air temperatures obtained using the stacking machine learning model. Exposure was calculated using de facto population density, and vulnerability was assessed using demographic and socioeconomic indicators. The resulting maps revealed distinct diurnal spatial patterns, with high-risk areas in the urban core during the day and dispersed at night. Daytime heat risk was strongly correlated with heat-related illness ratios (R= 0.8) and accurately captured temporal fluctuations in heat-related illness incidence. The proposed framework can guide site-specific adaptation and response plans for dynamic urban heat events.

Assessing heat effects on respiratory mortality and location characteristics as modifiers of heat effects at a small area scale in Central-Northern Europe.

Heat effects on respiratory mortality are known, mostly from time-series studies of city-wide data. A limited number of studies have been conducted at the national level or covering non-urban areas. Effect modification by area-level factors has not been extensively investigated. Our study assessed the heat effects on respiratory mortality at a small administrative area level in Norway, Germany, and England and Wales, in the warm period (May-September) within 1996-2018. Also, we examined possible effect modification by several area-level characteristics in the framework of the EU-Horizon2020 EXHAUSTION project. Daily respiratory mortality counts and modeled air temperature data were collected for Norway, Germany, and England and Wales at a small administrative area level. The temperature-mortality association was assessed by small area-specific Poisson regression allowing for overdispersion, using distributed lag non-linear models. Estimates were pooled at the national level and overall using a random-effect meta-analysis. Age- and sex-specific models were also applied. A multilevel random-effects model was applied to investigate the modification of the heat effects by area-level factors. A rise in temperature from the 75th to 99th percentile was associated with a 27% (95% confidence interval [CI] = 19%, 34%) increase in respiratory mortality, with higher effects for females. Increased population density and PM2.5 concentrations were associated with stronger heat effects on mortality. Our study strengthens the evidence of adverse heat effects on respiratory mortality in Northern Europe by identifying vulnerable subgroups and subregions. This may contribute to the development of targeted policies for adaptation to climate change.

Exploring the intensity, distribution and evolution of teleconnections using climate network analysis.

Teleconnections refer to long-range climate system linkages occurring over typically thousands of kilometers. Generally speaking, most teleconnections are attributed to the transmission of energy and propagation of waves although the physical complexity and characteristics behind these waves are not fully understood. To address this knowledge gap, we develop a climate network-based approach to reveal their directions and distribution patterns, evaluate the intensity of teleconnections, and identify sensitive regions using global daily surface air temperature data. Our results reveal a stable average intensity distribution pattern for teleconnections across a substantial spatiotemporal scale from 1948 to 2021, with the extent and intensity of teleconnection impacts increasing more prominently in the Southern Hemisphere over the past 37 years. Furthermore, we pinpoint climate-sensitive regions, such as southeastern Australia, which are likely to face increasing impacts due to global warming. Our proposed method offers new insights into the dynamics of global climate patterns and can inform strategies to address climate change and extreme events.

Experimental and simulation study on the modified shape coefficient of building integrated thermal considerations for heating buildings in solar-enriched areas

The shape coefficient of building (SCB) is a measure of correlating a building’s shape and its energy consumption. However, strictly adhering to restrictions the SCB may not be conducive to reducing energy consumption, as it disregards the potential benefits of solar radiation. To address this, a novel concept and method for determining the modified shape coefficient of building (MSCB) have been introduced in considerations of heat loss and solar heat gain. Field measurements were conducted in western Sichuan to establish a comprehensive understanding of the factors influencing indoor thermal environments, involving collecting indoor air temperature data for model buildings under natural and heating conditions, including solar radiation, building orientations, and U-values. Results demonstrated that maintaining an identical SCB did not consistently ensure similar indoor thermal environments. The indoor thermal environments were significantly influenced by building orientations, U-values, and solar radiation, highlighting the limitations of relying solely on SCB. A simulation method validated through experiments was utilized to predict the dynamic heating load of an actual building. The influence of building orientations on the MSCB across different time scales and actual climatic conditions was investigated. Results illustrated a potential reduction of the SCB by up to 32.4% in the specific case. This reduction correlated with trends observed in the monthly correction factors and heat consumption, suggesting that moderate relaxation of SCB restrictions could be viable even in scenarios with high heating demands. This research contributes valuable insights into the realm of energy-efficient building design.

Decoupling the response of vegetation dynamics to asymmetric warming over the Qinghai-Tibet plateau from 2001 to 2020

Global land surface air temperature data show that in the past 50 years, the rate of nighttime warming has been much faster than that of daytime, with the minimum daily temperature (Tmin) increasing about 40% faster than the maximum daily temperature (Tmax), resulting in a decreased diurnal temperature difference. The Qinghai-Tibet Plateau (QTP) is known as the "roof of the world", where temperatures have risen twice as fast as the global average warming rate in the last few decades. The factors affecting vegetation growth on the QTP are complex and still not fully understood to some extent. Previous studies paid less attention to the explanations of the complicated interactions and pathways between elements that influence vegetation growth, such as climate (especially asymmetric warming) and topography. In this study, we characterized the spatial and temporal trends of vegetation coverage and investigated the response of vegetation dynamics to asymmetric warming and topography in the QTP during 2001–2020 using trend analysis, partial correlation analysis, and partial least squares structural equation model (PLS-SEM) analysis. We found that from 2001 to 2020, the entire QTP demonstrated a greening trend in the growing season (April to October) at a rate of 0.0006/a (p < 0.05). The spatial distribution pattern of partial correlation between NDVI and Tmax differed from that of NDVI and Tmin. PLS-SEM results indicated that asymmetric warming (both Tmax and Tmin) had a consistent effect on vegetation development by directly promoting greening in the QTP, with NDVI values being more sensitive to Tmin, while topographic factors, especially elevation, mainly played an indirect role in influencing vegetation growth by affecting climate change. This study offers new insights into how vegetation responds to asymmetric warming and references for local ecological preservation.

Improving maps of daily air temperature considering the effects of topography: Data from Espírito Santo, Brazil (2007–2020)

Mapping air temperature is essential for climate studies and its application in environmental, forest, and agricultural sciences, among others. This study aimed to spatialize daily air temperature data, creating a database corrected by the effect of relief. This study was performed in the state of Espírito Santo, Southeastern Brazil, which has an altitude range of 2892 m from the coast to the highest point in Serra do Caparaó. Meteorological data were collected from 22 meteorological stations. The potential temperature equation and adiabatic lapse rate were used to estimate air temperature considering the effect of relief. These methods were compared with the inverse distance weighting (IDW) interpolator to evaluate the gain obtained by including topographic information in the temperature estimation. The performance of methods was evaluated by cross-validation, using the coefficient of determination (R2), bias, and mean absolute error (MAE). The method with the best performance in the spatialization of air temperature data was obtained by adjusting the temperature data for the same altitude condition, using the environmental lapse rate (−6.5 °C km−1) and the IDW interpolator to spatialize data. Finally, interpolated temperature values were adjusted for the original terrain altitude (−6.5 °C km−1), using the altitude existing in each pixel of a digital elevation model. This method provided a gain of 51% in the reduction of MAE values compared with the IDW interpolator, which does not consider the effect of relief. It was used to spatialize daily air temperature data (maximum, average, and minimum) for the entire territory of Espírito Santo, Brazil, with 500-m spatial resolution.

THE INFLUENCE OF SOME CLIMATIC FACTORS ON THE EFFECTIVENESS OF ESTRUS SYNCHRONIZATION IN BEEF OF DAIRY COWS

The aim of this study was to determine the influence of the temperature humidity index (THI) on the efficiency of estrus synchronization on both beef and dairy cows. The research was carried out at APC "Azamat" of the Beskaragai district of the East Kazakhstan region of the Republic of Kazakhstan. The natural and climatic conditions of this zone make it possible to raise both beef and dairy cattle. The study involved 408 Kazakh white-headed cows aged 3 to 5 year.&#x0D; During the experimental period, the data for air temperature (T), relative humidity (φ), wind speed (υ) were collected, and the temperature humidity index (THI) was determined. The data was recorded for 30 days (10 days before synchronization, 10 days during synchronization and 10 days after synchronization). In addition, during the synchronization period, clinical parameters - body temperature (t), pulse (P), respiratory rate (Re) and rumination (R) were measured daily in 10% of animals by conventional methods.&#x0D; During the experimental period (2020-2022) THI was 74,50±1,31, 78,20±2,11, 72,80±0,7210 days before the estrus synchronization. When THI was increased by 5.6±0.03 in 2021 compared to 2020, the fertility decreased by 9.7±0.78. Similarly, when THI was increased by 7.3 ± 0.56 compared to 2022, fertility was reduced by 13.8 ± 0.36. The latter indicates that when THI increases the fertility rate decreases.&#x0D; Based on the results, there is an influence of THI on the effectiveness of estrus synchronization. Increased THI, to a certain extent, reduces both estrus expression and effectiveness of estrus synchronization leading to reduced fertility rate and increased embryonic mortality.

Detecting Extreme Temperature Events Using Gaussian Mixture Models

AbstractExtreme temperature events have traditionally been detected assuming a unimodal distribution of temperature data. We found that surface temperature data can be described more accurately with a multimodal rather than a unimodal distribution. Here, we applied Gaussian Mixture Models (GMM) to daily near‐surface maximum air temperature data from the historical and future Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations for 46 land regions defined by the Intergovernmental Panel on Climate Change. Using the multimodal distribution, we found that temperature extremes, defined based on daily data in the warmest mode of the GMM distributions, are getting more frequent in all regions. Globally, a 10‐year extreme temperature event relative to 1985–2014 conditions will occur 13.6 times more frequently in the future under 3.0°C of global warming levels (GWL). The frequency increase can be even higher in tropical regions, such that 10‐year extreme temperature events will occur almost twice a week. Additionally, we analyzed the change in future temperature distributions under different GWL and found that the hot temperatures are increasing faster than cold temperatures in low latitudes, while the cold temperatures are increasing faster than the hot temperatures in high latitudes. The smallest changes in temperature distribution can be found in tropical regions, where the annual temperature range is small. Our method captures the differences in geographical regions and shows that the frequency of extreme events will be even higher than reported in previous studies.

Apple (Malus domestica Borkh.) phenology in relation to topoclimate in Central Macedonia, Greece

Plant phenological behavior is strongly dependent on climate conditions. In the current research, the apple (Malus domestica Borkh.) phenology response to topoclimate conditions, as mainly determined by air temperature, was investigated at two agricultural regions of the Region of Central Macedonia, Greece, Giannitsa Pellas and Rachi Pierias. In this regard, phenological observations were collected in order to evaluate the timing of appearance of the following phenophases: bud burst-green leaf tips (BBCH 53), flower bud visible-still closed (BBCH 55), pink bud stage-flower petals elongating-visible sepals slightly open (BBCH 57), and full flowering at least 50% (BBCH 65). Also, air temperature data were taken from agrometeorological stations which covered the study regions. Results showed that higher maximum and lower minimum air temperatures prevailed during almost the whole year at the plain of Giannitsa than those of Rachi. Earlier appearance of the examined apple phenophases was confirmed at Giannitsa, in comparison to Rachi, increasing the damage risk of vulnerable plant tissues during the bud development and flowering from the late spring frosts. It seems that the maximum and minimum air temperatures of March are decisive for the appearance o BBCH 53, 55 and 57 phenophases while those of April are considered as crucial factors for the onset of BBCH 65. The knowledge of relation between apple tree phenology and topoclimate, in terms of air temperature, may provide valuable information for farmers' decision-making regarding the rational planning of cultivation techniques.

Spatial and temporal patterns of indicators of climate change and variability in the Arab world in the past four decades

A comprehensive assessment of the spatial and temporal patterns of the most common indicators of climate change and variability in the Arab world in the past four decades was carried out. Monthly maximum and minimum air temperature and precipitation amount data for the period 1980–2018 were obtained from the CHELSA project with a resolution of 1 km2, which is suitable for detecting local geographic variations in climatic patterns. This data was analyzed using a seasonal-Kendall metric, followed by Sen’s slope analysis. The findings indicate that almost all areas of the Arab world are getting hotter. Maximum air temperatures increased by magnitudes varying from 0.027 to 0.714 °C/decade with a mean of 0.318 °C/decade while minimum air temperatures increased by magnitudes varying from 0.030 to 0.800 °C/decade with a mean of 0.356 °C/decade. Most of the Arab world did not exhibit clear increasing or decreasing precipitation trends. The remaining areas showed either decreasing or increasing precipitation trends. Decreasing trends varied from −0.001 to −1.825 kg m−2/decade with a mean of −0.163 kg m−2/decade, while increasing trends varied from 0.001 to 4.286 kg m−2/decade with a mean of 0.366 kg m−2/decade. We also analyzed country-wise data and identified areas of most vulnerability in the Arab world.

Impact of Land Surface Temperature (LST) and Ground Air Temperature (Tair) on Land Use and Land Cover (LULC): An Investigative Study

The Land Surface Temperature (LST) is the fundamental concept of measuring the temperature of the ground or soil using remote sensing technologies. LST is an essential environmental parameter that has several uses in meteorology, climatology, agriculture, urban planning, and environmental monitoring. Remote sensing instruments, such as thermal infrared sensors aboard satellites, provide the means to capture LST data on a global scale. LST and ground air temperature ( ) are two related but distinct measures that provide valuable insights into different aspects: LST refers to the temperature of the Earth's surface, and it provides information about the temperature of the soil or crop canopy, which can impact plant growth and development, on the other hand measures the temperature of the surrounding air, which influences the rate of evaporation, transpiration, and photosynthesis in plants. In this study, a linear regression equation was developed to correlate the LST and using air temperature data of 19th March 2017, and 14th March 2021 from the two meteorological stations namely Veganpur and Main Maize Research Station (MMRS) of the Godhra region. The Landsat 8 thermal and optical bands were used to estimate LST, whereas Sentinel 2 optical data was used for the Land Use and Land Cover (LULC) purpose. To correlate LST with , eight ground truth (GT) points were collected from both stations, namely the minimum and maximum air temperatures in 2017 and 2021. The satellite derived LST data and the maximum ground air temperature ( ) were found to strongly concur, with an R2 value of 0.97. It was noted that a temperature difference of 2.9°C was found between the maximum ground air temperature and the Land Surface Temperature (LST). Study showed that from 2017 to 2021, the area used for agriculture, forestry, water bodies, and barren land decreased from 75.62 to 71.51%, 15.17 to 14.30%, 1.60 to 1.38%, and 0.03 to 0.02%, respectively, however the area used for build-up increased from 7.59 to 12.79%. During a five-year period, the study identified an enhancement in Land Surface Temperature (LST) due to an increase in built-up area. A temperature change was also seen throughout the course of the five-year period as a result of variations in LULC. Overall, it came to light that, under specific conditions and speculation, LST estimated using thermal data from Landsat 8 can be closely associated with ground air maximum temperature.

Machine Learning and Conventional Methods for Reference Evapotranspiration Estimation Using Limited-Climatic-Data Scenarios

Reference evapotranspiration (ET0) is one important agrometeorological parameter for hydrological studies and climate risk zoning. ET0 calculation by the FAO Penman–Monteith method requires several input data. However, the availability of climate data has been a problem in many places around the world, so the study of scenarios with different combinations of climate data has become essential. The aim of this study was to evaluate the performance of artificial neural network (ANN), random forest (RF), support vector machine (SVM), and multiple linear regression (MLR) approaches to estimate monthly mean ET0 with different input data combinations and scenarios. Three scenarios were evaluated: at the state level, where all climatological stations were used (Scenario I–SI), and at the regional level, where the Minas Gerais state was divided according to the climatic classifications of Thornthwaite (Scenario II–SII) and Köppen (Scenario III–SIII). ANN and RF performed better in ET0 estimation among the models evaluated in the SI, SII, and SIII scenarios with the following data combinations: (i) latitude, longitude, altitude, month, mean, maximum and minimum temperature, and relative humidity and (ii) latitude, longitude, altitude, month, mean temperature, and relative humidity. SVM and MLR models are recommended for all scenarios in situations with limited climatic data where only air temperature and relative humidity data are available. The results and information presented in this study are important for the agricultural chain and water resources in Minas Gerais state.

Evaluation of fused multisource data of air temperature based on dropsonde and satellite observation

Continuous vertical air temperature (AT) from in-situ observation is of crucial importance for understanding the atmospheric environment, but the satellite data that have complete spatial coverage lacked vertical in-situ observation data, and the vertical dropsonde data from in-situ observations only were single-point observations. Therefore, this article introduced machine learning algorithms for fusing in-situ observation and multi-satellite data to achieve spatial continuity of vertical data on a large scale. Specially, random forest (RF), support vector regression (SVR), artificial neural network (ANN) and recurrent neural network (RNN) were employed to capture the non-linear relationships between the variables and estimated AT. The pre-training process and fine-tuning process ensured the prediction of AT spatiotemporal distribution. The four models were implemented for three-dimensional AT estimating across China. Additionally, we used the radiosonde observation data to evaluate the accuracy of estimated AT data under conventional weather and typhoon conditions. Our results revealed that the RF model performed the best with the R of 0.9992, the MAE of 0.70 °C, and the RMSE of 1.04 °C at the national scale, followed by the SVR and ANN models. The RNN model exhibited promising results under typhoon conditions, which will be valuable insights for further research on the applicability of machine learning models under different weather conditions. Besides, having a larger sample size does not necessarily result in reduced errors. For instance, the MAE value for SVR in the pressure height range of 100–200 hPa was larger than that in the pressure height range of 300–400 hPa, but the former sample size was 16,324, which was 7433 higher than the latter.