Dependence of energy consumption on air temperature in Moscow

The dependence of energy consumption on air temperature for every day of the period from 1990 to 2015 is for the first time studied for mid-latitudes using the long-term observational data of the Moscow State University Meteorological Observatory. It is demonstrated that this dependence is generally descending in the Moscow region: energy consumption decreases as air temperature rises. At the same time, the energy consumption increase slows down due to energy saving in case of severe frosts, whereas the opposite trend is manifested in case of abnormally hot weather, that is, the energy consumption increase along with the air temperature rise due to additional consumption for air conditioning. The optimum temperature for energy saving is 18°C. The relationship between energy consumption and air humidity characteristics is analyzed. There is almost no link with relative humidity, while in the link with the partial pressure of water vapor, the dependence of the latter on air temperature is reflected indirectly.     

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.     

Properties of spatial temperature derivatives in the atmospheric surface layer

A pair of parallel cold wires separated in either the vertical or lateral direction was used to obtain the three components _x, _y, _z of the temperature derivative in the streamwise, lateral and vertical directions, respectively. The average absolute skewness values of _x and _z are nonzero and approximately equal, while the skewness of _y is approximately zero. These results appear to be consistent with the presence of a large, three-dimensional organised structure in the surface layer. There is an apparent low-frequency contamination in the spectral density of _y and _z due mainly to small errors in estimating the sensitivity of the cold wires. The temperature derivatives were high-pass filtered, the filter being set to remove possible contributions from the large structure and to minimise low-frequency sensitivity contamination. The filtered rms ratios \~_x/\~_y and \~_x/\~_z were in the range 0.7 to 0.9, a result in qualitative agreement with that obtained in the laboratory boundary layer by Sreenivasan et al. (1977). The skewness of filtered _x or _z is negligible, consistent with local isotropy of small-scale temperature fluctuations and in support of the high wavenumber spectral isotropy discussed in Antonia and Chambers (1978).     

伊春气温因子的时空变化特征

选取伊春市1971-2010年的气温观测资料,进行气温因子的时空变化特征分析.结果表明,伊春近40a的年平均气温呈明显的上升趋势,平均上升0.39 ℃/10 a.1971-1987年处于偏冷期,1988-2010年为增暖期,气温增幅明显.伊春近40a的高温天气日数呈上升趋势,低温天气日数呈显著下降趋势.年平均气温逐年代上升,幅度较大,71-80年代气温非常寒冷,近代变暖显著.     

Daily soil temperature modeling using neuro-fuzzy approach

Soil temperature is an important meteorological parameter which influences a number of processes in agriculture, hydrology, and environment. However, soil temperature records are not routinely available from meteorological stations. This work aimed to estimate daily soil temperature using the coactive neuro-fuzzy inference system (CANFIS) in arid and semiarid regions. For this purpose, daily soil temperatures were recorded at six depths of 5, 10, 20, 30, 50, and 100 cm below the surface at two synoptic stations in Iran. According to correlation analysis, mean, maximum, and minimum air temperatures, relative humidity, sunshine hours, and solar radiation were selected as the inputs of the CANFIS models. It was concluded that, in most cases, the best soil temperature estimates with a CANFIS model can be provided with the Takagi–Sugeno–Kang (TSK) fuzzy model and the Gaussian membership function. Comparison of the models’ performances at arid and semiarid locations showed that the CANFIS models’ performances in arid site were slightly better than those in semiarid site. Overall, the obtained results indicated the capabilities of the CANFIS model in estimating soil temperature in arid and semiarid regions.     

Mean seasonal and spatial variability in global surface air temperature

Summary Using terrestrial observations of shelter-height air temperature and shipboard measurements, a global climatology of mean monthly surface air temperature has been compiled. Data were obtained from ten sources, screened for coding errors, and redundant station records were removed. The combined data base consists of 17 986 independent terrestrial station records and 6 955 oceanic grid-point records. These data were then interpolated to a 0.5° of latitude by 0.5° of longitude lattice using a spherically-based interpolation algorithm. Spatial distributions of the annual mean and intra-annual variance are presented along with a harmonic decomposition of the intra-annual variance.With 7 Figures     

黔西南州极端气温时空演变规律研究

利用黔西南州8个国家气象观测站1961～2020年的极端最高、最低和平均最高、最低气温资料,以年代为周期,分析近60年黔西南州极端最高、最低和平均最高、最低气温的时空演变特征。结果表明：夏季,北亚热带季风湿润气候区及南亚热带季风湿润气候区的平均最高气温、平均极端最高气温在60年代至80年代处于持续上升趋势,在90年代略下滑,进入21世纪后又持续上升。北亚热带季风湿润气候区,近60年平均最高气温升高0.96℃,平均极端最高气温升高0.43℃;南亚热带季风湿润气候区近60年平均最高气温升高0.73℃,平均极端最高气温升高0.62℃。冬季,北亚热带季风湿润气候区平均最低气温及平均极端最低气温在70年代至80年代处于持续上升趋势,90年代略下滑,而进入21世纪又转为上升,近60年平均最低气温升高0.92℃,平均极端最低气温升高1.64℃;南亚热带季风湿润气候区,冬季平均最低气温及平均极端最低气温近60年呈持续上升趋势,平均最低气温升高2.35℃,平均极端最低气温升高3.32℃。     

Fine-scale spatial temperature patterns across a UK conurbation

The public health implications of a warming urban environment mean that appropriate action by planners, designers and health workers will be necessary to minimise risk under future climate scenarios. Data at an appropriate spatial scale are required by user groups in order to identify key areas of vulnerability. Thermal mapping of a UK urban conurbation was carried out during the summers of 2007 and 2008 with the aim of providing high spatial resolution temperature data. The air temperature results showed an average daytime (night time) urban?Crural thermal contrast of 3°C (5°C) on summer days (nights) with ideal urban heat island (UHI) conditions. The intensity of the daytime surface temperature heat island was found to exceed 10°C. The measured data were used to derive an empirical model of spatial temperature patterns based upon characteristics of land use, distance from urban centre and building geometry. This model can be used to provide sub-kilometre resolution temperature data which are required by decision makers and can provide a mechanism for downscaling climate model output.     

近50年阳泉气温时空变化特征

本文就近50年来(1955～2000)阳泉逐月平均气温、平均最低气温、平均最高气温、02时平均气温、14时平均气温5个序列进行了深入细致的诊断分析.结果表明阳泉气温呈非连续的增暖趋势,线性变化约为0.24℃·(10a)-1,90年代较50年代上升了约0.9℃.1985年是一个突变点,这以后增温极为明显.夜间气温增温幅度高于白天增温幅度,冬季气温增温幅度高于夏季增温幅度,表现出明显的不对称性.     

Links between the spatial structure of weather generator and hydrological modeling

Pediatric vehicular hyperthermia (PVH) persists as the leading cause of non-crash, vehicle-related deaths among US children with an average of 37 children dying after being left unattended in motor vehicles each year. Our study aims to demonstrate the microclimate conditions within an enclosed vehicle that lead infants and small children to reach key physiological heat thresholds: uncompensable heating (>37 °C) and heatstroke (>40 °C) under “worst case” conditions. A modified version of the Man-Environment Heat Exchange Model was used to compute the length of time for an infant to reach these thresholds. Several different scenarios were modeled using different initial cabin air temperatures. Assuming full sun exposure and maximum heating rates, an infant may reach uncompensable heating within 5 min and experience hyperthermia anywhere from 15 to 55 min depending on the starting cabin air temperature. The rapid approach of these heat-related thresholds occurs as enclosed vehicles maximize heating and minimize cooling mechanisms, leading to net heating and increase in core body temperatures. Health experts can use this information to support public health messaging on the topic of PVH by explaining why it is important to never leave a child alone in a car and increase the public perception of severity and susceptibility to this ongoing public health issue.     

四川盆地气温的时空分布变化分析

本文利用正交分解(EOF)方法,对四川盆地气温的时空分布进行研究分析,识别出了盆地气温的主要空间型和时间演变特征。在春夏秋冬不同季节,四川盆地气温场呈一致性的正值,反映四川盆地气温大范围位相一致的变化,表现为全盆地性冷(暖),并且气温场在不同的季节,其分布特征具有较大的相似性和稳定性。从第一特征向量场对应时间系数演变过程来看,冬、夏两季的波动范围较春、秋两季的波动范围小;从第一时间系数年际变化分析,除盆地夏季平均气温没有明显变化外,冬、春、秋季的气温从20世纪90年代后期均持续表现为上升趋势,出现明显的气候变暖突变。冬季与秋季的气温分布及春季与秋季的气温分布具有较为显著的正相关关系。     

四川盆地气温的时空分布变化分析

本文利用正交分解(EOF)方法,对四川盆地气温的时空分布进行研究分析,识别出了盆地气温的主要空间型和时间演变特征.在春夏秋冬不同季节,四川盆地气温场呈一致性的正值,反映四川盆地气温大范围位相一致的变化,表现为全盆地性冷(暖),并且气温场在不同的季节,其分布特征具有较大的相似性和稳定性.从第一特征向量场对应时间系数演变过程来看,冬、夏两季的波动范围较春、秋两季的波动范围小;从第一时间系数年际变化分析,除盆地夏季平均气温没有明显变化外,冬、春、秋季的气温从20世纪90年代后期均持续表现为上升趋势,出现明显的气候变暖突变.冬季与秋季的气温分布及春季与秋季的气温分布具有较为显著的正相关关系.     

Spatiotemporal modeling of monthly soil temperature using artificial neural networks

Soil temperature data are critical for understanding land–atmosphere interactions. However, in many cases, they are limited at both spatial and temporal scales. In the current study, an attempt was made to predict monthly mean soil temperature at a depth of 10 cm using artificial neural networks (ANNs) over a large region with complex terrain. Gridded independent variables, including latitude, longitude, elevation, topographic wetness index, and normalized difference vegetation index, were derived from a digital elevation model and remote sensing images with a resolution of 1 km. The good performance and robustness of the proposed ANNs were demonstrated by comparisons with multiple linear regressions. On average, the developed ANNs presented a relative improvement of about 44 % in root mean square error, 70 % in mean absolute percentage error, and 18 % in coefficient of determination over classical linear models. The proposed ANN models were then applied to predict soil temperatures at unsampled locations across the study area. Spatiotemporal variability of soil temperature was investigated based on the obtained database. Future work will be needed to test the applicability of ANNs for estimating soil temperature at finer scales.     

Comparison of spatial interpolation methods for gridded bias removal in surface temperature forecasts

All numerical weather prediction (NWP) models inherently have substantial biases, especially in the forecast of near-surface weather variables. Statistical methods can be used to remove the systematic error based on historical bias data at observation stations. However, many end users of weather forecasts need bias corrected forecasts at locations that scarcely have any historical bias data. To circumvent this limitation, the bias of surface temperature forecasts on a regular grid covering Iran is removed, by using the information available at observation stations in the vicinity of any given grid point. To this end, the running mean error method is first used to correct the forecasts at observation stations, then four interpolation methods including inverse distance squared weighting with constant lapse rate (IDSW-CLR), Kriging with constant lapse rate (Kriging-CLR), gradient inverse distance squared with linear lapse rate (GIDS-LR), and gradient inverse distance squared with lapse rate determined by classification and regression tree (GIDS-CART), are employed to interpolate the bias corrected forecasts at neighboring observation stations to any given location. The results show that all four interpolation methods used do reduce the model error significantly, but Kriging-CLR has better performance than the other methods. For Kriging-CLR, root mean square error (RMSE) and mean absolute error (MAE) were decreased by 26% and 29%, respectively, as compared to the raw forecasts. It is found also, that after applying any of the proposed methods, unlike the raw forecasts, the bias corrected forecasts do not show spatial or temporal dependency.     

贵州夏季最高气温变率的时空分布特征

选取贵州19个代表站点47 a夏季日最高气温月平均及季平均资料,采用EOF分解方法对贵州夏季最高气温的变化进行分析,结果表明:前两个模态的累积方差贡献在82%~89.7%,特征值对应特征向量和时间系数能够较好地反映时空分布特征,第一特征向量代表的空间分布显示:全省变化趋势一致,东北部变化较西南部大,北部、东北部变率变化较南部西南部大;相应地时间系数变化幅度大,都有2~4 a的周期振荡;从第二特征向量代表的空间分布可知:各时段呈现出不一样的变化趋势,时间系数的值也普遍较第一特征向量小,突变不明显。     

Long-term variations of long-wave radiation in Moscow

Using the data of long-term (1958–2012) actinometric and meteorological observations of the Meteorological Observatory of Lomonosov Moscow State University, the observed and computed long-wave fluxes and the factors defining their variability are estimated. Obtained are the normals and determined are the limits of variability of effective radiation. Analyzed are the peculiarities of atmospheric back radiation. Demonstrated is the trend towards the decrease (in absolute value) in effective radiation caused by the increase in the atmospheric back radiation flux (E _a). The trend towards the increase in the atmospheric back radiation is determined by the increase in the values of meteorological parameters: cloudiness, atmospheric moisture content, and temperature. The content of aerosol and carbon dioxide does not affect the long-term variations of E _a registered in Moscow. Derived empirical formulae can be recommended for estimating the atmospheric back radiation and effective radiation of the Earth surface using meteorological observations.     

Synoptic Climatology of Air Pollution in Moscow

Summary Seasonal synoptic indices are developed employing principal components analysis and cluster analysis for Moscow and using surface and upper air meteorological data. Synoptic types are analysed in terms of circulation patterns and air mass characteristics. The indices are used to examine weather-induced variability in CO and NO₂ concentrations for different land use types and in the context of multiple pollution sources. Anticyclonic conditions in spring, summer and autumn are identified as producing high pollution concentrations. Local depressions developing in the low pressure gradient fields in summer are another synoptic type resulting in high levels of air pollution. Extremely cold anticyclonic conditions in winter result in high CO but low NO₂ levels. Weather patterns characterised by strong winds associated either with an established zonal flow or the passage of a cold front caused high NO₂ concentrations in the areas affected by elevated sources. Received October 28, 1997 Revised January 26, 1998     

Effects of regional temperature,wind speed and soil wetness on spatial structure of surface air temperature

Summary Climate parameters are usually collected on some grid or pattern that is supposed to represent the unobserved neighborhood. Spatial dependence is a measure of the extent to which observed data represent the unobserved neighborhood. Geostatistical analyses provide procedures for measuring spatial dependence. In this study, semivariograms were estimated from hourly observations of screen-height air temperature obtained from a dense meteorological observation network. The range and spatial component normalized by the sill were estimated from the semivariogram in order to obtain information on the spatial structure of the air temperature. Zones of spatial correlation were delineated, using the range of the semivariogram. Scales of spatial correlation depended on the regional mean air temperature and regional wetness at the ground. The range or spatial scale for data collected in winter was larger than those in summer. The range under wet conditions was larger than under dry conditions. Effects of regional wind speed on range were different, depending on the regional mean air temperature. The normalized spatial component was used as an index for measuring continuities on the interpolation surface. The normalized spatial component generally increased as the range increased.With 5 Figures     

Modeling daily soil temperature using data-driven models and spatial distribution

The objective of this study is to develop data-driven models, including multilayer perceptron (MLP) and adaptive neuro–fuzzy inference system (ANFIS), for estimating daily soil temperature at Champaign and Springfield stations in Illinois. The best input combinations (one, two, and three inputs) can be identified using MLP. The ANFIS is used to estimate daily soil temperature using the best input combinations (one, two, and three inputs). From the performance evaluation and scatter diagrams of MLP and ANFIS models, MLP 3 produces the best results for both stations at different depths (10 and 20 cm), and ANFIS 3 produces the best results for both stations at two different depths except for Champaign station at the 20 cm depth. Results of MLP are better than those of ANFIS for both stations at different depths. The MLP-based spatial distribution is used to estimate daily soil temperature using the best input combinations (one, two, and three inputs) at different depths below the ground. The MLP-based spatial distribution estimates daily soil temperature with high accuracy, but the results of MLP and ANFIS are better than those of the MLP-based spatial distribution for both stations at different depths. Data-driven models can estimate daily soil temperature successfully in this study.