scholarly journals Spatiotemporal variability of air temperatures in Central Serbia from 1949 to 2018

Időjárás ◽  
2021 ◽  
Vol 125 (2) ◽  
pp. 229-253
Author(s):  
Nikola R. Bačević ◽  
Nikola M. Milentijević ◽  
Aleksandar Valjarević ◽  
Ajša Gicić ◽  
Dušan Kićović ◽  
...  
Keyword(s):  
Time Series ◽  
Spatial Distribution ◽  
Air Temperature ◽  
Minimum Time ◽  
Spatiotemporal Variability ◽  
Air Temperatures ◽  
Gis Tools ◽  
Average Maximum ◽  
Annual Time Series ◽  
The Republic

The paper presents trends for three categories of variables: average annual, average maximum and average minimum air temperatures. Data was provided by the meteorological yearbooks of the Republic Hydrometeorological Service of Serbia. The main goal of this paper is to detect possible temperature trends in Central Serbia. The trend equation, trend magnitude, and Mann-Kendall non-parametric test were used in the analysis of climate parameters. The used statistical methods were supplemented by GIS numerical analysis, which aimed to analyze the spatial distribution of isotherms from 1949 to 2018. The obtained results indicate that out of the 72 analyzed time series, an increase in air temperature is dominant in 61 time series, while 11 time series show no changes. The highest increase was recorded in the average maximum time series (4.2 °C), followed by an increase of 3.5°C in average maximum air temperatures. The highest increase in the average annual time-series was 3.0 °C. The lowest increases in air temperature were recorded in the average minimum time series (0.1 and 0.2 °C). In two average minimum time series a decrease in average air temperatures was identified (-0.6 and -0.4 °C. The application of GIS tools indicates the existence of interregional differences in the arrangement of isotherms, leaded by the orography of the terrain. In the spatial distribution of the analyzed variables, "poles of heat" and "poles of cold" stand out, and the influence of the urban heat island is evident (especially in the case of the urban agglomeration of Belgrade). The manifested spatial patterns of air temperature need to be further examined and the correlation with possible causes need to be determined. For these reasons, the paper provides a solid basis for studying the climate of this area in the future, as it provides insight into climate dynamics over the past decades.

CORRELATION COMMUNICATION BETWEEN METEOROLOGICAL PARAMETERS AT EXTREME VALUES OF MAXIMUM AIR TEMPERATURES

2020 ◽  
pp. 101-112
Author(s):  
S.V. Savchuk ◽  
V.E. Timofeev ◽  
O.A. Shcheglov ◽  
V.A. Artemenko ◽  
I.L. Kozlenko
Keyword(s):  
Air Temperature ◽  
Meteorological Parameters ◽  
Extreme Values ◽  
Daily Temperature ◽  
Sea Level Pressure ◽  
Maximum Daily Temperature ◽  
The North ◽  
Air Temperatures ◽  
Average Maximum ◽  
Maximum Air Temperature

The object of the study is the maximum daily air temperature during the months of the year over 1991-2016 by the data of 186 meteorological stations of Ukraine. Extreme values of the maximum daily temperature equal to or exceeded their 95th (Tmax95p and above, ºС) percentile were taken as extreme. The article sets the dates (137 cases) of extreme values of maximum air temperature on more than 60 % of the territory. For these dates, 13 meteorological parameters were selected: average, minimum, and maximum air temperatures; average, minimum and maximum relative humidity; station and sea-level pressure; average, maximum (from 8 synoptic hours) wind speed; rainfall; height of snow cover. The purpose of this work is to determine the correlation coefficient (K), in particular, statistically significant (K≤-0.6, K≥0.6), on these dates between selected meteorological parameters at 186 meteorological stations of Ukraine for 1991-2013. The density of the cases of statistically significant dependence between the meteorological parameters in extremely warm days in separate seasons is determined. In extremely warm days, meteorological parameters and areas with statistically significant correlations at K≤-0.6 were detected: T and F (focally in southern and some western regions with significant density) − in winter; T and F (with the highest density ubiquitous or almost ubiquitous), P and V (in a large number of regions, usually west or right-bank, but with less frequency) − in the transition seasons, and in the autumn between − T and F (in the south with smaller density) and P and F (in some areas of the north, northwest, west, lower east). In all seasons, such a correlation between other meteorological parameters had a focal distribution, usually with a smaller density. In these days, a focal distribution with a small frequency of dependencies at K≥0.6 was found between the meteorological parameters detected (F and V in transition seasons, T and F in winter), except for similar ones. However, such dependence is observed between T and V in some regions in winter and autumn and in some areas of south, southeast, east with a smaller density. The study of the maximum daily temperature is relevant, because from the level of natural hydrometeorological phenomena it is accompanied by dangerous phenomena, negatively affecting the weather dependent industries.

scholarly journals Trends of Changes of Maximum Air Temperature in Ukraine as an Impact Factor on Population Health

2020 ◽  
Keyword(s):  
Population Health ◽  
Air Temperature ◽  
Thermal Regime ◽  
Climate Changes ◽  
System Analysis ◽  
Global Climate ◽  
Maximum Temperature ◽  
Air Temperatures ◽  
Average Maximum ◽  
Maximum Air Temperature

Purpose. The aim of this research is detection of trends of changes (according to fact and scenario data) of extreme air temperature as a component of thermal regime in different regions of Ukraine because of global climate change. Methods. System analysis, statistical methods. Results. Time distribution of maximum air temperature regime characteristics based on results of observations on the stations located in different regions of Ukraine during certain available periods: Uzhgorod (1946-2018), Kharkiv (1936-2005), Оdessа (1894-2005), аnd also according to scenarios of low (RCP2.6), medium (RCP4.5) and high (RCP8.5) levels of greenhouse gases emissions. Meanwhile, air temperature ≥ 25°С was considered high (days with maximum temperature within 25,0-29,9°С are hot), ≥ 30°С was considered very high (days with such temperature are abnormaly hot). Trends of changes of extreme air temperatures were identified as a component of thermal regime in different regions of Ukraine within global climate changes. Dynamics of maximum air temperature and its characteristics in ХХ and beginning of ХХІ centuries were researched. Expected time changes of maximum air temperature and number of days with high temperature during 2021-2050 were analyzed by RCP2.6, RCP4.5 and RCP8.5 scenarios. There were identified the highest day air temperatures possible once in a century and also possibility of maximum day temperature more than 30°С by RCP4.5 scenario. Well-timed prediction of climate changes will help evaluate their impact on human and natural systems which will be useful for development and taking preventive measures towards minimization of negative influence of such changes. Conclusions. Processes of climate warming in Ukraine are activating. There was determined a strong trend on increasing of average maximum of air temperature in winter with speed 0.17-0,39 degrees centigrade/10 years. According to climatic norm this index mainly increased mostly (up to 3,3 degrees centigrade) in January in North-East of the country. In future such anomalies will grow. Determination of correlation between climate and health is the base for taking protective measures against perils for population health connected with climate.

scholarly journals Analyses of the Zagreb Grič observatory air temperatures indices for the period 1881 to 2017

2018 ◽  
pp. 67-85 ◽  
Author(s):  
Ognjen Bonacci ◽  
Tanja Roje Bonacci
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Partial Sums ◽  
Daily Maximum ◽  
Absolute Maximum ◽  
Absolute Minimum ◽  
Air Temperatures ◽  
Temperature Regimes ◽  
Urban Heat ◽  
Maximum Temperatures

The paper studies time series of characteristic (minimum, mean, and maximum) daily, monthly, and yearly air temperatures measured at the Zagreb Grič Observatory in the period from 1 Jan. 1881 to 31 Dec. 2017. The following five air temperatures indices (ATI) are analysed: (1) absolute minimum yearly, monthly, and daily; (2) mean yearly, monthly, and daily minimum; (3) average mean yearly, monthly, and daily; (4) mean yearly, monthly, and daily maximum; (5) absolute maximum yearly, monthly, and daily. Methods of Rescaled Adjusted Partial Sums (RAPS), regression and correlation analyses, F-tests, and t-tests are used in order to describe changes in air temperature regimes over 137 years. Using the RAPS method the five analysed yearly ATI time series durations of 137 years were divided into two sub-periods. The analyses made in this paper showed that warming of minimum air temperatures started in 1970, mean air temperatures in 1988, and maximum air temperatures in 1998. Results of t-tests show an extreme statistically significant jump in the average air-temperature values in the second (recent time) sub-periods. Results of the t-tests of monthly temperatures show statistically significant differences between practically all five pairs (except in two cases) of analysed monthly ATI subseries for the period from January to August. From September to December the differences for most of pairs (except in six cases) of the analysed monthly ATI subseries are not statistically significant. It can be concluded that the urban heat island influenced the increase in recent temperatures more strongly than global warming. It seems that urbanisation firstly and chiefly influenced the minimum temperatures, as well as that Zagreb’s urbanisation had a bigger impact on minimum temperatures than on maximums. Increasing trend in time series of maximum temperatures started 20 years later.

scholarly journals Identifying the influence of dams and ponds on the thermal regime at regional scale: The case of Loire catchment

2020 ◽  
Author(s):  
Hanieh Seyedhashemi ◽  
Florentina Moatar ◽  
Jean-Philippe Vidal ◽  
Aurélien Beaufort ◽  
André Chandesris ◽  
...  
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Temperature Regime ◽  
Thermal Regime ◽  
Regional Scale ◽  
Stream Temperature ◽  
Annual Peak ◽  
Air Temperatures ◽  
Catchment Areas ◽  
Anthropogenic Structures

<p>Human activities and natural processes are the main drivers of the spatio-temporal variability of thermal regime. Despite a few local studies on the thermal regime variability, regional assessments are scarce in the scientific literature. However, regional assessments allow tracing systematic human-induced changes emerging from some types of anthropogenic structures like dams or ponds and identifying the locations of highly influenced reaches.</p><p>In the current study, we propose a framework to detect the influence of dams and ponds on stream temperature. We use observational data from 526 evenly distributed hourly stream temperature stations in the Loire River catchment, France (110,000 km<sup>2</sup>). The data consist of unbalanced time series of natural and altered thermal regimes that contain at least 80 summer days from 2000–2018. By comparing time series of observed stream temperature and air temperature, we define five indicators to distinguish different patterns of thermal regime. Three of them are based on weekly stream-air temperature linear regressions (slope; intercept; and coefficient of determination). The remaining two indicators compare monthly air and stream temperature regime: 1) the proportion of times stream temperature is greater than air temperature from March–October (“frequency”), and 2) the lag time between the annual peak in air temperature and annual peak in stream temperature (“shift”).</p><p>K-means clustering partitioned stations into three clusters: 1) pond-like, 2) dam-like 3) and natural, with 164, 37, and 316 stations, respectively. Supporting this cluster analysis, 93% of stations in pond-like cluster have upstream ponds, and 55% of stations in dam-like cluster have upstream large dams. Pond-like stations have the greatest slope between weekly stream and air temperatures (slope = 0.4) and have stream temperatures greater than air temperatures more frequently (68%) than other clusters. In contrast, dam-like stations have the lowest correlations between weekly stream and air temperatures (mean R<sup>2</sup>=0.3, compared to 0.7 for the other two clusters). Dam-like stations also exhibit the largest shifts in stream thermal regime relative to air temperature (mean shift = 30 days). Impounded runoff index (IRI), the ratio of reservoir volume to annual discharge, best explaines variability within the dam-like cluster. For pond-like stations, catchment areas and mean upstream ponded surface area best explain the within-cluster variability, particularly for the frequency indicator, although this relationship is sensitive to interannual air temperature regime.</p><p>These findings support modelers in quantifying the downstream impacts of different types of anthropogenic structures and managers in surveying and monitoring stream networks through identification of critical reaches.</p>

Modeling of air temperatures using a combination of TBATS and SVM models for various climatic locations in Europe

2020 ◽  
Author(s):  
Magdalena Gos ◽  
Piotr Baranowski ◽  
Jaromir Krzyszczak ◽  
Małgorzata Murat ◽  
Iwona Malinowska
Keyword(s):  
Time Series ◽  
State Space ◽  
Air Temperature ◽  
Weather Conditions ◽  
Exponential Smoothing ◽  
Climatic Conditions ◽  
State Space Models ◽  
Temperate Climate ◽  
Support Vector ◽  
Air Temperatures

<p>By modelling and forecasting  of meteorological  time  series it is possible to  improve   understanding  of  the  weather dynamics and fluctuations as a result of climate change . The most frequently used forecasting models are exponential smoothing, ARIMA models (Box and Jenkins, 1970), state-space models (Harvey, 1989) and innovations State Space Models (Hyndman et al., 2008).</p><p>The aim of this study was to check the effectiveness of the coupled TBATS and Support Vector Machines (SVM) model, supplied with some measured meteorological quantities to forecast air temperature for six years for four climatic localizations in Europe. The study was calculated from northern (Jokioinen in Finland), central (Dikopshof located in the west part of Germany and Nossen in the south part of Germany) and southern (Lleida in Spain) Europe to present different climatic conditions. Jokioinen city has a subarctic climate that has severe winters, with cool and short summers and strong seasonality. Lleida has a semi-arid climate with Mediterranean. Dikopshof represents maritime temperate climate. There are significant precipitation throughout the year in Dikopshof and Nossen. In the study we study on air temperature dataset collected on a daily basis from January 1st 1980 to December 31st 2010 (11322 days).</p><p>For all the studied sites coupled TBATS/SVM models occurred to be effective in predicting air temperature courses, giving an improved precision (up to 25%) in forecasting of the seasonality and local temperature variations, compared to pure SVM or TBATS modelling. The precision of prediction of the maximum and minimum air temperatures strongly depended on the dynamics of the weather conditions, and varied for different climatic zones.</p><p>This study has been partly financed from the funds of the Polish National Centre for Research and Development in frame of the project: MSINiN, contract number: BIOSTRATEG3/343547/8/NCBR/2017.</p><p> </p><p>Reference to a journal publication:</p><p>BOX, G.E.P. – Jenkins, G. 1970. Time Series Analysis: forecasting and control. Holden-Day, p. 20-31.</p><p>HARVEY A. 1989. Forecasting Structural Time Series Model and the Kalman Filter. New York, Cambridge University press., p. 32-41.</p><p>HYNDMAN, R.J. – KOEHLER, A.B. – ORD, J.K. – SNYDER, R.D. 2008. Forecasting with Exponential Smoothing: The State Space Approach. Springer-Verlag, p. 50-62.</p>

scholarly journals Detection of Changes in Hydrological Time Series During Recent Decades

2020 ◽  
Vol 28 (2) ◽  
pp. 56-62
Author(s):  
Mária Ďurigová ◽  
Kamila Hlavčová ◽  
Jana Poórová
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Time Series Data ◽  
Kendall Test ◽  
Change Points ◽  
Series Data ◽  
Data Series ◽  
Time Data ◽  
Air Temperatures ◽  
Pettitt’S Test

AbstractAn analysis of a hydrological time-series data offers the possibility of detecting changes that have arisen due to climate change or change in land use. This paper deals with the detection of changes in the hydrological time data series. The trend analysis was applied at 58 stage-discharge gauging stations that are located throughout Slovakia, with the measurement period from 1962 to 2017. The Mann-Kendall test show a declining trends in the summer and a few rising trends in the winter in discharges. In the town of Banská Bystrica at a station on the Hron River, decades of discharges, air temperatures, and precipitation totals were analyzed. The five decades from the 1960s to the 2000s were used. The hydrological time data series were also analyzed by the Pettitt’s test, which is used to detect change points. The decadal analysis at the Banská Bystrica station shows an increase in the air temperature but insignificant changes in discharges and precipitation. Pettitt’s test identified many change points in the 1990s in the air temperature.

scholarly journals Air temperature variability and trends over Greece

2013 ◽  
Vol 10 (2) ◽  
pp. 273-285
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Northern Region ◽  
Climatic Data ◽  
The South ◽  
Annual Time Series ◽  
Cooling Trend ◽  
The Mean ◽  
Temperature Levels ◽  
Summer Time

In this study, the variability and trends of the mean annual and seasonal surface air temperature in Greek peninsula are examined. The climatic data used, concern mean monthly values of air temperature of 20 meteorological stations of the Hellenic Meteorological Service, for the period 1951-2000. The air temperature time series for each station are analyzed, so that the variability and trends be described. Regarding the annual time series, a cooling trend is observed since the early 1960’s till the middle of the decade of 1970, when the trend reverses to heating till nowadays. It is remarkable that the today air temperature levels do not exceed the levels of the middle of the century. During the winter, it is crystal clear that a cooling trend exists from the middle of the decade of 1950 to the end of the decade of 1980, especially in the south region of the country. Afterwards an increasing trend is obvious till nowadays. The pattern in spring appears a slight heating trend in the northern region of Greece and a cooling trend in the south. The summer time series are similar to the annual ones, so the contribution of summer to annual variability is unquestionable. Generally speaking, the air temperature in autumn follows the same distribution in time. In the process, the Factor Analysis is applied on the mean annual and seasonal air temperature and thereafter the regions, within the air temperature covariates, are defined. Finally, the application of spectral analysis to annual and seasonal air temperature is regarded necessary so that the periodicities are derived.

scholarly journals Methodology of determination of extreme air temperatures thresholds in eastern Serbia (Negotin town): In January and July

2006 ◽  
Vol 86 (1) ◽  
pp. 61-72 ◽  
Author(s):  
Goran Andjelkovic
Keyword(s):  
Spatial Distribution ◽  
Air Temperature ◽  
Human Activities ◽  
Human Life ◽  
Climatic Conditions ◽  
Air Temperatures ◽  
The World ◽  
Climatic Phenomenon

The human life and the most of human activities are adapted to the normal climatic conditions everywhere in the world. Therefore any deviation in intensity, frequency or spatial distribution of climatic events is considered as extraordinary climatic phenomenon which could cause unpleasant consequences. These events are known as extreme phenomenon. In this paper extreme air temperature events are formulated, and methodology for identification is considered on the example of Negotin town in eastern part of Serbia.

scholarly journals Air temperature in Southern Roztochia

2015 ◽  
pp. 239-245
Author(s):  
Bohdan Mucha ◽  
Iryna Bulavenko ◽  
Oksana Rodych
Keyword(s):  
Air Temperature ◽  
Meteorological Data ◽  
Warming Trend ◽  
Annual Average ◽  
Regional Warming ◽  
Average Temperature ◽  
Air Temperatures ◽  
Average Maximum ◽  
National University

The demonstration and analysis of the monthly and annual average air temperatures in Southern Roztochia for last 46 years are proposed. The meteorological data of the Roztochia landscapegeophysical station (RLGS) of Ivan Franko National University of Lviv have served as the starting material for this publication. The long-term value of the average air temperature in RLGS has been defined. The average temperature warming by 2 °C has occurred from 1970 to 2000 and the amplitude of fluctuations of average temperatures has increased since 2000. The fact of a gradual warming trend in the region Roztochia and the adjacent Small Polissia was confirmed as an attribute of the consequences of global warming and drainage reclamation during the XX century. The graphs for annual average, maximum and minimum air temperatures for last 46 years were concluded for the duration of 5 years at the seasons. The coldest period of research is the years 1969–1989 and the warmest ones are the years since 2000 and especially 2015. The parameters of extreme warming in 2015 were fixed in agriculture and water management. We are warning about the possibility of aridization of the territory as a result of the trend of warming. The ways of preventing of regional warming due to reducing the activity of drainage reclamation systems, conservation of forest and meadow vegetation are suggested. Key words: average air temperature, regional warming, extreme air temperature, Southern Roztochia.

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