基于统计降尺度方法的夏季降水精细化预报

利用1999—2009年安徽省淮河以南地区60个县市站夏季逐日降水资料和安庆市探空站逐日资料,研究了中低层不同风向配置下局地降水与大尺度降水场之间的关系,以3种不同预报对象及相应的预报因子分别采用神经网络和线性回归方法设计6种预报模型对观测资料进行逼近和优化,从而实现空间降尺度.分析对比6种预报模型46站逐日降水量的拟合和预报效果,结果表明:采取相同的预报对象及预报因子的BP神经网络模型在拟合和预报效果上均好于线性回归模型,可见夏季降水场之间以非线性相关为主;神经网络模型预报结果同常用的Cressman插值预报相比,能很好地反映出降水的基本分布及局地特征;预报对象为单站降水序列的神经网络模型在以平原、河流为主要地形的区域预报效果较好,预报对象为REOF主成分的神经网络模型则在山地和丘陵地形区域预报效果较好.     

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.     

投影寻踪回归与BP神经网络方法在前汛期降水预测中的比较研究

以1962—2006年粤北地区7个站4—6月前汛期降水量资料为基础,将前汛期降水量与74项环流指数资料进行灰色关联度分析,确定了影响粤北地区前汛期降水量的16个关键环流指数因子,分别应用投影寻踪回归、BP神经网络和逐步回归方法,建立前汛期降水趋势预测模型,对粤北地区前汛期降水趋势进行预测。结果表明:投影寻踪回归和BP神经网络方法的预测能力均优于传统的逐步回归模型。其中,PPR模型比BP神经网络方法的预测效果更好。     

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.     

Statistical prediction of heavy rain in South Korea

1. Introduction In recent decades, extreme weather events seem to be growing in frequency and risk due to water-related disasters. According to the World Meteorological Or- ganization report (ISDR and WMO, 2004) on World Water Day, 22 March 2004, the economic losses caused by water-related disasters, including floods, droughts and tropical cyclones, are on an increasing trend as follows: the yearly mean in the 1970s was about 131 billion US dollars, 204 billion dollars in the 1980s, and …     

Variations and trends in annual and seasonal air temperatures in Greece determined from ground and satellite measurements

Summary The variations and trends in annual and seasonal air temperatures in Greece were examined on the basis of ground measurements for 25 stations during the period 1951–1993, and satellite measurements for the south eastern Mediterranean during the period 1979–1991. Data were smoothed using a 5-year running mean and were thereafter examined by regression analysis to define trends in the long duration time series. Data were also examined to detect abrupt changes and trends in the long duration annual, winter and summer series of mean maximum, mean minimum and mean temperatures. An overall cooling trend was detected for the majority of stations in winter over the entire period; the same cooling trend was also recognised for the annual and summer mean values, although a reverse warming trend was detected around the mid-70s at several stations. Satellite measurements indicate a slight warming trend, although this is not statistically significant. Considering the results of the regression analysis and the statistical tests applied to the 25 stations, it may be concluded that annual mean temperatures are dominated by an overall cooling trend, with the exception of stations in urban areas where urbanisation effects may have resulted in a warming trend. Summer temperatures, however, exhibit a warming trend roughly after 1975 at most stations.With 5 Figures     

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.     

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.     

TRMM月降水量产品在新疆地区的订正

利用1998-2013年TRMM月降水量产品与新疆同期的105个气象站地面观测降水量,运用逐步回归与BP神经网络方法,选取1998-2010年数据建立新疆地区的降水订正模型,并利用2011-2013年月降水量进行检验。结果表明:加入地形因子对TRMM月降水量产品订正效果明显,整体上两种模型对TRMM月降水量产品订正的相关系数从最初的0.66分别提高到0.75和0.80,相对误差由10.75%分别降低为4.88%和3.19%;月尺度上,TRMM月降水量产品相对误差为-5.68%~54.44%,经逐步回归模型订正后为-4.26%~32.57%,而BP神经网络模型订正后为-5.33%~24.48%,表明BP神经网络模型订正效果更好;从综合时间技巧评分S_T看,订正后TRMM月降水量产品在各月的效果均有不同程度提高,逐步回归模型订正后提高0.01~0.49,BP神经网络模型订正后提高0.03~0.70。因此,基于逐步回归模型与BP神经网络模型订正的TRMM降水量产品均能够准确、定量地再现降水分布,为TRMM降水量产品质量改进提供一种较实用的参考方法。     

利用FY-1D极轨气象卫星分裂窗区通道计算陆表温度

根据理论和经验上已证明的地表温度与AVHRR窗区通道4、5的亮度温度存在线性或非线性关系, 通过对2818条全球晴空大气廓线做不同比辐射率地表的FY-1D窗区通道4、5辐射率的模拟计算, 推导出FY-1D极轨气象卫星的红外通道4、5亮温与地表温度的二次回归关系式。同时详细介绍了由这一回归关系式和FY-1D高分辨率图像传输 (HRPT) 遥测数据计算陆表温度的方法, 最后给出陆表温度计算结果的精度:用中国地面气象台站的0 cm地温观测数据与相同时刻的分辨率为0.01°×0.01°经纬度的卫星陆表温度相对比, 两者非常吻合, 绝大部分台站|ΔT|＜3.0 K。     

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.     

基于支持向量机(SVM)方法的冬季温度预测

选取河南省5个代表站,分别代表河南省5个片,将气候预测中常用的74项环流特征量资料进行归一化处理,分别将其与5个代表站的冬季温度进行相关普查,在筛选预测因子的基础上,利用SVM两类分类和回归方法,建立河南各代表站冬季温度预测推理模型,用2000/2001~2004/2005年4年进行试报,结果显示SVM方法是处理非线性分类和回归等问题的有效方法,做分类和回归预测时,各代表站对应的SVM推理模型均具有良好的预报能力,且对温度预测SVM回归优于SVM分类。     

统计降尺度法对华北地区未来区域气温变化情景的预估

迄今为止,大部分海气耦合气候模式（AOGCM）的空间分辨率还较低,很难对区域尺度的气候变化情景做合理的预测。降尺度法已广泛用于弥补AOGCM在这方面的不足。作者采用统计降尺度方法对1月和7月华北地区49个气象观测站的未来月平均温度变化情景进行预估。采用的统计降尺度方法是主分量分析与逐步回归分析相结合的多元线性回归模型。首先,采用1961～2000年的 NCEP再分析资料和49个台站的观测资料建立月平均温度的统计降尺度模型,然后把建立的统计降尺度模型应用于HadCM3 SRES A2 和 B2 两种排放情景, 从而生成各个台站1950～2099年1月份和7月份温度变化情景。结果表明:在当前气候条件下,无论1月还是7月,统计降尺度方法模拟的温度与观测的温度有很好的一致性,而且在大多数台站,统计降尺度模拟气温与观测值相比略微偏低。对于未来气候情景的预估方面,无论1月还是7月,也无论是HadCM3 SRES A2 还是B2排放情景驱动统计模型,结果表明大多数的站点都存在温度的明显上升趋势,同时7月的上升趋势与1月相比偏低。     

近30年江苏地区气候要素相关性变化分析

利用江苏省24个台站的1981-2012年的气温、湿度和降水的月平均观测资料,分别计算了每对台站之间的3个气候要素的结构关系和相关函数,用曲线回归分析了结构函数与台站距离的关系,用线性回归分析了相关函数与台站距离的关系,分析与评估江苏省气象台站网密度.通过分析结果发现,各个气象要素的结构函数和相关函数在江苏整个地区不满足各项同性和均匀性,江苏地区的气象台站网设计要根据气候要素进行分区设计.     

基于遗传优化BP神经网络的水稻气象产量预报模型

利用1951—2010年江苏省水稻产量及同期14个气象站点的逐日平均气温、降水资料,采用因子膨化及相关分析,研究了水稻气象产量的影响因子及影响时段。在此基础上建立了逐步回归、PCA-BP神经网络以及PCA-GA-BP神经网络3种产量预报模型。结果表明:(1)7—9月份是水稻产量形成的关键时期,对气温、降水的变化最为敏感,气温对气象产量的影响大于降水;(2)两种神经网络模型预报效果好于回归模型;(3)遗传优化的神经网络模型比未优化模型的训练速度提高了70%左右,预报精度也提高了4.3%。     

统计降尺度方法在青海省冬季最低温度预测中的应用

利用2003-2007年国家气象中心T213L31全球中期数值预报模式逐日输出产品与青海地区25个气象站的观测数据作为试验资料, 利用相关系数和逐步回归进行因子选择, 并以单隐层神经网络和多元回归作为降尺度方法进行对比研究, 用2003-2006年间的11月1日～次年3月1日的资料作为训练样本, 以数值预报产品和前一日观测的最低温度作为因子, 建立青海省25个气候站的冬季最低温度的24, 48, 72 h预报模型, 并且以2006年12月和2007年的1、 2月作为24, 48, 72 h逐日最低温度预报试验时段。试验表明, 对于青海地区来说, 青海北部地区的预报命中率总体好于南部高原地区; 在4种对比方案中, 以选择数值预报资料结合前一日地面观测的最低温度作为主要因子的方法相对较优, 随着预报时效的延长, 24 h历史实况的作用逐渐减弱; 对于所有台站来说, 这4种方案各有优缺点, 没有一种方案可以完全代替其他所有方案; 在实际业务运行中, 对不同的台站应采用不同的预报方案进行实际业务预报。     

北方公路交通气象环境识别及安全管理策略研究

用沈山高速公路沿线10个交通气象站的逐10 min气温、能见度、路面温度、降水、湿度和风等资料,采用多元逐步线性回归、最小二乘曲线拟合、MLP神经网络建立数学模型对北方公路路面温度、能见度以及冰雪路面等交通气象环境进行识别。通过抽取随机样本对模型进行检验,结果表明:所建立的模型对交通气象环境具有较好的模拟和识别作用。基于交通工程理论建立的不同路况条件下车速预估和交通安全指数模型对公路交通安全管理具有很好的应用价值。     

随机森林回归法在冬季路面温度预报中的应用

基于宁宿徐高速公路三个交通气象站2015—2018年冬季逐10 min实时观测资料,使用随机森林回归模型预报这三个站的未来1h冬季路面温度,分析了该模型在冬季路面温度预报中的可行性和适用性.研究结果表明:随机森林回归法可以被用来预报高速公路冬季路面温度,不同类型的交通气象站点的特征输入方案和参数调试标准存在差异;与简单...     

The coupling of cloud base height and surface fluxes: a transferability intercomparison

This paper presents an evaluation of the simulated coupling between cloud base height (CBH) and surface fluxes over selected Coordinated Enhanced Observing Period (CEOP) reference stations by five regional climate models as part of a transferability intercomparison experiment. The model results are compared with station data obtained during the first phase of the CEOP measuring campaigns. The models gave a credible simulation of both diurnal and seasonal cycles of cloud base height and surface variables over the stations. However, the models exhibited some difficulty in reproducing the diurnal and seasonal temperatures over the tropical stations. The study used principal component analysis to show that three factors account for most of the variability in the observed and simulated data and to investigate the coupling between cloud base height and surface fluxes in the data. In the observations, CBH is well coupled with the surface fluxes over Cabauw, Bondville, Lamont, and Berms, but coupled only with temperature over Lindenberg and Tongyu. All models but GEMLAM simulate substantial coupling between CBH and surface fluxes at all stations; GEMLAM does not couple CBH with surface fluxes, but with surface temperature and specific humidity.     

Changes in temperature and temperature gradients in the French Northern Alps during the last century

In mountain environments, local factors such as topography or exposure to the sun influence the spatial distribution of temperatures. It is therefore difficult to characterise the global evolution of temperatures over several decades. Such local effects can either accentuate or attenuate thermal contrasts between neighbouring areas. The present study uses two regional thermal indicators—thermal gradients and temperatures reduced to sea level—to monitor the monthly evolution of minimum and maximum temperatures in the French Northern Alps. Measures were calculated for the period extending from 1960 to 2007 based on data from 92 measuring stations. Temperature gradients were computed and further used to monitor the altitudinal evolution of temperatures. A characteristic regional temperature was determined for the whole of the French Northern Alps based on temperatures reduced to sea level, and changes in temperatures since 1960 were assessed. Multiple linear regression models made it possible to extend measurements over a longer period and to make enhanced calculations of temperature changes in the mountains since 1885. This is the first study to examine temperature changes in the French Northern Alps over such an extended period. Gradient data suggest that over the last 50 years, temperatures have changed at all altitudes. In addition, the evaluation of the temperature rise over 100 years reveals that minimal and maximal monthly temperatures trends are only significant a few months of the year.