我国东部极端降水时空分布及其概率特征

利用我国105°E以东地区210个测站近50年(1953—2002年)逐日降水资料,在REOF客观分区的基础上,确定各分区的极端降水最佳采样期为1~2日。进而研究了日极端降水量的气候特征。采用具有优良特性的L-矩参数估计方法对我国东部极端降水拟合Gumbel分布。结果表明,L-矩参数估计方法的拟合优度比其它方法有进一步提高,近50年来,极端降水趋势虽无明显变化,但其时空差异较大。符合Gumbel分布的极端降水重现期的地理空间分布,大致特征是,东南大、西北小,两湖盆地、黄海海湾及辽东半岛也有高值区。     

环渤海地区极端降水事件概率分布特征

利用环渤海地区60个测站1961～2008年逐日降水资料,采用L-矩参数估计方法对各站超临界值日极端降水拟合广义帕雷托分布。结果表明,广义帕雷托分布能够很好地拟合环渤海地区日极端降水的概率分布,其尺度参数、形状参数与极端降水重现期的地理空间分布密切相关,重现期50、100年一遇的日极端降水空间分布基本一致,山东南部、河北西南部、辽宁西部与河北东北部等区域具有较高极端降水值。     

中国南方夏半年湿期概率特征及其极值风险分析

以中国南方诸代表站近40 a(1965—2004年)夏季(5—9月)逐日降水资料为研究对象,探讨了夏半年各站湿期游程及其极端值的概率分布最佳模式。在对各站湿期游程分别验证指数分布的基础上,作耿贝尔(Gumbel)极值分布和广义帕雷托分布(GPD)拟合,进而对两者的拟合效果进行比较。并由此对湿期长度估计其不同重现期(如20 a一遇、50 a一遇和100 a一遇)的极端湿期长度的分位数概率。经K-S方法的统计检验,证明GPD分布拟合效果较好,能更加精确的模拟出中国南方夏季的极端连雨日数及其概率。     

山西省主汛期极端变化降水特征

利用环渤海地区60个测站1961～2008年逐日降水资料,采用L矩参数估计方法对各站超临界值日极端降水拟合广义帕雷托分布。结果表明,广义帕雷托分布能够很好地拟合环渤海地区日极端降水的概率分布,其尺度参数、形状参数与极端降水重现期的地理空间分布密切相关,重现期50、100年一遇的日极端降水空间分布基本一致,山东南部、河北西南部、辽宁西部与河北东北部等区域具有较高极端降水值。     

广义帕雷托分布在重庆暴雨强降水研究中的应用

引进广义帕雷托分布(GPD),借助于现代L-矩估计方法,模拟重庆地区极端降水事件,推算一定重现期的极端降水量分位数。模拟试验表明,基于超门限峰值法(POT)的GPD不但计算简便,而且基本不受原始序列样本量的影响,具有全部取值域的高精度稳定拟合(包括高端厚尾部),与GEV模拟结果相比,GPD具有更高精度和稳定性,更为实用。     

鹤岗强降水的危险性评估

<正>1引言随着全球变暖的进一步加剧,在很多方面将导致某些极端天气、气候事件更加频发,由此产生的灾害损失也必然会越来越严重。近年来,鹤岗地区极端天气灾害频繁发生,2009年,鹤岗地区出现了春末夏初百年一遇的大旱。而2005年7月28-29日鹤岗暴雨降水量达116.7 mm,导致鹤岗出现严重的洪涝灾害,梧桐河流域超过警戒水位,达到鹤岗建站以来极值。本文利用GPD模式,借助L-矩估计方法对模型     

浙江省极端降水事件分布及其概率特征

以浙江省内时间序列较长,空间分布较为均匀的24个站的降水资料为基础,应用边缘分布函数来确定极端降水的阈值,进而分析能较好表征极端降水事件的各类极端降水指数的发生发展规律和时空分布特征。结果表明,极端降水事件在浙西北地区发生更为频繁,浙南地区发生日数较少,而极端降水平均强度的高值分布在浙江南部,低值位于东北部,极端降水日数各区域均表现出增多趋势,同时其分布也有一定的时间周期规律可循;极端降水平均强度除了浙中西部地区外也表现出不同程度的增强趋势。采用L-矩参数估计方法,应用Gumbel概率分布函数能较好的拟合各站点的降水极值,通过设定重现期求得降水极值的理论值,从其空间分布特征可发现,降水极值总体上呈现出由浙南向浙东北地区减小的趋势,且南北差异较大。     

黄淮地区月极端气温概率模型研究

利用黄淮地区 1 4个国家基本 (准 )站 1 951～ 1 993年的月极端气温资料序列 ,分别进行了正态、Gumbel、Weibell概率分布拟合试验。根据拟合检验值和误差 ,确定了各站月极端最高气温和极端最低气温的概率分布模型     

基于极大似然算法的风廓线雷达谱矩估计

在风廓线雷达谱数据处理中,雷达低层取样体积探测的谱数据通常出现地物杂波、间歇性杂波、地磁杂波与大气回波谱混杂交叠的情况。为了有效抑制和去除杂波干扰,并且提高风廓线雷达探测范围和测量精度,必须对大气回波谱的谱矩进行有效的估计。通过对风廓线雷达多普勒回波功率谱分析,提出基于极大似然算法估计雷达回波谱谱数据的方法,并且进行了实际数据仿真分析,通过与常规方法进行对比分析,验证了算法的可行性和有效性,在低层取样体积谱矩估计得到了显著的改善。     

青藏高原夏季极端降水概率分布特征

基于国家级地面气象站基本气象要素日值数据集的均一化降水序列计算了1961—2014年青藏高原中东部71个站夏季极端降水指数,选取二参数和三参数的Weibull分布、广义极值分布、皮尔逊Ⅲ型分布(Gamma分布)、对数逻辑斯特分布拟合其极值,分析了青藏高原中东部夏季极端降水的极值分布特征。结果表明:青藏高原夏季极端降水由东南向西北递减,大值中心位于四川东部地区,西藏东南部有一较大值中心,小值中心位于青海西北部。通过极值分布函数对极端降水指数的拟合,发现不同的指数适用的函数不同,需采用多种概率分布模式进行对比,并结合实际物理意义加以选择最适合的拟合分布函数。利用Gumbel分布计算夏季极端降水指数的多年一遇水平、50年一遇和100年一遇水平,均呈现出完全一致的空间分布特征:东南降水多,西北降水少;根据滑动t检验,强降水量和极强降水量均于2006年突变,由Gumbel分布估计突变后青藏高原中东部的极端降水有所增加。     

Statistical distributions of extreme dry spell in Peninsular Malaysia

Statistical distributions of annual extreme (AE) series and partial duration (PD) series for dry-spell event are analyzed for a database of daily rainfall records of 50 rain-gauge stations in Peninsular Malaysia, with recording period extending from 1975 to 2004. The three-parameter generalized extreme value (GEV) and generalized Pareto (GP) distributions are considered to model both series. In both cases, the parameters of these two distributions are fitted by means of the L-moments method, which provides a robust estimation of them. The goodness-of-fit (GOF) between empirical data and theoretical distributions are then evaluated by means of the L-moment ratio diagram and several goodness-of-fit tests for each of the 50 stations. It is found that for the majority of stations, the AE and PD series are well fitted by the GEV and GP models, respectively. Based on the models that have been identified, we can reasonably predict the risks associated with extreme dry spells for various return periods.     

A newly-discovered GPD-GEV relationship together with comparing their models of extreme precipitation in summer

It has been theoretically proven that at a high threshold an approximate expression for a quantile of GEV （Generalized Extreme Values） distribution can be derived from GPD （Generalized Pareto Distribution）. Afterwards, a quantile of extreme rainfall events in a certain return period is found using L-moment estimation and extreme rainfall events simulated by GPD and GEV, with all aspects of their results compared. Numerical simulations show that POT （Peaks Over Threshold）-based GPD is advantageous in its simple operation and subjected to practically no effect of the sample size of the primitive series, producing steady high-precision fittings in the whole field of values （including the high-end heavy tailed）. In comparison, BM （Block Maximum）-based GEV is limited, to some extent, to the probability and quantile simulation, thereby showing that GPD is an extension of GEV, the former being of greater utility and higher significance to climate research compared to the latter.     

使用遗传算法优化雷达测量降水Z-R关系

将遗传算法应用到雷达测量降水的Z-R关系参数优化问题中,通过引入改进的误差函数加入总降水量误差控制,对1998年6月28日~7月2日阜阳地区的两次降水过程进行了参数优化试验,并和常规的最小二乘法、最优化方法及M-P参数法进行了对比,试验结果表明:新方法在总降水量估测方面具有更高的准确性和更好的动态适应性。     

暴雨强度公式参数估计及其应用

利用最优化法实现非线性模型参数的数值解法 ,应用于暴雨强度公式参数估计 ,提高了公式参数估计的精度。该方法可广泛应用于多参数模式的优化问题     

Melt Pond Scheme Parameter Estimation Using an Adjoint Model

Melt ponds significantly affect Arctic sea ice thermodynamic processes. The melt pond parameterization scheme in the Los Alamos sea ice model(CICE6.0) can predict the volume, area fraction(the ratio between melt pond area to sea ice area in a model grid), and depth of melt ponds. However, this scheme has some uncertain parameters that affect melt pond simulations. These parameters could be determined through a conventional parameter estimation method, which requires a large number of sensitivity simulations. The adjoint model can calculate the parameter sensitivity efficiently. In the present research, an adjoint model was developed for the CESM(Community Earth System Model) melt pond scheme. A melt pond parameter estimation algorithm was then developed based on the CICE6.0 sea ice model, melt pond adjoint model,and L-BFGS(Limited-memory Broyden-Fletcher-Goldfard-Shanno) minimization algorithm. The parameter estimation algorithm was verified under idealized conditions. By using MODIS(Moderate Resolution Imaging Spectroradiometer)melt pond fraction observation as a constraint and the developed parameter estimation algorithm, the melt pond aspect ratio parameter in CESM scheme, which is defined as the ratio between pond depth and pond area fraction, was estimated every eight days during summertime for two different regions in the Arctic. One region was covered by multi-year ice(MYI) and the other by first-year ice(FYI). The estimated parameter was then used in simulations and the results show that:(1) the estimated parameter varies over time and is quite different for MYI and FYI;(2) the estimated parameter improved the simulation of the melt pond fraction.     

Misspecification of extreme rainfall population statistics and frequency inference due to measurement bias and variability

Statistical analysis of rainfall extremes is mostly based on the assumption of error-free data, despite common knowledge about the widespread incidence of precipitation measurement bias and variability. The objective of this study is primarily to investigate the impact of measurement bias and variability in statistical classification and quantile estimates of rainfall extremes. A theoretical framework is presented for the analysis of moment coefficients and probability distributions for rainfall extremes corrupted by measurement bias and variability. Furthermore, methods are outlined for practical statistical analysis of error-corrupted rainfall extremes, based on maximum likelihood. Frequency inference and testing for the presence of measurement variability are the main topics. Modelling of data series is undertaken in order to exemplify the statistical assessment and the real-life impact of measurement error.It is shown that: (a) unaccounted measurement error may potentially cause a considerable degree of misspecification about the conventional moment and L-moment coefficients of variation, and the conventional moments of skewness and kurtosis; (b) the presence of measurement variability alone can cause significant and nonlinear quantile bias which further strongly increases with the additional presence of measurement bias; and (c) maximum likelihood estimation provides a general and efficient tool for assessing measurement error in extreme rainfall frequency analysis.     

CoLM 模拟土壤温度和湿度最敏感参数的研究

合理的参数估计是提高陆面模式模拟能力的关键,而其过高的维数极大地增加了合理估计的难度。参数的敏感性分析,旨在针对目标变量找出最敏感的参数,从而实现在有限计算机资源条件下,对参数进行合理估计。本文以Common Land Model(CoLM)为研究对象,利用Morris 方法定性地从40 个参数中筛选出影响土壤温度和土壤湿度的敏感参数,并通过Sobol' 方法从敏感性顺序和各敏感参数的累积贡献率两个方面,对Morris 方法分析结果进行验证。在此基础上,本研究还利用Sobol' 方法对已筛选的参数做定量敏感性分析,最终确定参数的主效应、交互效应和总效应。研究结果表明,Morris 方法可以基于少量样本实现复杂的陆面模式的参数筛选,而Sobol' 方法的结果又从定量的角度描述了每个敏感参数对模型响应的影响程度,并且两种方法结论一致。     

Impact of the Time Scale of Model Sensitivity Response on Coupled Model Parameter Estimation

That a model has sensitivity responses to parameter uncertainties is a key concept in implementing model parameter estimation using filtering theory and methodology.Depending on the nature of associated physics and characteristic variability of the fluid in a coupled system,the response time scales of a model to parameters can be different,from hourly to decadal.Unlike state estimation,where the update frequency is usually linked with observational frequency,the update frequency for parameter estimation must be associated with the time scale of the model sensitivity response to the parameter being estimated.Here,with a simple coupled model,the impact of model sensitivity response time scales on coupled model parameter estimation is studied.The model includes characteristic synoptic to decadal scales by coupling a long-term varying deep ocean with a slow-varying upper ocean forced by a chaotic atmosphere.Results show that,using the update frequency determined by the model sensitivity response time scale,both the reliability and quality of parameter estimation can be improved significantly,and thus the estimated parameters make the model more consistent with the observation.These simple model results provide a guideline for when real observations are used to optimize the parameters in a coupled general circulation model for improving climate analysis and prediction initialization.     

A study of impact of the geographic dependence of observing system on parameter estimation with an intermediate coupled model

Observational information has a strong geographic dependence that may directly influence the quality of parameter estimation in a coupled climate system. Using an intermediate atmosphere-ocean-land coupled model, the impact of geographic dependent observing system on parameter estimation is explored within a “twin” experiment framework. The “observations” produced by a “truth” model are assimilated into an assimilation model in which the most sensitive model parameter has a different geographic structure from the “truth”, for retrieving the “truth” geographic structure of the parameter. To examine the influence of data-sparse areas on parameter estimation, the twin experiment is also performed with an observing system in which the observations in some area are removed. Results show that traditional single-valued parameter estimation (SPE) attains a global mean of the “truth”, while geographic dependent parameter optimization (GPO) can retrieve the “truth” structure of the parameter and therefore significantly improves estimated states and model predictability. This is especially true when an observing system with data-void areas is applied, where the error of state estimate is reduced by 31 % and the corresponding forecast skill is doubled by GPO compared with SPE.