利用ERA-Interim数据构建海区大气加权平均温度模型

针对海洋区域尤其远海缺乏探空资料,且常用的Bevis经验模型在海区存在模型系统误差的问题,研究基于ERA-Interim再分析资料构建海洋区域E-T_m回归模型。利用不同时间的ERA-Interim数据及近海探空资料,对E-T_m回归模型的拟合效果及预报能力进行检验,并与常规T_m获取方法进行比较,结果表明,E-T_m回归模型拟合效果较好,相比Bevis经验模型,其预报稳定性及精度更高;在典型海区与基于探空资料建立的本地化模型进行了预报精度的比较,结果表明,2. 5°×2. 5°分辨率的E-T_m模型与探空资料建立的本地化模型精度相当,可以在无法建立探空站的海域进行使用,仅存在1%的平均转换误差。     

江苏省夏季最高温度定量预报方法

以江苏省徐州、南京、射阳3个探空站2002～2006年7～8月逐日观测资料为基础,选取了影响最高温度变化的因子,利用逐步回归方法建立了以徐州、南京、射阳3地为中心的区域预报模型,并对模型的回归效果和预报情况进行分析.剖析了其用于实际预报的合理性和可信度,同时与欧洲中期天气预报中心动力数值预报结果相结合,利用高斯权重插值方法将预报场的格点资料捕值到江苏各站点,通过PP法,完成了江苏省最高温度的定量预报.预报当天最高温度误差在1℃以下的概率为5O%左右,2℃以下概率在8O%左右,该方法可用于最高温度预报.     

THE APPROACH TO REMOTE SENSING OF WATER VAPOR BASED ON GPS AND LINEAR REGRESSION Tm IN EASTERN REGION OF CHINA

The approach to remote sensing of water vapor by using global positioning systems(GPS) isdiscussed.In order to retrieve the vertical integrated water vapor(IWV) or the precipitable water(PW),the weighted "mean temperature" of the atmosphere,T_m would be estimated to the specificarea and season.T_m depends on surface temperature,tropospheric temperature profile,and thevertical distribution of water vapor.The surface temperature dependence is borne out by acomparison of T_m and the values of surface temperature T_s using radiosonde profiles of BeijingStation(No.54511) throughout 1992.The analysis of radiosonde profiles spanning a one-yearinterval(1992) from sites in eastern region of China with a latitude range of 20-50°N and alongitude range of 100-130°E yields the coefficients a and b of a linear regression equation T_m=a+bT_s.     

中国区域性大气加权平均温度线性模型精度评估

以欧洲中期天气预报中心的再分析资料ERA5为参考数据,评估由探空数据建立的中国区域88个单站大气加权平均温度（T_m）与地表气温（T_s）线性关系模型的精度.各站T_m-T_s线性模型计算的T_m（计算值）与ERA5 气压层数据积分所得的T_m（参考值）间偏差均方根值（RMSE）为1.8~5.5 K.不同站模型计算值与参考值间存在-1.22~4.54 K 的系统性偏差,且绝大多数测站（82个站）系统性偏差为正值,即模型计算值总体上大于参考值.补偿各站系统性偏差后,模型计算值与参考值间RMSE降为1.5~3.5 K.与使用中国区域统一模型相比,使用单站模型平均能提高0.6 K的T_m计算精度,尤其在中国西部、西北和内蒙区域,精度提高可达1~3.9 K.对所有测站模型计算值和参考值间偏差时序进行分析,发现超过半数测站的偏差存在明显季节性变化.     

Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region

Soil temperature (T _S) strongly influences a wide range of biotic and abiotic processes. As an alternative to direct measurement, indirect determination of T _S from meteorological parameters has been the focus of attention of environmental researchers. The main purpose of this study was to estimate daily T _S at six depths (5, 10, 20, 30, 50 and 100?cm) by using a multilayer perceptron (MLP) artificial neural network (ANN) model and a multivariate linear regression (MLR) method in an arid region of Iran. Mean daily meteorological parameters including air temperature (T _a), solar radiation (R _S), relative humidity (RH) and precipitation (P) were used as input data to the ANN and MLR models. The model results of the MLR model were compared to those of ANN. The accuracy of the predictions was evaluated by the correlation coefficient (r), the root mean-square error (RMSE) and the mean absolute error (MAE) between the measured and predicted T _S values. The results showed that the ANN method forecasts were superior to the corresponding values obtained by the MLR model. The regression analysis indicated that T _a, RH, R _S and P were reasonably correlated with T _S at various depths, but the most effective parameters influencing T _S at different depths were T _a and RH.     

Spatial gridding of daily maximum and minimum 2 m temperatures supported by satellite observations

The usefulness of two remotely sensed variables, land surface temperature (LST) and cloud cover (CC), as predictors for the gridding of daily maximum and minimum 2 m temperature (T _min/T _max) was assessed. Four similar gridding methods were compared, each of which applied regression kriging to capture the spatial variation explained by the predictors used; however, both methods differed in the interpolation steps performed and predictor combinations used. The robustness of the gridding methods was tested for daily observations in January and July in the period 2009–2011 and in two different regions: the Central European region (CER) and the Iberian Peninsula (IP). Moreover, the uncertainty estimate provided by each method was evaluated using cross-validation. The regression analyses for both regions demonstrated the high predictive skills of LST for T _min and T _max on daily and monthly timescales (and lower predictive skills of CC). The application of LST as a predictor considerably improved the gridding performance over the IP region in July; however, there was only a slight improvement over the CER region. CC reduced the loss of spatial variability in the interpolated daily T _min/T _max values over the IP region. The interpolation skill was mainly controlled by the station density, but also depended on the complexity of the terrain. LST was shown to be of particular value for very low station densities (1 station per 50,000 km²). Analyses with artificially decreasing station densities showed that even in the case of very low station densities, LST allows the determination of useful regression functions.     

Comprehensive modeling of monthly mean soil temperature using multivariate adaptive regression splines and support vector machine

Soil temperature (T _s) and its thermal regime are the most important factors in plant growth, biological activities, and water movement in soil. Due to scarcity of the T _s data, estimation of soil temperature is an important issue in different fields of sciences. The main objective of the present study is to investigate the accuracy of multivariate adaptive regression splines (MARS) and support vector machine (SVM) methods for estimating the T _s. For this aim, the monthly mean data of the T _s (at depths of 5, 10, 50, and 100 cm) and meteorological parameters of 30 synoptic stations in Iran were utilized. To develop the MARS and SVM models, various combinations of minimum, maximum, and mean air temperatures (T _min, T _max, T); actual and maximum possible sunshine duration; sunshine duration ratio (n, N, n/N); actual, net, and extraterrestrial solar radiation data (R _s, R _n, R _a); precipitation (P); relative humidity (RH); wind speed at 2 m height (u ₂); and water vapor pressure (V_p) were used as input variables. Three error statistics including root-mean-square-error (RMSE), mean absolute error (MAE), and determination coefficient (R ²) were used to check the performance of MARS and SVM models. The results indicated that the MARS was superior to the SVM at different depths. In the test and validation phases, the most accurate estimations for the MARS were obtained at the depth of 10 cm for T _max, T _min, T inputs (RMSE = 0.71 °C, MAE = 0.54 °C, and R ² = 0.995) and for RH, V _p, P, and u ₂ inputs (RMSE = 0.80 °C, MAE = 0.61 °C, and R ² = 0.996), respectively.     

Spatial gridding of daily maximum and minimum temperatures in Europe

We developed an operationally applicable land-only daily high-resolution (5?km?×?5?km) gridding method for station observations of minimum and maximum 2?m temperature (T _min/T _max) for Europe (WMO region VI). The method involves two major steps: (1) the generation of climatological T _min/T _max maps for each month of the year using block regression kriging, which considers the spatial variation explained by applied predictors; and (2) interpolation of transformed daily anomalies using block kriging, and combination of the resulting anomaly maps with climatological maps. To account for heterogeneous climatic conditions in the estimation of the statistical parameters, these steps were applied independently in overlapping climatic subregions, followed by an additional spatial merging step. Uncertainties in the gridded maps and the derived error maps were quantified: (a) by cross-validation; and (b) comparison with the T _min/T _max maps estimated in two regions having very dense temperature observation networks. The main advantages of the method are the high quality of the daily maps of T _min/T _max, the calculation of daily error maps and computational efficiency.     

Seasonal reversal at Miryang Eoreumgol (Ice Valley), Korea: observation and monitoring

Climate change information required for impact studies is of a much finer scale than that provided by Global circulation models (GCMs). This paper presents an application of partial least squares (PLS) regression for downscaling GCMs output. Statistical downscaling models were developed using PLS regression for simultaneous downscaling of mean monthly maximum and minimum temperatures (T _max and T _min) as well as pan evaporation to lake-basin scale in an arid region in India. The data used for evaluation were extracted from the NCEP/NCAR reanalysis dataset for the period 1948?C2000 and the simulations from the third-generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1, and COMMIT for the period 2001?C2100. A simple multiplicative shift was used for correcting predictand values. The results demonstrated that the downscaling method was able to capture the relationship between the premises and the response. The analysis of downscaling models reveals that (1) the correlation coefficient for downscaled versus observed mean maximum temperature, mean minimum temperature, and pan evaporation was 0.94, 0.96, and 0.89, respectively; (2) an increasing trend is observed for T _max and T _min for A1B, A2, and B1 scenarios, whereas no trend is discerned with the COMMIT scenario; and (3) there was no trend observed in pan evaporation. In COMMIT scenario, atmospheric CO₂ concentrations are held at year 2000 levels. Furthermore, a comparison with neural network technique shows the efficiency of PLS regression method.     

Comparison of mean radiant temperature from field experiment and modelling: a case study in Freiburg,Germany

Mean radiant temperature (T _mrt) based on two measurement methods and outputs from three models are compared in this study. They are the six direction radiation method, globe thermometer method, RayMan model, ENVI-met model and SOLWEIG model. The comparison shows that globe thermometer method may overestimate the T _mrt since wind velocity is a key variable in the estimation based on this method. For better estimation, T _mrt measured by the globe-thermometer method be corrected by the imported wind speed (stable, low and assuming wind speed) and validated by the six-direction radiation method. The comparison of models shows that the RayMan model’s evaluation of T _mrt involving global radiation with fine time resolution was better than the corresponding evaluations under the other two models (ENVI-met and SOLWEIG) in this case. However, the RayMan model can only assess T _mrt for a one-point one-time context, whereas the other two models can evaluate two-dimensional T _mrt. For two-dimensional evaluations of T _mrt, SOLWEIG have a better prediction of T _mrt than ENVI-met, and ENVI-met can simulate several different variables, which are wind field, particle distribution, CO₂ distribution and the other thermal parameters (T _a, surface temperature and radiation fluxes), that SOLWEIG cannot.     

用Kriging方法对中国历史气温数据插值可行性讨论

使用 Kriging 插值方法对已经过质量控制和均一化的1951年1月-2004年12月中国全部基本、基准站气温资料逐月进行空间插值.通过站点的实际序列与插值后格点序列进行比较,针对相关系数和线性趋势等多个量来检验 Kriging 方法对气候资料插值的效果.结果表明:插值前、后的气温空间分布、气温变化趋势都非常一致,从年际变化来看,插值序列与实际站点序列的相关性也非常高.对比分析还发现用距平序列的插值效果要明显优于原始气温序列插值,但不同的球面模型半径插值在站点稀疏地区的插值结果差别较大,需要先对气候要素进行空间代表性进行分析,以合适的球面半径进行插值.对于气候变化比较特殊的地区,如中国西南部分地区,插值序列很难反映更小尺度的气候变化规律.     

The climatological characteristics of photosynthetically active radiation in arid and semi-arid regions of China

Photosynthetically active radiation (Q _p ) is a key variable in models of net primary productivity and carbon cycle modelling. The relationship between broadband global solar radiation (R _s) and Q _p is investigated using 6?years of radiation data collected at 9 sites in arid and semi-arid regions of China. The dependence of Q _p /R _S on aerosol optical depth (AOD) and water vapour content are also discussed. A simple and efficient all-weather empirically derived model is developed to estimate Q _p from R _s. The annual average daily Q _p in arid and semi-arid areas is 29.9?±?11.7 and 27.3?±?10.1?mol?m^-2 d^-1, respectively. The highest value (31.9?±?11.3?mol?m^-2 d^-1) appears at Linze in the arid area. The lowest value (24.3?±?9.7?mol?m^-2 d^-1) appears at Ansai in the semi-arid area. The results show that the monthly variation of the Q _p /R _s ratio ranges from 1.69?±?0.19?mol?MJ^-1 in Aksu to 1.91?±?0.08?mol?MJ^-1 in Fukang. There is a small decreasing trend of the ratio of Q _p to R _s (PAR fraction) in arid and semi-arid regions because of the recent increase in fine aerosols. A simple and efficient empirically model suit for all-weather condition was developed to estimate Q _p from R _s. The slope a and intercept b of the regression line between estimated and measured values is close to 1 and zero, respectively. The application of the model to data collected from different locations also results in reasonable estimates of Q _p .     

A model for inferring canopy and underlying soil temperatures from multi-directional measurements

Thermal emission is modeled from a canopy/soil surface, where the soil and the leaves are at different temperatures,T _g andT _c respectively. The temperatureT _m corresponding to a radiometer reading is given by $$B_\lambda (T_m ) = \chi B_\lambda (T_g ) + (1 - \chi )B_\lambda (T_c ) ,$$ whereB _λ denotes the Planck blackbody function at wavelength λ, χ specifies the fraction of the field of view occupied by the soil at a given view direction, and an emissivity of 1.0 is assumed for the plants and the soil. The dependence of the soil-fraction χ on the view direction and the structure is expressed by the viewing-geometry parameter, which allows for concise and simple formulation. We observe from our model that at large view zenith angles, only the plants are effectively seen (that is, χ tends to zero), and thereforeT _c can be determined from observations at large zenith angles, to the extent that such observations are practical. Viewing from the zenith, χ = exp(-L _hc), whereL _hc is the projection of the canopy leaf-area (per unit surface area) on a horizontal plane. For off-zenith observations, the soil-fraction χ depends on the distribution in the azimuth of the projected areas of various leaf categories, in addition to the dependence on the sum total of these projections,L _hc.L _hc, rather than the leaf-area index, emerges as the parameter characterizing the optical thickness of the canopy. Inferring bothT _c andT _g from observations from the zenith and from large zenith angles is possible ifL _hc is known from other measurements. Drooping of leaves under water-stress conditions affects the observed temperatureT _m in a complicated way because a leaf-inclination change produces a change inL _hc (for the same leaf area) and also a change in the dependence of χ on the view direction. Water stress can produce an increase of the soil-fraction χ and thus tends to produce an exaggerated increase in the observed temperature compared to the actual increase in canopy temperature. These effects are analyzed for a simulated soybean canopy.     

A Study of Linke Turbidity Factor over Qena / Egypt

Data on instantaneous atmospheric Linke turbidity factor TL (m) are reported for clear days at Qena/Egypt in the period from June 1992 to May 1993.TL(m) is determined using the values of irradiance of direct solar radiation (I),which are calculated from global (G) and diffuse (D) - solar radiation measurements.Monthly and seasonally variations of both diurnal and daily average values of TL (m) increases steadily in the direction of sunset in the months from June to December 1992 as well as Summer and Autumn seasons,while it falls generally in this direction for the months from January to March and Winter season.In April and May,TL (m) fluctuates obviously through the day hours,it is also shown that the average values of TL(m) are particularly large during Summer months compared to other months of the year.This behavior of TL(m) is discussed in view of the variations of some weather elements,which affect the content of water vapor and dust particle in the atmosphere of the study region.It seems t be of s     

Incorporation of planetary boundary layer dynamics in a numerical model of long-range air-pollutant transport

In this paper, measurements of the first 150 m of the atmospheric boundary layer obtained by a high-frequency acoustic mini-sounder are compared with measurements obtained by a full complement of instruments including sonic anemometers mounted on the Boulder Atmospheric Observatory tower. The acoustic mini-sounder, starting as low as 6 m from the ground, measures in the monostatic mode the profiles of the vertical wind speed, w, and of the temperature structure parameter, C _T ² with enhanced height resolution of the order of 1 m and time resolution of the order of 30 s. The results of the comparison show that the high-frequency mini-sounder is an effective atmospheric boundary-layer profiler that is also portable and relatively inexpensive. Measurements of the spectrum of C _T ² are presented that provide information on the local isotropy of the temperature field. Statistics of the variability of C _T ² in both stable and unstable conditions are also given. The sounder's capabilities are further demonstrated by some detailed observations of the structure and time evolution of a thermal plume root at noon and of a nocturnal, stably stratified layer in which a dynamic instability develops. The plume starts at a height of less than 5 m, possesses substantial internal structure, and includes vertical velocities in excess of 2 m s^-1.     

由非季风区向季风区过渡过程中大气边界层结构的变化分析

利用中国西北中部具有代表性的非季风区、夏季风影响过渡区和季风区的7个高空站的2013年夏季晴天07时、13时、19时（北京时）的大气边界层资料,通过分析大气边界层位温、比湿、风速的垂直结构,发现大气边界层结构及厚度在不同区域的分布特征:稳定边界层厚度、残余层顶高度和对流边界层厚度从非季风区、夏季风影响过渡区至季风区出现阶梯性大幅降低,从非季风区至夏季风影响过渡区,以及从夏季风影响过渡区至季风区,对流边界层厚度降幅依次为25.6%和81.8%,稳定边界层厚度降幅依次为58.3%和41.8%;在稳定边界层条件下,可观察到低空急流的存在,非季风区低空急流出现高度明显高于夏季风影响过渡区和季风区,且非季风区的低空急流风速也明显大于夏季风影响过渡区和季风区。通过分析与大气边界层发展最为密切的陆面热力因素在不同气候区的分布,净辐射值、日地-气温差最大值以及感热通量值在非季风区大于夏季风影响过渡区和季风区,从陆面热力过程为非季风区大气边界层厚度大于夏季风影响过渡区和季风区提供了理论依据。     

The characteristics of ultraviolet radiation in arid and semi-arid regions of China

Measurements of the broadband global solar radiation (R _S) and total ultraviolet radiation (the sum of UV-A and UV-B) were conducted from 2005 to 2010 at 9 sites in arid and semi-arid regions of China. These data were used to determine the temporal variability of UV and UV/R _S and their dependence on the water vapor content and clearness index. The dependence of UV/R _S on aerosol optical depth (AOD) and water vapor content was also investigated. In addition, a simple and efficient empirically model suited for all-weather conditions was developed to estimate UV from R _s. The annual average daily UV level in arid and semi-arid areas is 0.61 and 0.59 MJ m^?2 d^?1, respectively. The highest value (0.66?±?0.25 MJ m^?2 d^?1) was recorded at an arid area at Linze. The lowest value (0.53?±?0.22 MJ m^?2 d^?1) was recorded at a semi-arid area at Ansai. The highest daily value of UV radiation was measured in May, whereas the lowest value was measured in December. The monthly variation of the UV/R _s ratio ranged from 0.41 in Aksu to 0.35 in Qira. The monthly mean value of UV/R _s gradually increased from November and then decreased in August. A small decreasing trend of UV/R _s was observed in the arid and semi-arid regions due to recently increasing amounts of fine aerosol. A simple and efficient empirically model suit for all-weather condition was developed to estimate UV from R _s. The slope a and intercept b of the regression line between the estimated and measured values were close to 1 and zero, respectively. The relative error between the estimated and measured values was less than 11.5%. Application of the model to data collected from different locations in this region also resulted in reasonable estimates of UV.     

Evaluation of solar radiation estimation methods for reference evapotranspiration estimation in Canada

The accuracy of nine solar radiation (R _s ) estimation models and their effects on reference evapotranspiration (ET _o ) were evaluated using data from eight meteorological stations in Canada. The R _s estimation models were FAO recommended Angstrom-Prescott (A-P) coefficients, locally calibrated A-P coefficients, Hargreaves and Samani (H-S) (1982), Annandale et al., (2002), Allen (1995), Self-Calibrating (S-C, Allen, 1997), Samani (2000), Mahmood and Hubbard (M-H) (2002), and Bristow and Campbell (B-C) (1984). The estimated R _s values were then compared to measured R _s to check the appropriateness of these models at the study locations. Based on root mean square error (RMSE), mean bias error (MBE) and modelling efficiency (ME) ranking, calibrated A-P coefficients performed better than all other methods. The calibrated H-S method (using new K _RS 0.15) estimated R _s more accurately than FAO-56 recommended A-P in Elora, and Winnipeg. The RMSE of the calibrated H-S method ranged between 1-6% and the RMSE of the calibrated and FAO recommended Angstrom-Prescott (A-P) methods ranged between 1-9%. The models with the least accuracy at the eight locations are the Mahmood & Hubbard (2002) and Self-Calibrating models. The percent deviation in ET _o calculated with estimated R _s was reduced by about 50% as compared to deviation in measured versus estimated R _s .     

Observed changes in relative humidity and dew point temperature in coastal regions of Iran

The analysis of trends in hydroclimatic parameters and assessment of their statistical significance have recently received a great concern to clarify whether or not there is an obvious climate change. In the current study, parametric linear regression and nonparametric Mann?CKendall tests were applied for detecting annual and seasonal trends in the relative humidity (RH) and dew point temperature (T _dew) time series at ten coastal weather stations in Iran during 1966?C2005. The serial structure of the data was considered, and the significant serial correlations were eliminated using the trend-free pre-whitening method. The results showed that annual RH increased by 1.03 and 0.28?%/decade at the northern and southern coastal regions of the country, respectively, while annual T _dew increased by 0.29 and 0.15°C per decade at the northern and southern regions, respectively. The significant trends were frequent in the T _dew series, but they were observed only at 2 out of the 50 RH series. The results showed that the difference between the results of the parametric and nonparametric tests was small, although the parametric test detected larger significant trends in the RH and T _dew time series. Furthermore, the differences between the results of the trend tests were not related to the normality of the statistical distribution.