Estimation of the monthly and annual mean maximum and mean minimum air temperature values in Greece

In this study, regression equations to estimate the monthly and annual values of the mean maximum and mean minimum air temperatures in Greece are derived. For this purpose, data from 87 meteorological stations distributed all over Greece are used. Geographical parameters, i.e., altitude, latitude, longitude, minimum distance from the sea and an index of terrain morphology, are used as independent variables. These equations explain 79?C97% of the variance of the temperature values and have standard error of estimate between 0.59 and 1.20°C. Data from 37 other meteorological stations are used to validate the accuracy of the equations. Topographic or climatic factors, which could not be introduced into the equations, are responsible for most temperature residuals >0.5°C or <?0.5°C. Moreover, some particular emphasis has been given to the values of the regression coefficient for the altitude, since it is the estimator for the mean lapse rate of air temperature.     

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.     

1881-2010年哈尔滨市气候变化及其影响

利用哈尔滨本站1881-2010年的月平均气温、1909-2010年的月总降水量和1961-2010年哈尔滨所辖区、县（市）月平均气温、月总降水量资料,采用线性趋势分析方法,计算了哈尔滨市气温、降水变化速率,分析了哈尔滨市气候变化特征;阐述了气候变化对哈尔滨市的影响。结果表明：近50a,除巴彦7月气温略呈下降趋势外,哈尔滨各区、县（市）各月、季、年平均气温均呈升高趋势。哈尔滨各区、县（市）各月、季、年总降水量变化趋势不一致。近130a,哈尔滨市年、季平均气温均呈明显的上升趋势,20世纪80年代开始明显增温,21世纪开始增温尤为显著。近百年来,哈尔滨市年、季总降水量均呈减少趋势。气候变化对哈尔滨市农业、能源等方面的影响有利有弊,但对于水资源、人体健康和交通等方面有较大的负面影响。     

1951—2009年中国不同区域气温和降水量变化特征

为了研究中国不同区域气候变化特征,将全国按照气候区域划分为11个气候区,并利用1951—2009年中国194个国家基本/基准站月、年气温和降水观测资料,对全国及每个气候区平均温度及降水量的年和季节变化特征进行分析。结果表明：中国及各地区增温趋势均为极显著增加,尤其近20 a增温速度更快;而2007年成为有记录以来最暖的...     

Empirical models of monthly and annual albedo in managed boreal forests of interior Norway

An 11-year remotely sensed surface albedo dataset coupled with historical meteorological and stand-level forest management data for a variety of stands in Norway’s most productive logging region is used to develop regression models describing temporal changes in forest albedo following clear-cut harvest disturbance events. Datasets are grouped by dominant tree species, and two alternate multiple regression models are developed and tested following a potential-modifier approach. This result in models with statistically significant parameters (p?<?0.05) that explain a large proportion of the observed variation, requiring a single canopy modifier predictor coupled with either monthly or annual mean air temperature as a predictor of a stand’s potential albedo. Models based on annual mean temperature predict annual albedo with errors (RMSE) in the range of 0.025–0.027, while models based on monthly mean temperature predict monthly albedo with errors ranging between of 0.057–0.065 depending on the dominant tree species. While both models have the potential to be transferable to other boreal regions with similar forest management regimes, further validation efforts are required. As active management of boreal forests is increasingly seen as a means to mitigate climate change, the presented models can be used with routine forest inventory and meteorological data to predict albedo evolution in managed forests throughout the region, which, together with carbon cycle modeling, can lead to more holistic climate impact assessments of alternative forest harvest scenarios and forest product systems.     

Accuracy of temperature and precipitation reproduction in Russia with global climate archives

Four climate archives at the regular grid points organized on the basis of interpolation and model reanalysis of observational data are considered. The regional differences between the air temperature and precipitation fields are estimated, the comparison with the observational data at 455 weather stations over the Russian territory and adjacent countries is considered. It is noted that global archives give biased estimates of the monthly and seasonal precipitation amount, annual mean air temperature and temperature sums of the warm and cold seasons. The maps of errors of climate characteristics for each of the archives are calculated and compiled, probable reasons for poor accuracy are analyzed.     

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.     

西南地区不同地形台阶气温时空变化特征

利用西南地区135个站1961—2005年的年、月气温资料,按海拔高差及地形气候特征划分成不同的三级地形台阶及4个分区,分析了不同分区的气温时空变化特征。结果表明：西南地区大部年平均气温表现出明显的增温趋势,上升趋势最显著的地区在西藏高原等高海拔地区,而在四川的东北部及云南北部存在降温中心。各分区四季的增温速率排序与全国平均情况有所不同,依次为冬季、秋季、夏季或春季,且均表现出冬季增温趋势明显大于其他季节的特性。各分区年平均气温20世纪60年代至80年代中期基本表现为明显的下降趋势或无明显的增减趋势,但自1997年以来,均表现出显著的增温趋势。突变检测的结果也表明,各分区年平均气温突变的区域或突变点大部分发生在90年代后期以后,且高海拔地区增温突变启动时间早于其他低海拔地区。     

Interpolation of climate variables and temperature modeling

Geographic Information Systems (GIS) and modeling are becoming powerful tools in agricultural research and natural resource management. This study proposes an empirical methodology for modeling and mapping of the monthly and annual air temperature using remote sensing and GIS techniques. The study area is Gangetic West Bengal and its neighborhood in the eastern India, where a number of weather systems occur throughout the year. Gangetic West Bengal is a region of strong heterogeneous surface with several weather disturbances. This paper also examines statistical approaches for interpolating climatic data over large regions, providing different interpolation techniques for climate variables' use in agricultural research. Three interpolation approaches, like inverse distance weighted averaging, thin-plate smoothing splines, and co-kriging are evaluated for 4°?×?4° area, covering the eastern part of India. The land use/land cover, soil texture, and digital elevation model are used as the independent variables for temperature modeling. Multiple regression analysis with standard method is used to add dependent variables into regression equation. Prediction of mean temperature for monsoon season is better than winter season. Finally standard deviation errors are evaluated after comparing the predicted temperature and observed temperature of the area. For better improvement, distance from the coastline and seasonal wind pattern are stressed to be included as independent variables.     

1881—2010年哈尔滨市气候变化及其影响

利用哈尔滨站1881—2010年的月平均气温、1909—2010年的月总降水量和1961—2010年哈尔滨所辖区、县（市）月平均气温、月总降水量资料,采用线性趋势分析方法,计算了哈尔滨市气温、降水变化速率,分析了哈尔滨市气候变化特征;阐述了气候变化对哈尔滨市的影响。结果表明：近50 a,除巴彦7月气温略呈下降趋势外,哈尔滨市各区、县（市）各月、季、年平均气温均呈升高趋势。哈尔滨各区、县（市）各月、季、年总降水量变化趋势不一致。近130 a,哈尔滨市年、季平均气温均呈明显的上升趋势,20世纪80年代开始明显增温,21世纪开始增温尤为显著。近百年来,哈尔滨市年、季总降水量均呈减少趋势。气候变化对哈尔滨市农业、能源等方面的影响有利有弊,但对于水资源、人体健康和交通等有较大的负面影响。     

1951—2009年中国不同区域气温和降水量变化特征

为了研究中国不同区域气候变化特征,将全国按照气候区域划分为11个气候区,并利用1951—2009年中国194个国家基本/基准站月、年气温和降水观测资料,对全国及每个气候区平均温度及降水量的年和季节变化特征进行分析。结果表明：中国及各地区增温趋势均为极显著增加,尤其近20 a增温速度更快;而2007年成为有记录以来最暖的一年;中国冬季平均温度上升趋势最明显,春季次之,夏季几乎没有变化。中国平均年总降水量20世纪50年代最多,2000年代最少;而华北地区的年降水量减少最快;在四季降水中,中国只有夏季降水量波动略有增加,且各区域降水分布具有明显的南北差异特征。     

Development and performance evaluation of statistical models correlating air pollutants and meteorological variables at Pantnagar,India

Ambient air quality in respect of SO₂, NO₂ and total suspended particulate matter (TSPM) was monitored at Pantnagar, India from May, 2008 to April, 2009 and statistically analyzed with meteorological variables such as relative humidity (RH), wind speed (WS), precipitation (P) and mean air temperature (T). TSPM was found to be the major air pollutant causing significant deterioration of air quality with annual mean concentrations of 280 μg/m³. Further, weekly mean air pollutant concentrations were statistically analyzed through stepwise multiple linear regression analysis in respect of independent meteorological variables to develop suitable statistical models. Both NO₂ and TSPM concentrations were found to have been influenced by meteorological variables with coefficient of determination (R²) of 82.21 and 92.84%, respectively. However, atmospheric SO₂ revealed only 22.87% of dependencies on meteorological variables. Partial correlation coefficients revealed that wind speed has the maximum influence (77.80 and 31.50%) on proposed equations for NO₂ and SO₂, closely followed by weekly mean temperature (73.60 and 24.30%). However, in case of TSPM, individual contribution of ambient temperature (94.40%) was found maximum, followed by relative humidity (86.50%). Model performances were evaluated through both quantitative data analysis techniques and statistical methods. Nearly 98 and 95% of potential error has been explained by the model developed for TSPM and NO₂, while in case of SO₂, it is found as only 61%. Therefore, performances of models (for TSPM and NO₂) to predict ambient weekly mean concentrations based on forecasted weather parameters were found to be excellent, however, performance of model developed for SO₂ was found only satisfactory.     

1980-2008年黑龙江省气候变暖及其对大豆种植的影响

基于黑龙江省10个农业气象观测站1980-2008年地面观测田平行观测资料和逐日气象资料,运用气候倾向率、M-K检测和统计回归等方法,分析研究区表征气候变暖的主要气候要素的时空变化规律以及气候变暖对大豆的影响。结果表明：1980-2008年,研究区气候出现明显变暖趋势,年平均气温升高,热量增加,无霜期延长,且区域间存在差异,南部地区热量资源增加显著。大豆百粒重随9月平均气温的升高而增加,却随年平均气温的升高而下降,表明秋季增温对大豆产量有正效应,而年平均气温的升高对大豆产量存在负作用。气候变暖对大豆生产的影响是利弊兼存,在调整种植布局时应给予重点关注。     

1958—2004年江汉平原气温变化特征分析

根据1958—2004年江汉平原地区8个站点的月平均气温资料,采用一元回归、相关分析和距平等数理统计方法对近47 a江汉平原地区的多年年、月平均气温和各站点年平均气温随时间的变化进行分析。结果表明:近47 a江汉平原地区年平均气温呈明显上升趋势,四季气温特别是秋、冬季的气温上升显著,而夏季有变凉趋势;月平均气温除7月呈下降趋势之外,其余各月均上升;太阳黑子峰值年和谷值年前后,江汉平原地区的年平均气温易出现极值。     

Trend of climate variability in China during the past decades

Trends in precipitation and mean air temperature in China are estimated, and trend analysis on statistical moments of residuals is further used to investigate climate variability at different timescales. Trends of statistical moments for residuals (i.e. detrended series of climate records) are estimated by using least-square method and Mann?CKendall test. Results show that upward trend is detected in annual mean air temperature but no linear trend for annual precipitation in China. Weak trend is found for variability of precipitation while no trend is found for that of air temperature for China as a whole. But some regional features of climate variability are observed. It is found that the northwest of China shows a significant increasing for precipitation variability, which is consistent with previous work, especially for monthly precipitation. No change is detected in monthly mean air temperature for all stations, while small decreasing and increasing trends are detected for variability of annual mean air temperature in northeast of China and southwest of China, respectively.     

塔克拉玛干沙漠腹地1961-1998年逐月平均气温序列的重建

 利用塔里木盆地周边27个气象站1961-2006年逐月平均气温和塔中气象站1999-2006年逐月平均气温资料,同时选取1961-2006年NCEP/NCAR 2.5°×2.5°经纬度距地表2 m的月平均气温再分析格点资料,分别用逐步回归分析、EOF分解和NCEP资料3种方法对塔中气象站1961-1998年历年逐月平均气温序列进行了恢复与重建,分析了误差,并与周边气象站的变化特征进行对比。结果表明,逐步回归和EOF法都能够作为重建塔中逐月平均气温的方法,但相对而言,逐步回归法重建的序列误差更小,平均拟合绝对误差为0.3℃,最大绝对误差为1.9℃。而NCEP/NCAR资料由于冬季存在明显的系统性误差,数值显著偏高,不能用于塔中气温序列的重建。     

Simulation and Regionalization of Daily Global Solar Radiation: A Case Study in Quebec,Canada

Global solar radiation (GSR) is essential for agricultural and plant growth modelling, air and water heating analyses, and solar electric power systems. However, GSR gauging stations are scarce compared with stations for monitoring common meteorological variables such as air temperature and relative humidity. In this study, one power function, three linear regression, and three non-linear models based on an artificial neural network (ANN) are developed to extend short records of daily GSR for meteorological stations where predictors (i.e., temperature and/or relative humidity) are available. The seven models are then applied to 19 meteorological stations located across the province of Quebec (Canada). On average, the root-mean-square errors (RMSEs) for ANN-based models are 0.33–0.54?MJ?m^?2?d^?1 smaller than those for the power function and linear regression models for the same input variables, indicating that the non-linear ANN-based models are more efficient in simulating daily GSR. Regionalization potential of the seven models is also evaluated for ungauged stations where predictors are available. The power function and the three linear regression models are tested by interpolating spatially correlated at-site coefficients using universal kriging or by applying a leave-one-out calibration procedure for spatially uncorrelated at-site coefficients. Regional ANN-based models are also developed by training the model based on the leave-one-out procedure. The RMSEs for regional ANN models are 0.08–0.46?MJ?m^?2?d^?1 smaller than for other models using the same input conditions. However, the regional ANN-based models are more sensitive to new station input values compared with the other models. Maps of interpolated coefficients and regional equations of the power function and the linear regression models are provided for direct application to the study area.     

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.     

Shift changes and monotonic trends in autocorrelated temperature series over Iran

Temperature data from 29 synoptic stations in Iran for a period of 40?years (1966–2005) were analyzed to test for the existence of monotonic trends and shift changes in the annual, seasonal, and monthly mean air temperature series using the Mann–Kendall and Mann–Whitney tests. The influences of significant lag-1 serial correlation were eliminated from data by the trend-free pre-whitening method prior to the trend analysis. The magnitude of the temperature trends was derived from the Theil–Sen’s slope estimator. It was found that annual mean air temperature increased at 25 out of the 29 stations, of which 17 stations showed significant monotonic trends. The magnitude of the annual mean air temperature trends averagely was (+)0.224°C per decade. Most of the stations with the significant positive monotonic trends had a significant upward shift change. The analysis indicated that the change point year of the significant upward shift changes was 1972 for the whole stations except the coastal ones. Moreover, the strongest monotonic increasing trends and upward shift changes were observed in summer especially in August and September. The spatial analysis of the mean air temperature trends revealed the highest numbers of significant monotonic trends in the big cities of Iran. These findings provide more insights for better understanding of regional temperature behavior in the study area.     

重庆山地区域气象要素空间插值方法对比

利用重庆地区1999年和2018年气象数据, 分别采用薄盘光滑样条、协同克里金、普通克里金、反距离加权4种方法, 从年和月两种尺度对气温、降水、太阳总辐射三个要素进行空间插值; 采取交叉验证方法, 用MAE、MRE、RMSE评估插值精度, 确定各要素最优插值方法。结果表明: 气温和太阳总辐射最优插值方法为薄盘光滑样条, 降水为反距离加权; 插值精度上气温、太阳总辐射高值月份优于低值月份, 降水则相反, 但三个要素均表现出年尺度优于月尺度。MRE检验表明, 插值精度为气温>太阳总辐射>降水, 1999年年尺度插值精度分别为1.86%、4.60%、6.87%, 月尺度插值精度分别为2.79%、5.82%、17.42%;2018年太阳总辐射年、月尺度插值精度分别为3.03%、4.88%, 区域站加密后气温、降水年尺度插值精度分别为2.03%、11.20%, 月尺度对应插值精度分别为3.20%、23.14%。