SCMOC温度精细化指导预报在陕西区域的质量检验

利用2012年陕西区域99站共366天北京时间08:00和20:00起报的SCMOC温度精细化指导预报与实况资料的比较,检验分析了定时温度、日最高气温和日最低气温的预报质量。结果表明:陕西区域SCMOC温度精细化指导预报08:00起报的准确率高于20:00起报的,且预报准确率有明显的季节变化,夏、秋季节较高,冬、春季节较低,日最高(低)气温的预报准确率与预报时效成反比。地形高度影响温度预报准确率,二者之间的相关系数通过了显著性检验。08:00起报的48h内逐3h气温多出现负误差,20:00起报的多出现正误差。08:00起报的日最高气温和20:00起报的日最高(低)气温多出现负误差,08:00起报的日最低气温多出现正误差。从对典型天气过程的温度预报质量检验来看,强冷空气影响下的降温天气过程的温度预报难度较大,预报准确率较其他天气类型偏低一些。     

Long-Term Trends in Extreme Temperatures in Hong Kong and Southern China

The observed long-term trends in extreme temperatures in Hong Kong were studied based on the meteorological data recorded at the Hong Kong Observatory Headquarters from 1885-2008. Results show that, over the past 124 years, the extreme daily minimum and maximum temperatures, as well as the length of the warm spell in Hong Kong, exhibit statistically significant long-term rising trends, while the length of the cold spell shows a statistically significant decreasing trend. The time-dependent return period analysis also indicated that the return period for daily minimum temperature at 4°C or lower lengthened considerably from 6 years in 1900 to over 150 years in 2000, while the return periods for daily maximum temperature reaching 35°C or above shortened drastically from 32 years in 1900 to 4.5 years in 2000. Past trends in extreme temperatures from selected weather stations in southern China from 1951-2004 were also assessed. Over 70% of the stations studied yielded a statistically significant rising trend in extreme daily minimum temperature, while the trend for extreme maximum temperatures was found to vary, with no significant trend established for the majority of stations.     

A MODIFIED WEATHER GENERATOR(WGEN)FOR THE GENERATION OF DAILY WEATHER VARIABLES

Long series of daily weather data are frequently needed to evaluate diachronic climatic impacton water resources,the effects of watershed changes on hydrology and to use in a variety ofweather and general circulation models.A computer generation model called WGEN that wasdeveloped in the United States of America,was modified in this study and applied to Nigerianweather data spanning the period between 1969 to 1988 and covering 17 sites located in thedifferent climatic zones in Nigeria.The model generated the monthly mean maximum and minimumtemperatures,solar radiation,total rainfall,and number of wet and dry days with high accuracy,95% of the times.The Mann-Whitney U-test revealed that the number of months per year forwhich observed and generated weather variables were significant,was less than 4 in majority of thesites.     

9414号热带气旋(DOUG)非对称结构和对降水影响的分析

ＤＯＵＧ热带气旋在靠近华东沿海大陆北上过程中，气旋西侧的降水强度显著减弱，主要是ＤＯＵＧ气旋的完全非对称结构造成的。本文从大尺度天气系统配置、卫星云图和中尺度数值模式计算结果分析了ＤＯＵＧ气旋内区和外区非对称结构的成因和演变。外区的非对称结构主要由环境场的大尺度天气系统的东高西低气压场配置造成的。气旋近中心中－β尺度的辐散下沉系统的发展是造成内区非对称结构的主要原因。二者的形成是相互独立的。但内区非对称结构的发展会促使外区非对称结构的加强。前者向后者逼近并结合造成了ＤＯＵＧ的完全非对称结构     

Statistical downscaling of summer temperature extremes in northern China

Two approaches of statistical downscaling were applied to indices of temperature extremes based on percentiles of daily maximum and minimum temperature observations at Beijing station in summer during 1960-2008. One was to downscale daily maximum and minimum temperatures by using EOF analysis and stepwise linear regression at first, then to calculate the indices of extremes; the other was to directly downscale the percentile-based indices by using seasonal large-scale temperature and geo-potential height records. The cross-validation results showed that the latter approach has a better performance than the former. Then, the latter approach was applied to 48 meteorological stations in northern China. The cross-validation results for all 48 stations showed close correlation between the percentile-based indices and the seasonal large-scale variables. Finally, future scenarios of indices of temperature extremes in northern China were projected by applying the statistical downscaling to Hadley Centre Coupled Model Version 3 (HadCM3) simulations under the Representative Concentration Pathways 4.5 (RCP 4.5) scenario of the Fifth Coupled Model Inter-comparison Project (CMIP5). The results showed that the 90th percentile of daily maximum temperatures will increase by about 1.5℃, and the 10th of daily minimum temperatures will increase by about 2℃ during the period 2011-35 relative to 1980-99.     

1978—2008年城市化对北京地区气温变化影响的初步分析

应用北京地区20个常规站1978-2008年经均一性序列多元分析方法均一化处理的气温数据,初步分析了北京地区城市化对年平均和不同季节日最高、最低以及平均气温的影响。结果表明,1978—2008年,年平均日最低、平均气温空间分布自北向南、自西向东,温度逐渐升高,在城区达到最高,日最高气温表现为从西向东南逐步升高,在城区形成较为明显的热岛。温度变化趋势表明,各站日最低气温、平均气温、最高气温均呈升温趋势。城市化对北京地区城区及近郊区站点日平均气温和最低气温影响最大,对自北部佛爷顶至昌平到城区一带站点的最高气温影响最大。城市化对北京(观象台)站的增温影响最为明显,对城区站点温度平均的增温影响次之,对全市站点温度平均的增温影响最小。城市化对观象台站、城区站点平均、全市站点平均日平均气温、最低气温的年平均、各季节均非常显著,其中在秋季影响最大,对日最高气温的影响则是在夏季最大。     

我国太阳日总辐射计算方法的研究

对全国23个站点的日照时数、日最高气温、日最低气温、太阳日总辐射量等气象要素实测资料进行统计分析,利用回归分析法建立了以日照百分率和气温日较差为主要相关因子的各地日总辐射估算模型。结果表明：除了高原站拉萨以外,推算模型的复相关系数均介于0．80～0．93之间,拟合效果较好。在春、夏季使用独立的季节模型有一定的必要性,该方法适用于我国各地太阳日总辐射的推算,     

Stochastic Modeling of the Effects of Large-Scale Circulation on Daily Weather in the Southeastern U.S.

Statistical methodology is devised to model time series of daily weather at individual locations in the southeastern U.S. conditional on patterns in large-scale atmosphere–ocean circulation. In this way, weather information on an appropriate temporal and spatial scale for input to crop–climate models can be generated, consistent with the relationship between circulation and temporally and/or spatially aggregated climate data (an exercise sometimes termed `downscaling'). The Bermuda High, a subtropical Atlantic circulation feature, is found to have the strongest contemporaneous correlation with seasonal mean temperature and total precipitation in the Southeast (in particular, stronger than for the El Niño–Southern Oscillation phenomenon). Stochastic models for time series of daily minimum and maximum temperature and precipitation amount are fitted conditional on an index indicating the average position of the Bermuda High. For precipitation, a multi-site approach involving a statistical technique known as `borrowing strength' is applied, constraining the relationship between daily precipitation and the Bermuda High index to be spatially the same. In winter (the time of greatest correlation), higher daily maximum and minimum temperature means and higher daily probability of occurrence of precipitation are found when there is an easterly shift in the average position of the Bermuda High. Methods for determining aggregative properties of these stochastic models for daily weather (e.g., variance and spatial correlation of seasonal total precipitation) are also described, so that their performance in representing low frequency variations can be readily evaluated.     

近46年来华东及周围地区最高与最低气温时空演变的气候特征

选用华东及周围地区157个气象观测站1960-2005年6~8月逐日最高、 最低气温资料, 利用分段线性突变检验、 EOF分解等方法, 分析了我国华东地区最高, 最低气温的空间分布及时间演变特征。结果表明： 高温主要出现在长江以南; 长江以北夏季气温分布受地形影响显著。最高气温分布具有良好的空间一致性, 主要受大尺度天气系统影响; 最低气温更多表现出局地变化特征; 华东夏季最高、 最低气温分布形态较为类似, 有“全区一致型”和“南北反向型”两种, 1980年是夏季气温的一个突变点。1980年以前, 最高、 最低气温都呈下降趋势; 1980年以后则呈升温趋势; 最高气温的演变以“高温-低温-高温”的年代际振荡为主, 极端高温主要发生在两个时段： 第一次在1960-1965年之间; 第二次在2000年以后, 而极端最低气温以升温趋势为主。     

Estimating missing daily temperature extremes in Jaffna,Sri Lanka

The accuracy of reconstructing missing daily temperature extremes in the Jaffna climatological station, situated in the northern part of the dry zone of Sri Lanka, is presented. The adopted method utilizes standard departures of daily maximum and minimum temperature values at four neighbouring stations, Mannar, Anuradhapura, Puttalam and Trincomalee to estimate the standard departures of daily maximum and minimum temperatures at the target station, Jaffna. The daily maximum and minimum temperatures from 1966 to 1980 (15 years) were used to test the validity of the method. The accuracy of the estimation is higher for daily maximum temperature compared to daily minimum temperature. About 95% of the estimated daily maximum temperatures are within ±1.5 °C of the observed values. For daily minimum temperature, the percentage is about 92. By calculating the standard deviation of the difference in estimated and observed values, we have shown that the error in estimating the daily maximum and minimum temperatures is ±0.7 and ±0.9 °C, respectively. To obtain the best accuracy when estimating the missing daily temperature extremes, it is important to include Mannar which is the nearest station to the target station, Jaffna. We conclude from the analysis that the method can be applied successfully to reconstruct the missing daily temperature extremes in Jaffna where no data is available due to frequent disruptions caused by civil unrests and hostilities in the region during the period, 1984 to 2000.     

Evaluation of machine learning tools as a statistical downscaling tool: temperatures projections for multi-stations for Thames River Basin, Canada

Many impact studies require climate change information at a finer resolution than that provided by global climate models (GCMs). This paper investigates the performances of existing state-of-the-art rule induction and tree algorithms, namely single conjunctive rule learner, decision table, M5 model tree, and REPTree, and explores the impact of climate change on maximum and minimum temperatures (i.e., predictands) of 14 meteorological stations in the Upper Thames River Basin, Ontario, Canada. The data used for evaluation were large-scale predictor variables, extracted from National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis dataset and the simulations from third generation Canadian coupled global climate model. Data for four grid points covering the study region were used for developing the downscaling model. M5 model tree algorithm was found to yield better performance among all other learning techniques explored in the present study. Hence, this technique was applied to project predictands generated from GCM using three scenarios (A1B, A2, and B1) for the periods (2046–2065 and 2081–2100). A simple multiplicative shift was used for correcting predictand values. The potential of the downscaling models in simulating predictands was evaluated, and downscaling results reveal that the proposed downscaling model can reproduce local daily predictands from large-scale weather variables. Trend of projected maximum and minimum temperatures was studied for historical as well as downscaled values using GCM and scenario uncertainty. There is likely an increasing trend for T _max and T _min for A1B, A2, and B1 scenarios while decreasing trend has been observed for B1 scenarios during 2081–2100.     

南宁各种下垫面温度特征及预报方法探讨

介绍下垫面温度观测的原理,并用南宁2002年12月至2003年11月资料,分析了沥青、水泥、泥土温度的特征,包括这3种下垫面的日平均温度、日最高温度、日最低温度、温度日较差特征以及它们与气温的差值。指出气温是影响各种下垫面温度的最主要因素,云对下垫面日最高温度影响显著,大气环流变换对各种下垫面温度的波动有明显影响。对各种下垫面日最高温度的预报方法进行了初步探讨。建立了基于日最高气温预报之上的各种下垫面日最高温度预报模型,并对预报与实况值进行了对比分析,指出提高次日最高气温的预报准确率和预报是否出现阴天有中到大雨天气的预报准确率,可显著改善各种下垫面日最高温度的预报效果。     

Stochastic precipitation generator with hidden state covariates

Time series of daily weather such as precipitation, minimum temperature and maximum temperature are commonly required for various fields. Stochastic weather generators constitute one of the techniques to produce synthetic daily weather. The recently introduced approach for stochastic weather generators is based on generalized linear modeling (GLM) with covariates to account for seasonality and teleconnections (e.g., with the El Niño). In general, stochastic weather generators tend to underestimate the observed interannual variance of seasonally aggregated variables. To reduce this overdispersion, we incorporated time series of seasonal dry/wet indicators in the GLM weather generator as covariates. These seasonal time series were local (or global) decodings obtained by a hidden Markov model of seasonal total precipitation and implemented in the weather generator. The proposed method is applied to time series of daily weather from Seoul, Korea and Pergamino, Argentina. This method provides a straightforward translation of the uncertainty of the seasonal forecast to the corresponding conditional daily weather statistics.     

基于数值模式误差分析的气温预报方法

采用欧洲中期天气预报中心(ECMWF)全球确定性预报模式地面气温和国家地面站点观测资料,对模式初值场误差、历史误差以及卡尔曼滤波预测误差与实况误差之间的相关性进行分析,设计了4种回归方案订正日最高、最低气温预报偏差,并与ECMWF、中央气象台和全国城镇的预报产品进行了检验对比。结果表明:采用了模式近1～3 d最高(最低)气温和模式最高(最低)气温历史平均误差、初值场误差以及卡尔曼滤波反演误差作为预报因子的改进方案效果最优,经对其2017年日最高和最低气温的预报检验,预报准确率较ECMWF原始模式预报有较明显提高,也明显优于中央气象台指导预报。在空间分布方面,其对地形较为复杂地区的改进效果更好。同时,与当前业务中质量最好的全国城镇预报相比,最高气温预报平均绝对偏差(Mean Absolute Error,MAE)较全国城镇预报低8.24%～13.97%,预报准确率提高1.24%～3.57%,日最低气温平均绝对偏差较城镇预报低9.43%～17.69%,预报准确率提高1.77%～2.72%。在3 d的预报中,对24 h时效内预报相对于48 h和72 h的改进幅度更大,订正效果更加明显。     

九华山区地形逆温对天气变化的前兆作用

九华山受山区下垫面影响,升温期间温度的垂直分布有时出现山上温度大于山下的异常现象,即地形逆温。通过山区不同高度上温度的垂直对比以及其日变化特征分析,表明：山区地形逆温现象总是在升温期间出现的,并以日最低气温要素垂直分布的异常特征最为明显（与日平均气温、最高气温比较）;最低气温出现的逆温具有明显季节性特征;山区日最低气温垂直分布出现的地形逆温对天气变化有很好的前兆作用：升温期间有地形逆温出现,消失后必会降温,降温期间必有降水和复杂天气出现;而且形成“地形逆温”的时间和幅度与后来的降温持续时间、降温幅度及天气都有一定的对应关系。因此,在山区复杂地形中分析气温的时空变化特征,对于气温变化的分区预报和天气转变的预测都有很好的前兆作用。     

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.     

Simulating Climate over Western North America Using Stochastic Weather Generators

The climatologies of daily precipitation and of maximum and minimum temperatures over western North America are simulated using stochastic weather generators. Two types of generator, differentiated only by their method of modeling precipitation occurrence, are investigated. A second-order Markov model, in which the probability of the occurrence of precipitation is modeled as contingent upon its occurrence on the previous two days, is compared with a spell-length model, in which mass functions of wet- and dry-spell lengths are modeled. Both models are able to reproduce the observed annual and monthly climatology in the region to a high degree of accuracy. However, there is considerable over-dispersion in annual precipitation, resulting primarily from an underestimation in the interannual variability of precipitation intensity. The interannual variability of temperatures is similarly underestimated, and is most severe for minimum temperatures. There is a severe problem in estimating minimum temperature extremes, which can be attributed to the negatively skewed distribution of daily minimum temperatures. Non-normality in the distribution of daily temperatures is shown to be a problem in simulating extreme temperature maxima as well as of minima. It is suggested that the normal distribution used in the generation of daily temperatures in the widely used Richardson (1981) generator, and its derivations, be supplanted by a more appropriate distribution that permits skewness in either direction.     

衡阳地区自动站与人工观测站气温对比分析

利用衡阳站、常宁站、南岳高山站2004—2006年自动与人工平行观测资料,运用相关分布、对比差值等分析方法,研究了自动站与人工站气温的差异性及变化特点。结果表明:自动气象站与人工站气温值的稳定性均很好,未出现大的系统性偏差。衡阳自动站以偏低为主,南岳高山和常宁自动站则以偏高为主。在气温较低的地区或时间段,自动站与人工站气温值相等出现几率更大。对比差值在逐时变化中表现出"单峰型"分布特征,最大值出现在11时,最小值出现在20时。影响对比差值大小变化的主要因素是气温的变化幅度,这种反应存在一定的滞后性,同时这种变化也是非对称性的,对比差上升增速要大于其下降的减速。衡阳站、南岳高山站气温对比差值,月平均误差范围小,达到《地面气象观测规范》要求。常宁自动气象站气温以偏高为主,温差变化大,特别在春、夏季部分月份表现更为明显。     

北京1841年以来均一化最高和最低气温日值序列的构建

长期连续的日值观测资料是研究百年来极端气候事件及其变化特征的重要基础支撑.目前中外由于缺乏可靠的逐日百年尺度气候资料,使得20世纪50年代以前的极端气候变化规律仍然没有得到很好的认识.基于国家气象信息中心收集整理的日最高和最低气温观测资料,构建北京1841—2019年气温日值序列.首先,通过数据质量控制剔除原始基础资料...     

南京极值温度长期变化及其与平均温度的关系

利用1951—2009年南京日平均气温、日最高气温以及日最低气温等资料,分析了南京日最高气温和最低气温的长期演变趋势及其与平均温度的关系。结果表明：近60 a来,南京年平均气温、年平均最高气温、年平均最低气温均呈变暖趋势,20世纪90年代增温尤为明显;日最高气温,除夏季表现为降温趋势外,其他季节均为升温趋势;而四季平均气温和平均最低气温均为增温趋势;夏季气温日较差下降趋势明显,导致夏季昼夜温差减小;极端高温、低温的发生日数均呈下降趋势。极端气温与平均气温之间存在明显的相关性,且极端低温对平均气温影响更为明显。