Differences between true mean daily, monthly and annual air temperatures and air temperatures calculated with three equations: a case study from three Croatian stations

Differences between true mean daily, monthly and annual air temperatures T0 [Eq. (1)] and temperatures calculated with three different equations [(2), (3) and (4)] (commonly used in climatological practice) were investigated at three main meteorological Croatian stations from 1 January 1999 to 31 December 2011. The stations are situated in the following three climatically distinct areas: (1) Zagreb-Gri? (mild continental climate), (2) Zavi?an (cold mountain climate), and (3) Dubrovnik (hot Mediterranean climate). T1 [Eq. (2)] and T3 [Eq. (4)] mean temperatures are defined by the algorithms based on the weighted means of temperatures measured at irregularly spaced, yet fixed hours. T2 [Eq. (3)] is the mean temperature defined as the average of daily maximum and minimum temperature. The equation as well as the time of observations used introduces a bias into mean temperatures. The largest differences occur for mean daily temperatures. The calculated daily difference value from all three equations and all analysed stations varies from ?3.73 °C to +3.56 °C, from ?1.39 °C to +0.79 °C for monthly differences and from ?0.76 °C to +0.30 °C for annual differences.     

Modeling monthly mean air temperature for Brazil

Air temperature is one of the main weather variables influencing agriculture around the world. Its availability, however, is a concern, mainly in Brazil where the weather stations are more concentrated on the coastal regions of the country. Therefore, the present study had as an objective to develop models for estimating monthly and annual mean air temperature for the Brazilian territory using multiple regression and geographic information system techniques. Temperature data from 2,400 stations distributed across the Brazilian territory were used, 1,800 to develop the equations and 600 for validating them, as well as their geographical coordinates and altitude as independent variables for the models. A total of 39 models were developed, relating the dependent variables maximum, mean, and minimum air temperatures (monthly and annual) to the independent variables latitude, longitude, altitude, and their combinations. All regression models were statistically significant (α?≤?0.01). The monthly and annual temperature models presented determination coefficients between 0.54 and 0.96. We obtained an overall spatial correlation higher than 0.9 between the models proposed and the 16 major models already published for some Brazilian regions, considering a total of 3.67?×?10⁸?pixels evaluated. Our national temperature models are recommended to predict air temperature in all Brazilian territories.     

Hourly air temperature driven using multi-layer perceptron and radial basis function networks in arid and semi-arid regions

This study employed two artificial neural network (ANN) models, including multi-layer perceptron (MLP) and radial basis function (RBF), as data-driven methods of hourly air temperature at three meteorological stations in Fars province, Iran. MLP was optimized using the Levenberg–Marquardt (MLP_LM) training algorithm with a tangent sigmoid transfer function. Both time series (TS) and randomized (RZ) data were used for training and testing of ANNs. Daily maximum and minimum air temperatures (MM) and antecedent daily maximum and minimum air temperatures (AMM) constituted the input for ANNs. The ANN models were evaluated using the root mean square error (RMSE), the coefficient of determination (R ²) and the mean absolute error. The use of AMM led to a more accurate estimation of hourly temperature compared with the use of MM. The MLP-ANN seemed to have a higher estimation efficiency than the RBF ANN. Furthermore, the ANN testing using randomized data showed more accurate estimation. The RMSE values for MLP with RZ data using daily maximum and minimum air temperatures for testing phase were equal to 1.2°C, 1.8°C, and 1.7°C, respectively, at Arsanjan, Bajgah, and Kooshkak stations. The results of this study showed that hourly air temperature driven using ANNs (proposed models) had less error than the empirical equation.     

基于GIS气象要素空间插值方法在巴彦淖尔市冬季公路的养护应用

针对内蒙古自治区巴彦淖尔市气象站点分布情况,以2000—2009年9个气象站点(临河,杭锦后旗,五原,磴口,乌拉特前、中、后三旗,海力素,大佘太)的年平均降水和温度为基础数据,通过GIS空间分析中的空间数据插值方法,分别建立平均降水和温度与海拔、经纬度之间的回归方程;在此基础上,建立模拟站点来增加气象数据的信息量。分别采用反距离加权法、径向基函数法、趋势面分析和普通克里格法几种插值方法进行比较插值分析。误差分析表明没有绝对最优的空间插值方法,只有在特定的条件下的最有效方法,对于降水量插值来说三次样条小波插值法是最好的;比较分析的结果表明,模拟站点的加入大大提高了插值的精度,普通克里格法比反距离加权法和径向基函数法具有更为理想的插值效果;并据此理论分析对空间插值在冬季高速公路养护方面的应用问题进行阐述。     

Assessment of global warming on the island of Tenerife, Canary Islands (Spain). Trends in minimum, maximum and mean temperatures since 1944

Temperature variation is studied at different altitudes and orientation on the island of Tenerife, according to the trends in the mean, maximum and minimum at 21 meteorological stations. Reference series are obtained by sectors, along with a representative overall series for Tenerife, in which temperature shows a statistically significant growth trend of 0.09?±?0.04°C/decade since 1944. Night-time temperatures have risen most (0.17°C?±?0.04°C/decade), while by day they have been more stable. Consequently, the diurnal temperature range between day and night has narrowed. By regions, warming has been much more intense in the high mountains than the other sectors below the inversion layer between 600 and 1,400?m altitude, and progressively milder towards the coast. The temperature rise on the windward (north-northeast) slopes is greater than on the leeward side and could be related to the increase in cloudiness on the northern side. The general warming of the island is less than in continental areas at between 24 and 44oN, being closer to the sea surface temperature in the same area. This is probably explained largely by the insular conditions. In fact warming is more evident in the high mountains (0.14?±?0.07°C/decade), where the tempering effect of the ocean and the impact of changes in the stratocumulus is weaker, being similar to the mean continental values in the northern hemisphere.     

库尔勒气象站迁站前后气象要素特征及成因分析

利用2016-2018年库尔勒气象站迁站前后基本气象要素的观测资料进行对比分析,结果显示：(1)平均气温、平均最低气温年、月值均是新站低于旧站,年值分别低2.1℃和4.1℃,年平均最高气温持平；春季气温差值变化相对较小,夏、秋、冬季气温差值变化相对偏大。(2)各月相对湿度新站大于旧站,各季相对湿度差值夏季最大,年平均相对湿度新站比旧站高11%。(3)平均气压新站高于旧站,年平均气压差值为3.2pha。各季差值冬季最大,(4)平均风速新站比旧站偏大0.1m/s,春季、夏季风速大于其他季节；最大风速新站比旧站偏大1.3-6.2m/s；主导风向由ENE转为E。(5)年平均气温、最低气温、平均湿度和年平均气压,迁站前后资料有显著差异,年平均最高气温、平均风速无显著差异。(6)测站周围环境、海拔高度、下垫面、地形等因素是造成新旧站气象要素差异的主要原因。     

Detecting climate change signals in Saudi Arabia using mean annual surface air temperatures

Climate change signals in Saudi Arabia are investigated using the surface air temperature (SAT) data of 19 meteorological stations, well distributed across the country. Analyses are performed using cumulative sum, cumulative annual mean, and the Mann–Kendall rank statistical test for the period of 1978–2010. A notable change in SAT for the majority of stations is found around 1997. The results show a negative temperature trend (cooling) for all stations during the first period (1978–1997), followed by a positive trend (warming) in the second period (1998–2010) with reference to the entire period of analysis. The Mann–Kendall test confirms that there is no abrupt cooling at any station during the analysis period, reflecting the warming trend across the country. The warming trend is found to be 0.06 °C/year, while the cooling trend is 0.03 °C/year, which are statistically significant.     

Air temperatures in a power line corridor over 10?years following afforestation with Eucalyptus globulus

Over the 10?years following the planting of Eucalyptus globulus on an area of 320?ha in 1998, air temperature was measured in the middle of a power line corridor and compared with temperatures recorded at meteorological stations located 11.2 and 35.7?km away. At these reference stations, the annual means of both daily minimum and daily maximum temperature remained approximately constant. In the power line corridor, annual mean daily minimum temperature also remained approximately constant, but annual mean daily maximum temperature fell by about 3.6°C; in particular, between years 4 and 7 the cooling rate was on average about 0.7°C per year. The resulting sigmoid time course of annual mean daily temperature range may partly reflect the evolution of evapotranspiration from the plantation, but seems to be mainly attributable to the shading of the power line corridor by the growing trees, being closely correlated with solar irradiance at the center of the corridor as estimated from tree growth data using sun charts and standard formulae for clear-sky solar irradiance.     

Local indications of climate changes in Turkey: Bursa as a case example

This study aims to put out on what ratio Bursa province, one of the important heavy industry regions of Turkey, has been affected climatic process called “Global Warming” or “Climate Change”. For this intend climatic measurement results from Bursa center, top of Uludağ Mount, Yenişehir and Keles meteorological stations were used. These measurements were taken as minimum temperature at night-time, maximum temperature at day-time, and mean temperature, mean pressure, insolation intensity, insolation duration, mean wind speed, minimum temperature above soil, soil temperatures at depths of 5, 10, and 20 cm rainfall. Overall, our statistical results showed that there was a considerable warming at statistically 1% and 5% levels in summer months, particularly in July Almost all performed measurements confirm this result. According to climatic data for thirty years (1975–2005), in the last twelve years contrary to previous 18 years, mean temperature values were higher than long-term mean value nine times (years) repetitively. Temperatures did not deviated higher than 0.5°C in six of these. At the temperatures below mean, The maximum deviation was −0.4°C.     

空间回归检验方法在气象资料质量检验中的应用

该文详细介绍了空间回归检验方法, 并使用2003年我国671站的逐日平均气温、最高气温、最低气温、平均水汽压、平均风速、平均0 cm地温、降水量资料, 检验该方法在气象资料质量检验中的适用性。按区号将全国划分为10个区, 利用该方法分别对各区7个要素进行了检验试验。结果表明:空间回归检验方法能够有效检验出可疑数据, 适用于对单一要素的检验; 对降水、风速等空间变化比较大的要素, 该方法有比较好的检验效果; 应用该方法计算时, 在不同地区、不同要素之间存在差异; 当固定出错比率时, 各区应该选择不同的f值。与一般空间检验方法相同, 该方法也与地理环境、周边台站分布有关, 并受台站密度的影响。     

大连月平均气温短序列订正方法

针对大连地区气象台站少,研究区域气候比较困难的实际,以大连地区7个气象台站作为基本站,以各自所辖的气象哨作为订正站.探讨适合大连地区月平均气温的短序列订正的简便实用方法.构建了12个气象哨与各自基本站之间月平均气温序列订正的一元线性回归方程和差值订正方程,并从统计学和订正误差的角度进行了比较分析.结果表明:两种方法的订正误差无显著性差异,均可以用于大连地区月平均气温的短序列订正.     

Climate change in Bangladesh: a spatio-temporal analysis and simulation of recent temperature and rainfall data using GIS and time series analysis model

In this paper, temperature and rainfall data series were analysed from 34 meteorological stations distributed throughout Bangladesh over a 40-year period (1971 to 2010) in order to evaluate the magnitude of these changes statistically and spatially. Linear regression, coefficient of variation, inverse distance weighted interpolation techniques and geographical information systems were performed to analyse the trends, variability and spatial patterns of temperature and rainfall. Autoregressive integrated moving average time series model was used to simulate the temperature and rainfall data. The results confirm a particularly strong and recent climate change in Bangladesh with a 0.20 °C per decade upward trend of mean temperature. The highest upward trend in minimum temperature (range of 0.80–2.4 °C) was observed in the northern, northwestern, northeastern, central and central southern parts while greatest warming in the maximum temperature (range of 1.20–2.48 °C) was found in the southern, southeastern and northeastern parts during 1971–2010. An upward trend of annual rainfall (+7.13 mm per year) and downward pre-monsoon (?0.75 mm per year) and post-monsoon rainfall (?0.55 mm per year) trends were observed during this period. Rainfall was erratic in pre-monsoon season and even more so during the post-monsoon season (variability of 44.84 and 85.25 % per year, respectively). The mean forecasted temperature exhibited an increase of 0.018 °C per year in 2011–2020, and if this trend continues, this would lead to approximately 1.0 °C warmer temperatures in Bangladesh by 2020, compared to that of 1971. A greater rise is projected for the mean minimum (0.20 °C) than the mean maximum (0.16 °C) temperature. Annual rainfall is projected to decline 153 mm from 2011 to 2020, and a drying condition will persist in the northwestern, western and southwestern parts of the country during the pre- and post-monsoonal seasons.     

Spatial Heating Monthly Degree-Day Features and ClimatologicPatterns in Turkey

Summary  Degree-days as a measure of accumulated temperature deviations from a base temperature have many practical applications in various human related activities such as home cooling, heating, plant growth in agriculture and power generation in addition to energy requirement. Long temperature records are necessary for their reliable estimations at given stations. In this paper, degree-day measure has been applied to monthly temperature records for systematically changed base temperature values from − 25 °C to + 35 °C with 5 °C increments at 255 meteorology stations in Turkey. The results are represented in the form of spatial degree-day distribution maps, which are then related to various climatic, meteorological and topographic features of Turkey. For instance, free surface water bodies in forms of surrounding seas, lakes and rivers insert retardation in the expansion of heating degree-days over large regions. On the other hand, cold air penetration from polar regions in the northeastern Turkey originating from Siberia appears at moderate base temperature heating degree-days. Received August 20, 1998 Revised June 21, 1999     

河北省高速公路路面温度变化特征及预报模型

利用2010年1月至2011年12月邯长、京秦高速公路涉县、玉田南北2监测站和所在气象站观测资料,统计分析南北2站路面温度与气温的日变化特征及路面最高温度与气象因子的关系,基于多元回归分析方法建立逐月路面温度预报方程,并进行精度检验。结果表明,路面温度的日变化不但与季节、天空状况有关,还与地理位置密切相关。路面最高温度受多种气象因子的影响,与前一日路面最高温度、最高气温、能见度呈显著正相关,与总云量、低云量、相对湿度呈显著负相关,其中与最高气温的相关性最显著;路面最低温度与最低气温呈显著线性相关。基于路面最高温度预报方程的检验精度,玉田站年平均为77.5%,涉县为79.2%,可为今后路面最高温度预报提供参考。     

Bulgarian mountains air temperatures and precipitation—statistical downscaling of global climate models and some projections

Measured air temperature and precipitation data from three high mountainous Bulgarian stations were used along with data from 18 global climate models (GCMs). Air temperature and precipitation outputs of preindustrial control experiment were compared with actually observed values. GCM with the best overall performance is BCCR BCM 2.0 for air temperatures (period 1941?C2009) and CGCM 3.1/T47 for precipitation (period 1947?C2009). Statistical methods were used in this research??nonparametric Spearman correlation, Mann?CWhitney test, multiple linear regression, etc. Projections were made for the following future decades: 2015?C2024, 2045?C2054 and 2075?C2084. The best months, described by multiple linear regression (MLR) model of air temperatures, are November, January, March, and May. The worst described are summer months. There is not any pattern in the relationship between constructed MLR models and measured precipitation. Models that perform the best in different months at the three investigated stations are MIUB ECHO-G, GISS AOM, CGCM 3.1/T63, and CNRM CM3 for air temperatures and GFDL CM 2.1, GISS AOM, and MIUB ECHO-G for precipitation. The fit between statistical models' outputs and values observed at stations is different, better in cold part of the year. There will be mixed future changes of air temperatures at all the three high mountainous stations. An increase of temperatures is expected in April, November, and December. A decrease will happen in February, July, and October. Mean annual temperatures are expected to rise by 0.1?°C (Botev) to 0.2?°C (Musala and Cherni vrah) in the decade 2075?C2084, but mean annual temperatures at the end of the period with measurements (2009) has already exceeded by far projected values. Trends in precipitation are mixed both in spatial and in temporal directions. Observed decrease of precipitation, especially in the warm half of the year, is not described well in MLR models. The same is valid for annual amounts, which are projected to be higher than those measured in the end of instrumental period (2009). This is opposite to observed trends in recent decades, especially at stations Cherni vrah and Botev, where a significant decrease of precipitation amounts has happened.     

Estimation of surface air temperature trends over the Russian Federation territory using the quantile regression method

The results are presented of the estimation of surface air temperature variations in different climatically quasi-homogeneous regions of Russia using the nonparametric method of regression analysis (quantile regression). Daily observation records from 517 weather stations were used. The quantile regression technique used for analyzing the trends in long-term series allows obtaining information on trends for the whole range of quantile values from 0 to 1 of dependent variable distributions. Seasonal and regional features of daily minimum, mean, and maximum air temperature trends are considered in a wide range of quantile values. The proposed method that generalizes long-term trends obt ained for groups of stations by quantile regression, is applied to quasi-homogeneous climate regions identified on the territory of Russia.     

The effect of air temperature and human thermal indices on mortality in Athens, Greece

This paper investigates whether there is any association between the daily mortality for the wider region of Athens, Greece and the thermal conditions, for the 10-year period 1992–2001. The daily mortality datasets were acquired from the Hellenic Statistical Service and the daily meteorological datasets, concerning daily maximum and minimum air temperature, from the Hellinikon/Athens meteorological station, established at the headquarters of the Greek Meteorological Service. Besides, the daily values of the thermal indices Physiologically Equivalent Temperature (PET) and Universal Thermal Climate Index (UTCI) were evaluated in order to interpret the grade of physiological stress. The first step was the application of Pearson’s χ ² test to the compiled contingency tables, resulting in that the probability of independence is zero (p?=?0.000); namely, mortality is in close relation to the air temperature and PET/UTCI. Furthermore, the findings extracted by the generalized linear models showed that, statistically significant relationships (p?<?0.01) between air temperature, PET, UTCI and mortality exist on the same day. More concretely, on one hand during the cold period (October–March), a 10°C decrease in daily maximum air temperature, minimum air temperature, temperature range, PET and UTCI is related with an increase 13%, 15%, 2%, 7% and 6% of the probability having a death, respectively. On the other hand, during the warm period (April–September), a 10°C increase in daily maximum air temperature, minimum air temperature, temperature range, PET and UTCI is related with an increase 3%, 1%, 10%, 3% and 5% of the probability having a death, respectively. Taking into consideration the time lag effect of the examined parameters on mortality, it was found that significant effects of 3-day lag during the cold period appears against 1-day lag during the warm period. In spite of the general aspect that cold conditions seem to be favourable factors for daily mortality, the air temperature and PET/UTCI exceedances over specific thresholds depending on the distribution reveal that, very hot conditions are risk factors for the daily mortality.     

Comparison of artificial intelligence techniques for prediction of soil temperatures in Turkey

Soil temperature is a meteorological data directly affecting the formation and development of plants of all kinds. Soil temperatures are usually estimated with various models including the artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models. Soil temperatures along with other climate data are recorded by the Turkish State Meteorological Service (MGM) at specific locations all over Turkey. Soil temperatures are commonly measured at 5-, 10-, 20-, 50-, and 100-cm depths below the soil surface. In this study, the soil temperature data in monthly units measured at 261 stations in Turkey having records of at least 20 years were used to develop relevant models. Different input combinations were tested in the ANN and ANFIS models to estimate soil temperatures, and the best combination of significant explanatory variables turns out to be monthly minimum and maximum air temperatures, calendar month number, depth of soil, and monthly precipitation. Next, three standard error terms (mean absolute error (MAE, °C), root mean squared error (RMSE, °C), and determination coefficient (R ²)) were employed to check the reliability of the test data results obtained through the ANN, ANFIS, and MLR models. ANFIS (RMSE 1.99; MAE 1.09; R ² 0.98) is found to outperform both ANN and MLR (RMSE 5.80, 8.89; MAE 1.89, 2.36; R ² 0.93, 0.91) in estimating soil temperature in Turkey.

     

Compilation method for 1 km grid data of monthly mean air temperature for quantitative assessments of climate change impacts

A new method is proposed to compile 1 km grid data of monthly mean air temperature by dynamically downscaling general circulation model (GCM) data with a regional climate model (RCM). The downscaling method used is a technique referred to as the pseudoglobal warming method to reduce GCM bias. For the grid data, RCM data were corrected with data from an existing meteorological network. The correction model for the RCM bias was developed by stepwise multiple regression analysis using the difference in the monthly mean air temperatures between the observation and RCM output as a dependent variable and the geographical factors as independent variables. Our method corrected the RCM bias from 1.69°C to 0.58°C for the month of August in the 1990s (1990–1999).     

Modeling and mapping temperature and precipitation climate data in Greece using topographical and geographical parameters

This study presents a methodology for modeling and mapping the seasonal and annual air temperature and precipitation climate normals over Greece using several topographical and geographical parameters. Data series of air temperature and precipitation from 84 weather stations distributed evenly over Greece are used along with a set of topographical and geographical parameters extracted with Geographic Information System methods from a digital elevation model (DEM). Normalized difference vegetation index (NDVI) obtained from MODIS Aqua satellite data is also used as a geographical parameter. First, the relation of the two climate elements to the topographical and geographical parameters was investigated based on the Pearson’s correlation coefficient to identify the parameters that mostly affect the spatial variability of air temperature and precipitation over Greece. Then a backward stepwise multiple regression was applied to add topographical and geographical parameters as independent variables into a regression equation and develop linear estimation models for both climate parameters. These models are subjected to residual correction using different local interpolation methods, in an attempt to refine the estimated values. The validity of these models is checked through cross-validation error statistics against an independent test subset of station data. The topographical and geographical parameters used as independent variables in the multiple regression models are mostly those found to be strongly correlated with both climatic variables. Models perform best for annual and spring temperatures and effectively for winter and autumn temperatures. Summer temperature spatial variability is rather poorly simulated by the multiple regression model. On the contrary, best performance is obtained for summer and autumn precipitation while the multiple regression model is not able to simulate effectively the spatial distribution of spring precipitation. Results revealed also a relatively weaker model performance for precipitation than that for air temperature probably due to the highly variable nature of precipitation compared to the relatively low spatial variability of air temperature field. The correction of the developed regression models using residuals improved though not significantly the interpolation accuracy.