锦州地区逐日降水概率分布特征

利用锦州地区的逐日降水量观测资料对逐日降水量的概率分布进行了统计分析,采用最大似然估计法得到Gamma函数分布的形状参数α和尺度参数β,通过Gamma概率分布模拟观测站点逐日降水的概率分布。结果表明：锦州地区逐日降水频率整体趋势先上升后下降,基本呈对称式分布,降水概率有一定的振荡,个别日会出现远超相邻日期的降水频率,7月21日降水频率最高,在不计微量降水的情况下,最低逐日降水概率有多个日期为0。各季降水频率偏低是造成义县地区干旱的原因之一;北镇夏季平均降水频率最低,但其夏季平均降水量却为锦州地区最高,说明北镇可能易出现较大量级降水或易出现极端降水天气。清明期间降水频率在50%以上、高考期间降水频率在80%以上,符合大众日常对特殊日期降水情况的认知;逐日降水频率可以为公众气象服务提供新的思路。凌海、北镇更容易出现极端降水天气;锦州地区日降水出现小雨天气概率最高,暴雨以上降水概率较低,锦州地区各站极少出现大暴雨以上量级降水,对锦州降水量级预报,尤其是暴雨或大暴雨以上降水量级的预报起到一定的指示作用。     

中国天气发生器非降水变量模拟参数分布特征

对基于马尔可夫链的理查森型中国天气发生器降水模拟已经有过比较系统的研究,但对非降水变量的模拟及其参数的分布特征等的研究还有待进一步深入。文中根据中国669个站点1971—2000年的逐日降水、最高气温、最低气温和日照时数资料,分干、湿两种状态计算了中国天气发生器各非降水变量的模拟参数——干、湿日条件下平均值和标准差的傅立叶系数以及各变量残差序列之间当天和后延一天的自相关、互相关系数,并分析了这些模拟参数在中国的空间分布规律,为中国天气发生器的进一步推广应用以及模拟参数的空间插值提供了技术支撑。     

中国天气发生器模拟非降水变量的效果评估

文中介绍了天气发生器BCC/RCG-WG基于谐波分析和多变量平稳过程对最高气温、最低气温、日照时数、相对湿度和平均风速等5个非降水变量的模拟模型,并根据中国669个站点1971-2000年的逐日气候资料(降水、最高气温,最低气温、日照时数、相对湿度和平均风速)计算了各站点的非降水变量的模拟参数.根据计算的模拟参数对中国...     

陕西省年、月降水量的理论频数分布

本文对陕西省年、月降水量频数进行了统计检验。检验结果表明:(1)年降水量遵从正态分布;(2)月降水量在做了立方根变换后,有83％的站月遵从正态分布;(3)月降水量中有58％的站遵从γ(Gamma)分布;(4)建议在估算月降水概率时,采用正态分布模型。     

雨滴谱分布函数的选择:M-P和Gamma分布的对比研究

M-P分布和Gamma分布是常用的两种雨滴谱分布函数。利用PMS GBPP-100型雨滴谱仪2003年7-8月在沈阳观测的雨滴谱资料,采用阶矩法对两种分布函数进行拟合,对比分析两种分布函数对谱及数浓度、雨强和雷达反射率因子的拟合效果。结果表明,在降水较弱、小滴偏少时Gamma分布会低估小滴,而M-P分布会高估小滴;降水强时,两种分布均低估小滴。M-P和Gamma分布对数浓度、雨强和雷达反射率因子这些特征量的拟合效果,在降水较强时差异很小,在降水较弱时差异较大。Gamma分布的代表性更好。此外,还讨论了两种分布的参数和雨强的关系。     

对流性降水雨滴谱特征及其与雷达反射率因子的对比分析

利用OTT Parsivel激光降水粒子谱仪观测的南京地区梅雨季节对流性降水过程的雨滴谱资料和江苏省气象台龙王山雷达观测资料,结合天气形势,对梅雨季节对流性降水过程的微物理参量、平均雨滴谱和速度谱分布特征进行分析;在对平均谱拟合时发现,Gamma分布对小滴数目的估计和大滴端形状的符合程度效果好于M-P分布和对数正态分布,并且拟合了Gamma分布参数μ和λ的二次项关系;建立了雷达反射率因子与雨强的相关关系,并将Parsive激光降水粒子谱仪观测计算的回波强度与雷达观测的回波强度作以比较,结果表明:对于此次暴雨过程,雷达观测到的回波强度有低估的现象,并且Parsivel粒子激光探测仪观测计算的回波强度越大,雷达低估的现象越为明显。回波修正后,用统计的Z-I关系式估算的降水量与Parsivel测得的降水量更为接近。     

基于自组织映射神经网络的吉林省春夏期降水统计模拟研究

利用1997—2015年吉林省春夏期(4—7月)逐日气象站地面观测资料,以气温、气压、相对湿度、水汽压、风速为协变量,建立各站点逐日降水量的基于自组织映射神经网络(Self-Organizing Maps,SOM)的统计预测模型;分析吉林省春夏期的主要天气模态,研究逐日降水和天气模态之间的关系,并基于此关系提出逐日降水量的蒙特卡罗模拟方法。结果表明:SOM对天气模态的分型质量较好,邻近天气模态的累积概率分布较相似,距离较远的天气模态累计概率分布差异较大。各天气模态下无降水的概率与日降水量区间宽度的相关系数为-0. 94,显著性水平小于0. 01。基于降水量累积概率分布,20种天气模态被划分成4类,并与降水易发程度和逐日降水量完全对应。在此基础上,对吉林省24个站点逐日降水量进行蒙特卡罗模拟,并进行预测性能分析。平均绝对误差(Mean Absolute Error,MAE)和均方根误差(Root Mean Square Error,RM SE)的中位数分别为3. 12 mm和6. 13 mm,SBrier和Ssig分别为0. 06和0. 51,站点的逐日降水量预测性能整体较好。MAE和RMSE分布呈现东南大西北小,去除降水自然变异差异的影响,所有站点的误差都较小; SBrier和Ssig没有明显的空间分布特征。     

CLIGEN天气发生器模拟沈阳地区降水的适用性评价

利用1981—2015年沈阳地区7个气象站的日观测数据,通过CLIGEN(Climate Generater)天气发生器模拟沈阳地区日降水序列数据,并统计模拟日降水量、月降水量、年降水量及年最大日降水量,利用平均值、标准差、偏度及峰度对CLIGEN天气发生器模拟的沈阳地区降水进行适用性评价。结果表明:CLIGEN天气发生器对沈阳地区日降水量、月降水量和年降水量平均值的模拟效果较好,模拟降水量的平均相对误差绝对值(Mean Absolute Relative Error,MARE)分别为2.1%、1.3%和3.3%,年最大日降水量的模拟精度稍差。对于降水最大值方面,CLIGEN天气发生器对沈阳地区日最大降水量和年最大降水量的模拟效果较差,模拟的日最大降水量和年最大降水量相对误差的最大值分别为-27.2%、18.3%。GLIGEN天气发生器能较好地模拟沈阳地区的月降水量和年降水量,t检验、F检验和K-S检验均表明,模拟的日最大降水量与年最大日降水量仅康平站达到极显著水平。从总体模拟效果来看,CLIGEN天气发生器能较好地模拟沈阳地区平均降水的统计特征。     

起伏地形下重庆降水精细的空间分布

采用重庆地区34个气象观测站1971—2000年30 a平均月降水总量资料,以及重庆地区100 m×100 m DEM(D igital E levation Model)数据,对重庆地区降水空间分布进行研究。根据山地气候学原理,利用GIS(Geographical Information Systems)软件,分析降水空间分布的影响因子,建立平均月降水量空间估算模型,计算了平均月降水量的空间分布。结果表明:随着海拔高度的增加,降水量逐渐增加;各月降水量的最大值出现在东北山区;降水量的季节变化明显。     

广州市单日降水量极值特征、分布拟合与推算

选取最优概率分布函数有助于提高气象要素重现期极值计算的可靠性。基于广州气象站1908—2016年逐日降水资料,构建年最大日降水量序列,采用线性趋势分析方法,研究了广州市年最大日降水量的变化特征,选取皮尔逊-Ⅲ型、对数正态、指数和耿贝尔-Ⅰ分布4种分布函数拟合广州市年最大日降水量序列,并按ω2检验、似然比检验等方法进行拟合优度检验。结果表明,近56年来,广州市年最大日降水量呈不显著的增加趋势。4—9月日最大降水量出现次数较多,6个月的出现次数占全年的93. 6%,其中,前汛期出现次数大于后汛期的。对数正态分布确定为广州市年最大日降水量拟合最优分布函数。对数正态分布估算的广州市50 a一遇的年最大日降水量是240. 1 mm,100 a一遇的是266. 1 mm,150 a一遇估算的是281. 4 mm。观测资料表明,广州平均1. 8 a出现一次150 mm以上的日降水量,而该降水量的估算重现期是1. 9 a,相当吻合。     

广义线性统计降尺度方法模拟日降水量的应用研究

利用1960—2010年青藏高原23个台站和长江下游25个台站的日降水量观测资料及NCEP再分析资料,采用广义线性模型的统计降尺度方法模拟台站日降水量,并评估了广义线性模型对日降水量的模拟能力。在建模期(1960—2005年)广义线性模型对日降水量表现出良好的模拟能力,两区域模拟结果与观测值1月平均相关系数0.75左右,7月也均超过0.5。模拟结果大部分台站日降水偏大,但偏大的量值较小;模拟的无降水准确率较高,最高值在高原区域,1月平均达85.2%。检验期(2006—2010年)广义线性模型模拟的日降水与建模期具有较好的一致性。此外,对两区域代表站的分析显示,广义线性模型模拟降水极值和降水0值的效果较好,且较好地还原了主要降水过程。总之,广义线性模型对日降水量的降尺度效果良好,适合应用于气候领域的相关研究。     

A Comparison of Four Precipitation Distribution Models Used in Daily Stochastic Models

Stochastic weather generators are statistical models that produce random numbers that resemble the observed weather data on which they have been fitted; they are widely used in meteorological and hydrologi- cal simulations. For modeling daily precipitation in weather generators, first-order Markov chain-dependent exponential, gamma, mixed-exponential, and lognormal distributions can be used. To examine the perfor- mance of these four distributions for precipitation simulation, they were fitted to observed data collected at 10 stations in the watershed of Yishu River. The parameters of these models were estimated using a maximum-likelihood technique performed using genetic algorithms. Parameters for each calendar month and the Fourier series describing parameters for the whole year were estimated separately. Bayesian infor- mation criterion, simulated monthly mean, maximum daily value, and variance were tested and compared to evaluate the fitness and performance of these models. The results indicate that the lognormal and mixed-exponential distributions give smaller BICs, but their stochastic simulations have overestimation and underestimation respectively, while the gamma and exponential distributions give larger BICs, but their stochastic simulations produced monthly mean precipitation very well. When these distributions were fitted using Fourier series, they all underestimated the above statistics for the months of June, July and August.     

Statistical downscaling of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River, China

A statistical downscaling method (SDSM) was evaluated by simultaneously downscaling air temperature, evaporation, and precipitation in Haihe River basin, China. The data used for evaluation were large-scale atmospheric data encompassing daily NCEP/NCAR reanalysis data and the daily mean climate model results for scenarios A2 and B2 of the HadCM3 model. Selected as climate variables for downscaling were measured daily mean air temperature, pan evaporation, and precipitation data (1961–2000) from 11 weather stations in the Haihe River basin. The results obtained from SDSM showed that: (1) the pattern of change in and numerical values of the climate variables can be reasonably simulated, with the coefficients of determination between observed and downscaled mean temperature, pan evaporation, and precipitation being 99%, 93%, and 73%, respectively; (2) systematic errors existed in simulating extreme events, but the results were acceptable for practical applications; and (3) the mean air temperature would increase by about 0.7°C during 2011~2040; the total annual precipitation would decrease by about 7% in A2 scenario but increase by about 4% in B2 scenario; and there were no apparent changes in pan evaporation. It was concluded that in the next 30 years, climate would be warmer and drier, extreme events could be more intense, and autumn might be the most distinct season among all the changes.     

Coupling statistical and dynamical methods for spatial downscaling of precipitation

The resolution of General Circulation Models (GCMs) is too coarse for climate change impact studies at the catchment or site-specific scales. To overcome this problem, both dynamical and statistical downscaling methods have been developed. Each downscaling method has its advantages and drawbacks, which have been described in great detail in the literature. This paper evaluates the improvement in statistical downscaling (SD) predictive power when using predictors from a Regional Climate Model (RCM) over a GCM for downscaling site-specific precipitation. Our approach uses mixed downscaling, combining both dynamic and statistical methods. Precipitation, a critical element of hydrology studies that is also much more difficult to downscale than temperature, is the only variable evaluated in this study. The SD method selected here uses a stepwise linear regression approach for precipitation quantity and occurrence (similar to the well-known Statistical Downscaling Model (SDSM) and called SDSM-like herein). In addition, a discriminant analysis (DA) was tested to generate precipitation occurrence, and a weather typing approach was used to derive statistical relationships based on weather types, and not only on a seasonal basis as is usually done. The existing data record was separated into a calibration and validation periods. To compare the relative efficiency of the SD approaches, relationships were derived at the same sites using the same predictors at a 300km scale (the National Center for Environmental Prediction (NCEP) reanalysis) and at a 45km scale with data from the limited-area Canadian Regional Climate Model (CRCM) driven by NCEP data at its boundaries. Predictably, using CRCM variables as predictors rather than NCEP data resulted in a much-improved explained variance for precipitation, although it was always less than 50?% overall. For precipitation occurrence, the SDSM-like model slightly overestimated the frequencies of wet and dry periods, while these were well-replicated by the DA-based model. Both the SDSM-like and DA-based models reproduced the percentage of wet days, but the wet and dry statuses for each day were poorly downscaled by both approaches. Overall, precipitation occurrence downscaled by the DA-based model was much better than that predicted by the SDSM-like model. Despite the added complexity, the weather typing approach was not better at downscaling precipitation than approaches without classification. Overall, despite significant improvements in precipitation occurrence prediction by the DA scheme, and even going to finer scales predictors, the SD approach tested here still explained less than 50?% of the total precipitation variance. While going to even smaller scale predictors (10–15?km) might improve results even more, such smaller scales would basically transform the direct outputs of climate models into impact models, thus negating the need for statistical downscaling approaches.     

Application of physical scaling towards downscaling climate model precipitation data

Physical scaling (SP) method downscales climate model data to local or regional scales taking into consideration physical characteristics of the area under analysis. In this study, multiple SP method based models are tested for their effectiveness towards downscaling North American regional reanalysis (NARR) daily precipitation data. Model performance is compared with two state-of-the-art downscaling methods: statistical downscaling model (SDSM) and generalized linear modeling (GLM). The downscaled precipitation is evaluated with reference to recorded precipitation at 57 gauging stations located within the study region. The spatial and temporal robustness of the downscaling methods is evaluated using seven precipitation based indices. Results indicate that SP method-based models perform best in downscaling precipitation followed by GLM, followed by the SDSM model. Best performing models are thereafter used to downscale future precipitations made by three global circulation models (GCMs) following two emission scenarios: representative concentration pathway (RCP) 2.6 and RCP 8.5 over the twenty-first century. The downscaled future precipitation projections indicate an increase in mean and maximum precipitation intensity as well as a decrease in the total number of dry days. Further an increase in the frequency of short (1-day), moderately long (2–4 day), and long (more than 5-day) precipitation events is projected.     

Impacts of the spectral nudging technique on simulation of the East Asian summer monsoon

This study assesses the performance of spectral nudging methodology in dynamical regional climate downscaling for summer climate over East Asia. The regional climate model NCAR-MM5v3 was used to dynamically downscale the 2.5-degree NCEP/NCAR reanalysis (NNRP) data onto 50-km regional grids. The main focus is the model’s simulation of precipitation. The NCEP/CPC precipitation analysis data were used as the verification. Boreal summers (June, July, and August) in 1991, 1998, and 2003 and heavy floods that occurred in Eastern China were selected for the study. Compared to the control runs (CTLs) without spectral nudging (SN), experiments with SNs greatly reduced systematic errors in upper-level large-scale circulations and were in better agreement with the NNRP. At the same time, SNs outperformed CTLs in simulating model variables near the surface. In comparison with observational precipitation data, spectral nudging also improved the model’s simulation of precipitation in spatial and temporal distributions. SN-simulated precipitation field patterns, including the spatial distribution of monthly mean precipitation band, the seasonal march of major precipitation bands, and the daily variability of regional-averaged time series, show much more consistency with observations than those of the CTL runs.     

Downscaling GCMs Using the Smooth Support Vector Machine Method to Predict Daily Precipitation in the Hanjiang Basin

General circulation models (GCMs) are often used in assessing the impact of climate change at global and continental scales. However, the climatic factors simulated by GCMs are inconsistent at comparatively smaller scales, such as individual river basins. In this study, a statistical downscaling approach based on the Smooth Support Vector Machine (SSVM) method was constructed to predict daily precipitation of the changed climate in the Hanjiang Basin. NCEP/NCAR reanalysis data were used to establish the sta...     

Downscaling extreme month-long anomalies in southern South America

We investigate the performance of one stretched-grid atmospheric global model, five different regional climate models and a statistical downscaling technique in simulating 3 months (January 1971, November 1986, July 1996) characterized by anomalous climate conditions in the southern La Plata Basin. Models were driven by reanalysis (ERA-40). The analysis has emphasized on the simulation of the precipitation over land and has provided a quantification of the biases of and scatter between the different regional simulations. Most but not all dynamical models underpredict precipitation amounts in south eastern South America during the three periods. Results suggest that models have regime dependence, performing better for some conditions than others. The models’ ensemble and the statistical technique succeed in reproducing the overall observed frequency of daily precipitation for all periods. But most models tend to underestimate the frequency of dry days and overestimate the amount of light rainfall days. The number of events with strong or heavy precipitation tends to be under simulated by the models.     

WRF模式对中国夏季降水的动力降尺度模拟研究

采用NCEP的FNL再分析资料驱动WRF模式,对中国10 a(2000—2009年)夏季降水进行双重动力降尺度(双重嵌套)模拟,将子、母区域模拟结果和观测进行对比,以检验双重动力降尺度对中国夏季降水模拟的"增值"能力。结果表明:单重动力降尺度(单重嵌套)方法能较好模拟出中国10 a夏季平均降水的空间分布,对季风雨带"北跳"特征模拟较好,但模拟降水具有系统性正偏差。在母区域的强迫下,双重动力降尺度模拟的降水分布与单重动力降尺度相比,没有发生根本性变化。但由于子区域的分辨率要高于母区域,双重动力降尺度比单重动力降尺度能提供更多有价值的降水细节。双重动力降尺度的这种"增值"能力存在地域依赖性,在华南地区和江淮地区,双重动力降尺度模拟出的降水分布、量值和逐日演变都要好于单重动力降尺度。但在华北地区,双重动力降尺度没有表现出明显的"增值"。     

Uncertainty analysis of statistical downscaling models using Hadley Centre Coupled Model

Regression-based statistical downscaling is a method broadly used to resolve the coarse spatial resolution of general circulation models. Nevertheless, the assessment of uncertainties linked with climatic variables is essential to climate impact studies. This study presents a procedure to characterize the uncertainty in regression-based statistical downscaling of daily precipitation and temperature over a highly vulnerable area (semiarid catchment) in the west of Iran, based on two downscaling models: a statistical downscaling model (SDSM) and an artificial neural network (ANN) model. Biases in mean, variance, and wet/dry spells are estimated for downscaled data using vigorous statistical tests for 30 years of observed and downscaled daily precipitation and temperature data taken from the National Center for Environmental Prediction reanalysis predictors for the years of 1961 to 1990. In the case of daily temperature, uncertainty is estimated by comparing monthly mean and variance of downscaled and observed daily data at a 95 % confidence level. In daily precipitation, downscaling uncertainties were evaluated from comparing monthly mean dry and wet spell lengths and their confidence intervals, cumulative frequency distributions of monthly mean of daily precipitation, and the distributions of monthly wet and dry days for observed and modeled daily precipitation. Results showed that uncertainty in downscaled precipitation is high, but simulation of daily temperature can reproduce extreme events accurately. Finally, this study shows that the SDSM is the most proficient model at reproducing various statistical characteristics of observed data at a 95 % confidence level, while the ANN model is the least capable in this respect. This study attempts to test uncertainties of regression-based statistical downscaling techniques in a semiarid area and therefore contributes to an improvement of the quality of predictions of climate change impact assessment in regions of this type.