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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]

The paper compares air and sea surface temperatures in recent years on two islands in the Adriatic Sea. The data measured at the climatological station Krk on the island of Krk and the main meteorological station Lastovo on the island of Lastovo are used. The island of Krk is located in the north of the Adriatic Sea and Lastovo in the south. Since a significant increase in air and sea surface temperatures has been observed over the last thirty years, the goal is to establish how they reflect at these two stations, 313 km apart. The goal of the analysis is to monitor the changes in these two islands to reduce the negative impacts they may cause. The analysis of sea temperatures showed that global warming has a greater impact in the northern Adriatic than in the southern Adriatic. Air and sea surface temperatures have a faster upward trend on Krk than on Lastovo. Similar to the Mediterranean Sea, a positive trend was observed in the Adriatic Sea for both sea surface temperature and air temperature. [ABSTRACT FROM AUTHOR]

Surface air temperature is a critical element in the surface–atmosphere interaction, energy exchange, and water cycle. Multi-source fusion reanalysis products (hereafter referred to as reanalysis) have spatiotemporal continuity and broad applicability that can provide key data support for various studies such as glacier melting, soil freeze-thaw and desertification, ecosystem, and climate change in the alpine region of the Qinghai–Tibet Plateau (QTP). Surface air temperature observations collected at 17 weather stations in the High-cold region Observation and Research Network for Land Surface Process and Environment of China (HORN) over the period of 2017–2018 are implemented to evaluate the advanced and widely used surface air temperature reanalysis datasets, which include the European Centre for Medium-Range Weather Forecasts (ECMWF) Fifth Generation Land Surface Reanalysis (ERA5L), the U.S. Global Land Data Assimilation System (GLDAS), and China Meteorological Administration Land Data Assimilation System (CLDAS). Results are as follows: (1) Evaluation results of temporal changes and spatial distribution characteristics indicate that the three reanalysis datasets are consistent with in-situ observations in the alpine region of the QTP. CLDAS is more consistent with observations and can better describe details of temperature distribution and variation than ERA5L and GLDAS. (2) For the evaluation period, CLDAS is 0.53 °C higher than the in-situ observation, while ERA5L and GLDAS are lower than the in-situ observation by −3.45 °C and −1.40 °C, respectively. (3) The accuracy of CLDAS is better than ERA5L and GLDAS under different elevations and land covers. We resampled three reanalysis datasets with a spatial resolution of 0.25° and used the two most common interpolation methods to analyze the impact of spatial resolution and different interpolation methods on the evaluation results. We found that the impact is small. In summary, the three reanalysis datasets all have certain applicability in the alpine region of the QTP, and the accuracy of CLDAS is significantly higher than ERA5L and GLDAS. The results of the present paper have important implications for the selection of reanalysis data in the studies of climate, ecosystem, and sustainable development in the QTP. [ABSTRACT FROM AUTHOR]

The urban heat island (UHI) intensity in Moscow and the influence of various meteorological parameters are discussed using weather station data. The maximal and average in-space UHI intensities, i.e., a comparison of air temperature T either in the city centre or in the whole urban area together with rural zone have averaged 1.9 and 0.9 °C, respectively, in recent years. The UHI in Moscow has stabilized over the past decade and is not growing. Under conditions of a strong anticyclone, the maximal UHI intensity in space and time reaches 11–12 °C. Low cloudiness and amplitudes of diurnal air temperature, as well as surface temperature, demonstrate the closest relationship with the UHI intensity among other parameters with the correlation coefficient of up to −0.67 for low cloudiness and the maximal UHI intensity. The effect of wind speed, total cloudiness and relative humidity on the UHI is slightly weaker, but still significant. The relationships of all meteorological parameters with the maximal UHI intensity are closer than those with the average one. The multiple correlation coefficient between the maximal UHI intensity and both parameters (low cloudiness and average daily wind speed) is 0.76–0.82. The UHI intensity function of air temperature has a minimum in the range from −4 to 0 °C; its growth both at lower and higher T is due to the influence of anticyclonic weather. The UHI intensity function of wind speed decreases with wind strength. The threshold value at which this function asymptotically approaches its lower limit is 10 m/s in the 40–200 m air layer. The UHI intensity functions of both total and low cloudiness decrease with increasing cloudiness and the differences between them are significant if the cloud cover is more than 50%. [ABSTRACT FROM AUTHOR]

This research examined the characteristics of cold days and spells in Bangladesh using long-term averages (1971–2000) of maximum (Tmax) and minimum temperatures (Tmin) and their standard deviations (SD). Cold days and spells were calculated and their rate of change during the winter months (December–February) of 2000–2021 was quantified. In this research, a cold day was defined as when the daily maximum or minimum temperature is ≤−1.5 the standard deviations of the long-term daily average of maximum or minimum temperature and the daily average air temperature was equal to or below 17 °C. The results showed that the cold days were more in the west-northwestern regions and far less in the southern and southeastern regions. A gradual decrease in cold days and spells was found from the north and northwest towards the south and southeast. The highest number of cold spells (3.05 spells/year) was experienced in the northwest Rajshahi division and the lowest (1.70 spells/year) in the northeast Sylhet division. In general, the number of cold spells was found to be much higher in January than in the other two winter months. In the case of cold spell severity, Rangpur and Rajshahi divisions in the northwest experienced the highest number of extreme cold spells against the highest number of mild cold spells in the Barishal and Chattogram divisions in the south and southeast. While nine (out of twenty-nine) weather stations in the country showed significant trends in cold days in December, it was not significant on the seasonal scale. Adapting the proposed method would be useful in calculating cold days and spells to facilitate regional-focused mitigation and adaptation to minimize cold-related deaths. [ABSTRACT FROM AUTHOR]

Short-term fluctuations in air temperature, called the daily temperature range (DTR), or its daily amplitude, have a strong impact on ecosystems, as well as on the health and well-being of people. The pronounced effect of DTR on mortality from all causes and especially on cardiovascular mortality is well documented in the scientific literature, but little is known about spatial, inter-annual, and inter-seasonal fluctuations of DTR in the Russian Far East (RFE), an area with high annual dynamics of air temperature. Data from 99 weather stations for the period from 2000 to 2019 was used to evaluate spatial and temporal DTR patterns at the RFE. A higher DTR, up to 16 °C in Toko on the border with Sakha (Yakutia) as a mean for the entire period, is typical for continental areas further to the north. Lower values are observed at continental weather stations in the south and in coastal climates (4.7 °C in Mys Alevina, Magadan Region). In general, a distinct seasonal change in DTR was found for continental locations with a significant difference from month to month throughout the year. On the annual cycle, the maximum DTR at the continental northern stations is observed in April and June–July, and at the continental southern areas in February; the minimum DTR is shown in November and December. The DTR ranges as much as from 6 °C in December to 17 °C in April in the continental Korkodon in the far north. Locations with a marine climate are characterized by a smoothed seasonal change in DTR, with obvious peaks in February–March and October, and a minimum in July–August. The downward trend in DTR for the period from 2000 to 2019, up to −0.7 °C in coastal Bolsheretsk, is based on a faster increase in the minimum daily temperature compared to the maximum, which is typical for most weather stations at the RFE and is known worldwide as a diurnal asymmetry of global warming. At the same time, an increase in the daily amplitude of air temperature (up to +0.6 °C in continental Dolinovka) was found for some localities, associated with a higher positive trend of maximum temperatures, which contradicts global patterns. [ABSTRACT FROM AUTHOR]

An analysis of spatiotemporal variability of air temperature and precipitation in the Volga Federal District (European Russia) between 1966 and 2021 was carried out. Based on data from 20 meteorological stations, relatively evenly located on the territory under consideration, the spatial distribution of average monthly and average annual air temperatures and monthly and annual precipitation was assessed; some indicators of the temporal variability of these variables in the period under consideration were calculated and analyzed. It was revealed that throughout the Volga Federal District, there was a tendency of climate warming in all months, and a slight increase in annual precipitation, except for the southeast of the district, where the precipitation trend was negative. It is noted that in the period 1955–1998, the number of negative air temperature anomalies was approximately equal to the number of positive ones; however, in the later period 1999–2021, the number of positive anomalies significantly exceeded the number of negative ones. Based on reanalysis data, climatic maps of vaporization and runoff in the Volga Federal District during 1966–2021 were created. The dependence of air temperature fluctuations on the nature of atmospheric circulation was revealed using the NAO, AO, and SCAND indices. On the example of the central part of the district (Republic of Tatarstan), some increase in summer aridity of the climate was revealed by using Budyko's dryness index, Selyaninov's hydrothermal coefficient, and Sapozhnikov's humidification coefficient. The indicators of runoff and evaporation were also calculated using the methods of Schreiber and Ivanov. Against the background of the positive trend in vaporization rates, favorable conditions for a decrease in runoff were noted. [ABSTRACT FROM AUTHOR]

The impact that climate change and urbanization are having on the thermal-energy balance of the built environment is a major environmental concern today. Urban heat island (UHI) is another phenomenon that can raise the temperature in cities. This study aims to examine the UHI magnitude and its association with the main meteorological parameters (i.e., temperature, wind speed, and wind direction) in Dubai, United Arab Emirates. Five years of hourly weather data (2014–2018) obtained from weather stations located in an urban, suburban, and rural area, were post-processed by means of a clustering technique. Six clusters characterized by different ranges of wind directions were analyzed. The analysis reveals that UHI is affected by the synoptic weather conditions (i.e., sea breeze and hot air coming from the desert) and is larger at night. In the urban area, air temperature and night-time UHI intensity, averaged on the five year period, are 1.3 °C and 3.3 °C higher with respect to the rural area, respectively, and the UHI and air temperature are independent of each other only when the wind comes from the desert. A negative and inverse correlation was found between the UHI and wind speed for all the wind directions, except for the northern wind where no correlation was observed. In the suburban area, the UHI and both temperatures and wind speed ranged between the strong and a weak negative correlation considering all the wind directions, while a strong negative correlation was observed in the rural area. This paper concludes that UHI intensity is strongly associated with local climatic parameters and to the changes in wind direction. [ABSTRACT FROM AUTHOR]

An increasing number of individuals and institutions own or operate meteorological stations, but the resulting data are not yet commonly used in the Czech Republic. One of the main difficulties is the heterogeneity of measuring systems that puts in question the quality of outcoming data. Only after a thorough quality control of recorded data is it possible to proceed with for example a specific survey of variability of a chosen meteorological parameter in an urban or suburban region. The most commonly researched element in the given environment is air temperature. In the first phase, this paper focuses on the quality of data provided by amateur and institutional stations. The following analyses consequently work with already amended time series. Due to the nature of analyzed data and their potential use in the future it is opportune to assess the appropriateness of chronological and possibly spatial interpolation of missing values. The evaluation of seasonal variability of air temperature in the scale of Brno city and surrounding area in 2015–2017 demonstrates, that the enrichment of network of standard (professional) stations with new stations may significantly refine or even revise the current state of knowledge, for example in the case of urban heat island phenomena. A cluster analysis was applied in order to assess the impact of localization circumstances (station environment, exposition, etc.) as well as typological classification of the set of meteorological stations. [ABSTRACT FROM AUTHOR]

The air temperature trends measured at the central meteorological station Varaždin and the water temperature measured at the Botovo station on the Drava River were analyzed from 1 January 1969 to 31 December 2021. Analyses were performed for three different time scales: year, month, and day. Mann–Kendall testing statistically determined the significant trends over the analyzed period and found increasing air and water temperatures. From 1975 to 1989, three reservoirs of different volumes and water surface areas were built. The Botovo water measuring station is 11 km from the third largest reservoir and 28 km from the mouth. Applying the day-to-day (DTD) method, we determined that the variations in the daily air temperatures are significantly higher than the simultaneous variations in the daily water temperatures. Also, the rise in water temperatures at the downstream water measuring station Botovo was influenced by the construction of reservoirs. The commissioning of the second reservoir in 1982 caused a significant rise in water temperature at the Botovo station. Trends in water temperature increase during all months of the year were statistically significant, while air temperature trends were statistically significant during the warm parts of the year. [ABSTRACT FROM AUTHOR]

Surface air temperature (Ta) is an important physical quantity, usually measured at ground weather station networks. Measured Ta data is inadequate to characterize the complex spatial patterns of Ta field due to low density and unevenness of the networks. Remote sensing can provide satellite imagery with large scale spatial coverage and fine resolution. Estimating spatially continuous Ta by integrating ground measurements and satellite data is an active research area. A variety of methods have been proposed and applied in this area. However, the existing studies primarily focused on daily Ta and failed to quantify uncertainties in model parameter and estimated results. In this paper, a Bayesian Kriging regression (BKR) method is proposed to model and estimate monthly Ta using satellite-derived land surface temperature (LST) as the only input. The BKR is a spatial statistical model with the capacity to quantify uncertainties via Bayesian inference. The BKR method was applied to estimate monthly maximum air temperature (Tmax) and minimum air temperature (Tmin) over the conterminous United States in 2015. An exploratory analysis shows a strong relationship between LST and Ta at the monthly scale, indicating LST has the great potential to estimate monthly Ta. 10-fold cross-validation approach was adopted to compare the predictive performance of the BKR method with the linear regression method over the whole region and the urban areas of the contiguous United States. For the whole region, the results show that the BKR method achieves a competitively better performance with averaged RMSE values 1.23 K for Tmax and 1.20 K for Tmin, which are also lower than previous studies on estimation of monthly Ta. In the urban areas, the cross-validation demonstrates similar results with averaged RMSE values 1.21 K for Tmax and 1.27 K for Tmin. Posterior samples for model parameters and estimated Ta were obtained and used to analyze uncertainties in the model parameters and estimated Ta. The BKR method provides a promising way to estimate Ta with competitively predictive performance and to quantify model uncertainties at the same time. [ABSTRACT FROM AUTHOR]

Atmospheric drought is an extremely important issue on a global, regional and local scale, especially in the context of climate change. The aim of the study was to assess the spatiotemporal variation of atmospheric (meteorological) drought in agricultural areas of east-central Poland, represented by the Lublin Voivodeship (Lublin region) in 1971–2015. Average monthly air temperatures and monthly precipitation totals recorded over the 45-year period at 25 weather stations were used in the study. The assessment of spatiotemporal variation in atmospheric drought in the study area was based on calculations of the aridity index. The analysis showed an increase in the severity of atmospheric drought in the Lublin region, with intensification of this phenomenon in the last two decades, especially in the warmer half of the year (April, June–August). The main cause of drought in the Lublin region was identified as a statistically significant increase in air temperature (on average, from 0.4 °C to 0.7 °C/10 years in April, July and August, and from 0.2 °C to 0.5 °C/10 years in June) together with the absence of changes in precipitation in the warmer half of the year. This may be linked to some increase in the frequency of high-pressure circulation types, both non-directional and advection from the south. Due to the worsening problem of drought in Poland in recent years, especially in agriculture, there is a need for further research on this subject and for solutions aimed at optimizing agricultural use of the productive environment. [ABSTRACT FROM AUTHOR]

This study was designed to better understand vegetation's impact on air maximum (Tmax), minimum (Tmin), and daily temperature range (DTR), as well as seasonality and variability. We selected a flat, under synoptic-scale, northern Serbian region with an operational network of automated weather stations (AWS) for the study. Data were collected directly from the eighteen AWSs placed in the orchard canopy during 2013–2018. Meteorological data, plant phenological data in the form of the BBCH scale, and orchards' soil characteristics data were collected. Environmental factors influencing the temperature were classified as static (slow or unchangeable) and dynamic (fast-changing). The impact of both factors on maximum, minimum, and daily temperature range and its variability were analyzed. Results show that static factors (like soil texture) affect the annual variation of Tmax, Tmin, and DTR rather than its variability over the season. The dynamic factors, mainly coming from the plant's phenology, substantially affected the seasonal variability of these variables. Studies like this suffer from missing data and sparse spatial coverage by the AWS network. Therefore, the alternatives of orchard micrometeorological data, nearest climatological station, and ERA5-Land reanalysis data are tested. Both data sets showcased limitations in their applicability, while reanalysis data deviated more from the in-situ measurements, both seasonally and regionally. [ABSTRACT FROM AUTHOR]

In this study, an improved method named spatial disaggregation and detrended bias correction (SDDBC) based on spatial disaggregation and bias correction (SDBC) combined with trend correction was proposed. Using data from meteorological stations over China from 1991 to 2020 and the seasonal hindcast data from the Beijing Climate Center Climate System Model (BCC_CSM1.1 (m)), the performances of the model, SDBC, and SDDBC in spring temperature forecasts were evaluated. The results showed that the observed spring temperature exhibits a significant increasing trend in most of China, but the warming trend simulated by the model was obviously smaller. SDBC performed poorly in temperature trend correction. With SDDBC, the model's deviation in temperature trend was corrected, and consequently, the temporal correlation between the model's simulation and the observation as well as the forecasting skill on the phase of temperature were improved, thus improving the MSSS and the ACC. From the perspective of probabilistic prediction, the relative operating characteristic skill score (ROCSS) and the Brier skill score (BSS) of the SDDBC for three categorical forecasts were higher than those of the model and SDBC. The SDDBC's BSS increased as the effect of the increasing resolution component was greater than that of the decreasing reliability component. Therefore, it is necessary to correct the predicted temperature trend in post-processing for the output of numerical prediction models. [ABSTRACT FROM AUTHOR]

Myanmar is located in a tropical region where temperature rises very fast and hence is highly vulnerable to climate change. The high variability of the air temperature poses potential risks to the local community. Thus, the current study uses 42 synoptic meteorological stations to assess the spatiotemporal changes in air temperature over Myanmar during 1971–2013. The nonparametric sequential Mann-Kendall (SqMK), linear regression, empirical orthogonal function (EOF), Principal Component Analysis (PCA), and composite analysis were used to assess the long-term trends in maximum (Tmax) and minimum (Tmin) temperature series and their possible mechanism over the study region. The results indicate that the trend of Tmax has significantly increased at the rates of 90% in summer season, while the Tmin revealed a substantial positive trend in winter season time series with the magnitude of 30%, respectively. Moreover, during a rapid change of climate (1995–2013) we observed an air temperature increase of 0.7 °C. The spatial distributions of EOF revealed relatively warmer temperatures over the whole region except the south in the summer; however, a similar pattern can be seen for the rainy season and winter, implying warming in the central part and cooling in the northern and southern parts. Furthermore, the Indian Ocean Dipole (IOD) influence on air temperature over Myanmar is more prevalent than that of the El Niño Southern Oscillation (ENSO). The result implies that the positive phase of the IOD and negative phase of the Southern Oscillation Index (SOI; El Niño) events led to the higher temperature, resulting in intense climatic extremes (i.e., droughts and heatwaves) over the target region. Therefore, this study's findings can help policymakers and decision-makers improve economic growth, agricultural production, ecology, water resource management, and preserving the natural habitat in the target region. [ABSTRACT FROM AUTHOR]

Air temperature and precipitation are two important meteorological factors affecting the earth's energy exchange and hydrological process. High quality temperature and precipitation forcing datasets are of great significance to agro-meteorology and disaster monitoring. In this study, the accuracy of air temperature and precipitation of the fifth generation of atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) and High-Resolution China Meteorological Administration Land Data Assimilation System (HRCLDAS) datasets are compared and evaluated from multiple spatial–temporal perspectives based on the ground meteorological station observations over major land areas of China in 2018. Concurrently, the applicability to the monitoring of high temperatures and rainstorms is also distinguished. The results show that (1) although both forcing datasets can capture the broad features of spatial distribution and seasonal variation in air temperature and precipitation, HRCLDAS shows more detailed features, especially in areas with complex underlying surfaces; (2) compared with the ground observations, it can be found that the air temperature and precipitation of HRCLDAS perform better than ERA5. The root-mean-square error (RMSE) of mean air temperature are 1.3 °C for HRCLDAS and 2.3 °C for ERA5, and the RMSE of precipitation are 2.4 mm for HRCLDAS and 5.4 mm for ERA5; (3) in the monitoring of important weather processes, the two forcing datasets can well reproduce the high temperature, rainstorm and heavy rainstorm events from June to August in 2018. HRCLDAS is more accurate in the area and magnitude of high temperature and rainstorm due to its high spatial and temporal resolution. The evaluation results can help researchers to understand the superiority and drawbacks of these two forcing datasets and select datasets reasonably in the study of climate change, agro-meteorological modeling, extreme weather research, hydrological processes and sustainable development. [ABSTRACT FROM AUTHOR]

The Intergovernmental Panel on Climate Change regular scientific assessments of global warming is based on measurements of air temperature from weather stations, buoys or ships. More specifically, air temperature annual means are estimated from their integration into climate models, with some areas (Africa, Antarctica, seas) being clearly underrepresented. Present satellites allow estimation of surface temperature for a full coverage of our planet with a sub-daily revisit frequency and kilometric resolution. In this work, a simple methodology is developed that allows estimating the surface temperature of Planet Earth with MODIS Terra and Aqua land and sea surface temperature products, as if the whole planet was reduced to a single pixel. The results, through a completely independent methodology, corroborate the temperature anomalies retrieved from climate models and show a linear warming trend of 0.018 ± 0.007 °C/yr. [ABSTRACT FROM AUTHOR]

Climate change poses potential challenges to sensitive areas, such as high-elevation regions. The Yarlung Zangbo River (YLZR) basin is located in the southeast of the Qinghai-Tibetan Plateau. It contains large amounts of snow and numerous glaciers that are vulnerable to climate change. Based on daily observational data at 17 meteorological stations in and around the YLZR basin during 1957–2015, the variability of precipitation, air temperature, and streamflow were analyzed. The nonparametric Mann–Kendall test, Sen's slope estimate method, cross wavelet transform (XWT), and wavelet coherence (WTC) were used to identify the annual seasonal trends. the abrupt changes of precipitation and air temperature, and their associations with large-scale circulation. The results showed that the YLZR basin experienced an overall rapid warming and wetting during the study period, with an average warming rate of 0.33 °C/10 a and wetting rate of 4.25 mm/10a, respectively. Abrupt change points in precipitation and air temperature occurred around the 1970s and 1990s, respectively. The abrupt change points of three hydrological stations occurred around the late 1960s and the late 1990s, respectively. The precipitation, annual average temperature, and the streamflow of the three hydrological stations were negatively correlated with the Pacific decadal oscillation (PDO) and the multivariate El Niño-Southern Oscillation (ENSO) index (MEI), reaching a significant level of 0.05. [ABSTRACT FROM AUTHOR]