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1.
以淮河流域为研究区域,利用线性矩精确求解极值降水统计特征值,为极值降水频率估计值的计算提供理论基础和依据.主要结果表明：淮河流域各站点的年最大日降雨量（Annual Maximum Precipitation,AMP）的均值和离差范围分别为92.79~130.28 mm和0.193~0.266;各站点的AMP分布都为右偏分布,即至少有一半以上的AMP值是小于平均值的,有少数的AMP值远大于平均值,且对平均值的影响较大;各站点的分布相对于正态分布而言,都属于尖峰厚尾.极值降水统计特征值的空间分布表明：大值主要分布在流域的西南山区和东部丘陵地带,即这些地方具有更多更强的AMP极值.极值降水统计参数不仅能够反映AMP的一般平均水平和年际AMP的波动情况,还能更好地掌握AMP的两端分布情况,特别是尾端的暴雨极值.因此分析极值降水统计参数可以给洪涝灾害提供很好的指导.  相似文献   

2.
利用1961~2002年ERA-40逐日再分析资料和江淮流域56个台站逐日观测降水量资料,引入基于自组织映射神经网络(Self-Organizing Maps,简称SOM)的统计降尺度方法,对江淮流域夏季(6~8月)逐日降水量进行统计建模与验证,以考察SOM对中国东部季风降水和极端降水的统计降尺度模拟能力。结果表明,SOM通过建立主要天气型与局地降水的条件转换关系,能够再现与观测一致的日降水量概率分布特征,所有台站基于概率分布函数的Brier评分(Brier Score)均近似为0,显著性评分(Significance Score)全部在0.8以上;模拟的多年平均降水日数、中雨日数、夏季总降水量、日降水强度、极端降水阈值和极端降水贡献率区域平均的偏差都低于11%;并且能够在一定程度上模拟出江淮流域夏季降水的时间变率。进一步将SOM降尺度模型应用到BCCCSM1.1(m)模式当前气候情景下,评估其对耦合模式模拟结果的改善能力。发现降尺度显著改善了模式对极端降水模拟偏弱的缺陷,对不同降水指数的模拟较BCC-CSM1.1(m)模式显著提高,降尺度后所有台站6个降水指数的相对误差百分率基本在20%以内,偏差比降尺度前减小了40%~60%;降尺度后6个降水指数气候场的空间相关系数提高到0.9,相对标准差均接近1.0,并且均方根误差在0.5以下。表明SOM降尺度方法显著提高日降水概率分布,特别是概率分布曲线尾部特征的模拟能力,极大改善了模式对极端降水场的模拟能力,为提高未来预估能力提供了基础。  相似文献   

3.
使用基于动力降尺度和统计降尺度方法得到的RCP4.5情景下的6.25 km高分辨率联合降尺度预估数据集,对长江经济带未来极端气候事件及其造成的风险展开评估和预估。结果表明：降尺度预估数据能较好的再现各极端温度指数和大部分极端降水指数的空间分布,但一些极端降水指数的偏差略大。未来长江经济带极端热事件将增加,冷事件减少;长江中游东部和下游的极端降水事件将增加,上游地区东南部发生干旱事件的可能性大。长江经济带以及上游、中游和下游3个分区的高温事件和强降水事件的国内生产总值（GDP）暴露度都将增加;人口暴露度呈先增后降的变化趋势。高温事件的GDP暴露度的分布因子和非线性因子的贡献同样重要,人口暴露度中分布因子的影响更大;强降水事件的暴露度主要取决于GDP或人口分布因子。  相似文献   

4.
1981～2010年北京地区极端降水变化特征   总被引：6，自引：1，他引：5  
采用北京地区20个常规气象站1981~2010年逐日降水数据,对北京地区极端降水的空间分布特征进行了分析。得到以下主要结论:1981~2010年,北京地区极端降水百分位数(第90、95和99个百分位数)阈值表现出较一致的空间分布特征,以第95个百分位数阈值计算的极端降水日数与降水阈值和降水量的分布有较大差异,极端降水量对总降水量的贡献可达30%~37%,极端降水强度分布与极端降水阈值分布相似。近30年,北京地区多数站点的极端降水量、降水日数和降水强度呈下降趋势,极端降水量以上甸子、怀柔、平谷和观象台下降较为明显,可达到40 mm(10 a)–1以上,极端降水强度以顺义、海淀、观象台、大兴和上甸子等站下降较为显著,每10 a降水强度减小趋势可达4 mm d–1,极端降水日数变化分布与极端降水量变化分布类似,极端降水强度变化与降水量和降水日数变化的分布有明显不同。  相似文献   

5.
Predictor selection is a critical factor affecting the statistical downscaling of daily precipitation. This study provides a general comparison between uncertainties in downscaled results from three commonly used predictor selection methods (correlation analysis, partial correlation analysis, and stepwise regression analysis). Uncertainty is analyzed by comparing statistical indices, including the mean, variance, and the distribution of monthly mean daily precipitation, wet spell length, and the number of wet days. The downscaled results are produced by the artificial neural network (ANN) statistical downscaling model and 50 years (1961–2010) of observed daily precipitation together with reanalysis predictors. Although results show little difference between downscaling methods, stepwise regression analysis is generally the best method for selecting predictors for the ANN statistical downscaling model of daily precipitation, followed by partial correlation analysis and then correlation analysis.  相似文献   

6.
We study the influence of synoptic scale atmospheric circulation on extreme daily precipitation across the United Kingdom, using observed time series from 689 rain gauges. To this end we employ a statistical model, that uses airflow strength, direction and vorticity as predictors for the generalised extreme value distribution of monthly precipitation maxima. The inferred relationships are connected with the dominant westerly flow, the orography, and the moisture supply from surrounding seas. We aggregated the results for individual rain gauges to regional scales to investigate the temporal variability of extreme precipitation. Airflow explains a significant fraction of the variability on subannual to decadal time scales. A large fraction of the especially heavy winter precipitation during the 1980s and 1990s in north Scotland can be attributed to a prevailing positive phase of the North Atlantic Oscillation. Our statistical model can be used for statistical downscaling and to validate regional climate model output.  相似文献   

7.
This paper presents a novel statistical downscaling method based on a non-linear classification technique known as self-organizing maps (SOMs) and has therefore been named SOM-SD. The relationship between large-scale atmospheric circulation and local-scale surface variable was constructed in a relatively simple and transparent manner. For a specific atmospheric state, an ensemble of possible values was generated for the predictand following the Monte Carlo method. Such a stochastic simulation is essential to explore the uncertainties of climate change in the future through a series of random re-sampling experiments. The novel downscaling method was evaluated by downscaling daily precipitation over Southeast Australia. The large-scale predictors were extracted from the daily NCAR/NCEP reanalysis data, while the predictand was high-resolution gridded daily observed precipitation (1958?C2008) from the Australian Bureau of Meteorology. The results showed that the method works reasonably well across a variety of climatic zones in the study area. Overall, there was no particular zone that stands out as a climatic entity where the downscaling skill in reproducing all statistical indices was consistently lower or higher across seasons than the other zones. The method displayed a high skill in reproducing not only the climatologic statistical properties of the observed precipitation, but also the characteristics of the extreme precipitation events. Furthermore, the model was able to reproduce, to a certain extent, the inter-annual variability of precipitation characteristics.  相似文献   

8.
Several studies have been devoted to dynamic and statistical downscaling for both climate variability and climate change. This paper introduces an application of temporal neural networks for downscaling global climate model output and autocorrelation functions. This method is proposed for downscaling daily precipitation time series for a region in the Amazon Basin. The downscaling models were developed and validated using IPCC AR4 model output and observed daily precipitation. In this paper, five AOGCMs for the twentieth century (20C3M; 1970–1999) and three SRES scenarios (A2, A1B, and B1) were used. The performance in downscaling of the temporal neural network was compared to that of an autocorrelation statistical downscaling model with emphasis on its ability to reproduce the observed climate variability and tendency for the period 1970–1999. The model test results indicate that the neural network model significantly outperforms the statistical models for the downscaling of daily precipitation variability.  相似文献   

9.
基于DERF的SD方法预测月降水和极端降水日数   总被引：3，自引：1，他引：2       下载免费PDF全文
针对动力气候模式对区域或更小空间尺度内的日降水预测技巧偏低的问题,应用最优子集回归 (OSR) 方法对国家气候中心业务化的月动力气候模式 (DERF) 输出的高度场、风场和海平面气压场进行降尺度处理用于降水预测,旨在提高预测准确率。1982—2006年交叉检验结果表明:OSR方法能显著提高降水预测技巧,其中11~40 d改善效果最为显著。在此基础上,应用一步法和两步法两种统计降尺度方法预测极端降水日数,交叉检验结果表明:两种方法均优于随机预测,冬季两步法预测技巧略高于一步法,夏季一步法略优于两步法。综合认为OSR,OSR结合随机天气发生器 (WG) 两种统计降尺度方法对月尺度降水或极端降水日数的预测均具有较高的技巧,可作为短期气候预测的重要参考信息。  相似文献   

10.
Statistical downscaling is a technique widely used to overcome the spatial resolution problem of General Circulation Models (GCMs). Nevertheless, the evaluation of uncertainties linked with downscaled temperature and precipitation variables is essential to climate impact studies. This paper shows the potential of a statistical downscaling technique (in this case SDSM) using predictors from three different GCMs (GCGM3, GFDL and MRI) over a highly heterogeneous area in the central Andes. Biases in median and variance are estimated for downscaled temperature and precipitation using robust statistical tests, respectively Mann?CWhitney and Brown?CForsythe's tests. In addition, the ability of the downscaled variables to reproduce extreme events is tested using a frequency analysis. Results show that uncertainties in downscaled precipitations are high and that simulated precipitation variables failed to reproduce extreme events accurately. Nevertheless, a greater confidence remains in downscaled temperatures variables for the area. GCMs performed differently for temperature and precipitation as well as for the different test. In general, this study shows that statistical downscaling is able to simulate with accuracy temperature variables. More inhomogeneities are detected for precipitation variables. This first attempt to test uncertainties of statistical downscaling techniques in the heterogeneous arid central Andes contributes therefore to an improvement of the quality of predictions of climate impact studies in this area.  相似文献   

11.
The current study examines the recently proposed “bias correction and stochastic analogues” (BCSA) statistical spatial downscaling technique and attempts to improve it by conditioning coarse resolution data when generating replicates. While the BCSA method reproduces the statistical features of the observed fine data, this existing model does not replicate the observed coarse spatial pattern, and subsequently, the cross-correlation between the observed coarse data and downscaled fine data with the model cannot be preserved. To address the dissimilarity between the BCSA downscaled data and observed fine data, a new statistical spatial downscaling method, “conditional stochastic simulation with bias correction” (BCCS), which employs the conditional multivariate distribution and principal component analysis, is proposed. Gridded observed climate data of mean daily precipitation (mm/day) covering a month at 1/8° for a fine resolution and at 1° for a coarse resolution over Florida for the current and future periods were used to verify and cross-validate the proposed technique. The observed coarse and fine data cover the 50-year period from 1950 to1999, and the future RCP4.5 and RCP8.5 climate scenarios cover the 100-year period from 2000 to 2099. The verification and cross-validation results show that the proposed BCCS downscaling method serves as an effective alternative means of downscaling monthly precipitation levels to assess climate change effects on hydrological variables. The RCP4.5 and RCP8.5 GCM scenarios are successfully downscaled.  相似文献   

12.
A probabilistic precipitation forecasting model using generalized additive models (GAMs) and Bayesian model averaging (BMA) was proposed in this paper.GAMs were used to fit the spatial-temporal precipi...  相似文献   

13.
This study compared precipitation, mean air temperature (MAT) and mean sea level pressure (MSLP) from two widely used reanalysis datasets (ERA-40 and NCEP) with those from observed stations across eastern China. The evaluation was based on a comparison of both temporal and spatial variability and included several assessment criteria such as the mean values, normalized root mean square error, Mann–Kendall test, empirical orthogonal functions (EOFs) and probability density functions. The results showed that both the ERA-40 and NCEP datasets could capture temporal and spatial variability of the observed precipitation, MAT and MSLP over eastern China. The results showed that the two reanalysis datasets performed better for MAT and MSLP than for precipitation. Overall, the two reanalysis datasets revealed reasonable agreement with observations according to the evaluation. ERA-40 was better at capturing the temporal and spatial distributions for these three variables than NCEP, especially for MAT and MSLP. NCEP tended to overestimate the annual precipitation for both mean and extreme values, while ERA-40 tended to underestimate it, particularly for extreme values. The two reanalysis datasets performed better in the east and northeast regions of the study area than in other regions for capturing the temporal variability of MAT and MSLP. ERA-40 was poor at capturing the temporal variability of precipitation in northeastern China. According to the trend analysis, the two reanalysis datasets showed lower trends for MAT and precipitation and higher trends for MSLP. Both ERA-40 and NCEP had larger explained variances for the first two EOFs than the observed precipitation. This implies that both reanalysis datasets tend to simulate a more uniform spatial distribution for precipitation in the study area.  相似文献   

14.
利用1961—1990年江淮流域逐日降水资料、NCEP/NCAR再分析资料和HadCM3 SRES A1B情景下模式预估资料,采用典型相关分析统计降尺度方法,评估降尺度模型对当前极端降水指数的模拟能力,并对21世纪中期和末期的极端降水变化进行预估。结果表明：通过降尺度能够有效改善HadCM3对区域气候特征的模拟能力,极端降水指数气候平均态相对误差降低了30%～100%,但降尺度结果仍然在冬季存在湿偏差、夏季存在干偏差;在SRES A1B排放情景下,该区域大部分站点的极端强降水事件将增多,强度增大,极端强降水指数的变化幅度高于平均降水指数,且夏季增幅高于冬季;冬季极端降水贡献率（R95t）在21世纪中期和末期的平均增幅分别为14%和25%,夏季则分别增加24%和32%。  相似文献   

15.
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.  相似文献   

16.
现阶段的动力气候模式尚不能满足东亚区域气候预测的实际需求,这就需要动力和统计相结合的方法,将动力模式中具有较高预测技巧的大尺度环流信息应用到降水等气象要素的统计预测模型当中,以改善后者预测效果。本文中所介绍的组合统计降尺度模型,可将动力气候模式预测的大尺度环流变量和前期观测的外强迫信号作为预测因子来预测中国夏季降水异常。交叉检验结果显示,组合统计降尺度预测模型的距平相关系数较原始模式结果有较大提高。在实时夏季降水预测中,2013～2018年平均的预测技巧相对较高,趋势异常综合检验（PS）评分平均为71.5分,特别是2015～2018年平均的PS评分预测技巧达到72.7分,总体上高于业务模式原始预测和业务发布预测的技巧。该组合统计降尺度模型预测性能稳定,为我国季节预测业务提供了一种有效参考。  相似文献   

17.
我国地面降水的分级回归统计降尺度预报研究   总被引：2，自引：1，他引：1       下载免费PDF全文
利用TIGGE资料中欧洲中期天气预报中心(ECMWF,the European Centre for Medium-Range Weather Forecasts)、日本气象厅(JMA,the Japan Meteorological Agency)、美国国家环境预报中心(NCEP,the National Centers for Environmental Prediction)以及英国气象局(UKMO,the UK Met Office)4个中心1~7 d预报的日降水量集合预报资料,并以中国降水融合产品作为"观测值",对我国地面降水量预报进行统计降尺度处理。采用空间滑动窗口增加中雨和大雨雨量样本,建立分级雨量的回归方程,并与未分级雨量的统计降尺度预报进行对比。结果表明,对于不同模式、不同预报时效以及不同降水量级,统计降尺度的预报技巧改进程度不尽相同。统计降尺度的预报技巧依赖于模式本身的预报效果。相比雨量未分级回归,雨量分级回归的统计降尺度预报与观测值的距平相关系数更高,均方根误差更小,不同量级降水的ETS评分明显提高。对雨量分级回归统计降尺度预报结果进行二次订正,可大大减少小雨的空报。  相似文献   

18.
基于分位数映射法的黑河上游气候模式降水误差订正   总被引：1，自引：0，他引：1  
区域气候模式降水弥补了高寒山区气象站点稀少的缺陷,是水文模拟的重要驱动变量。然而,高寒山区模式输出降水的总量和频率都存在较大不确定性。因此,改进了用于降水频率纠正的分位数映射法(Quantile Mapping,QM),对中尺度数值预报模式(Weather Research and Forecasting model,WRF)模拟的黑河上游日降水输出数据进行误差订正。选取第95分位和第98分位降水量为阈值,选择2004-2009年为建模时段,2010-2013年为验证时段,使用分段拟合的方法建立传递函数,侧重于对极端降水进行单独订正。研究结果表明:该方法不仅对降水空间分布有明显的改善,对极端降水也有很好的订正效果。订正前模式模拟日降水与台站之间的均方根误差为3.41 mm·d^-1,绝对偏差为115.67 mm·y^-1,订正后均方根误差减少为3.11 mm·d^-1,绝对偏差有明显改善,为60.3 mm·y^-1。订正后流域内年降水空间分布更加合理,年降水量也更接近于观测降水插值结果,其空间相关系数由0.74改善为0.94。春、夏季订正效果优于秋、冬季,其中夏季订正效果较为明显,订正前降水偏差百分比在-0.1~0.1以内的区域面积仅占流域总面积的28%,而订正后占比增加至66%。同时,该方法对极端降水有较好的订正效果,减小了日降水强度(SDII)和极强降水量(R99p)的模拟偏差,订正后的第95分位模拟降水与观测降水插值的相关系数由0.15提高到0.48。本研究为站点稀少的黑河上游提供了一种更有效的误差订正方案,有利于为寒区水文研究获取更精确的降水数据。  相似文献   

19.
In this study, the applicability of the statistical downscaling model (SDSM) in modeling five extreme precipitation indices including R10 (no. of days with precipitation ≥10?mm?day?1), SDI (simple daily intensity), CDD (maximum number of consecutive dry days), R1d (maximum 1-day precipitation total) and R5d (maximum 5-day precipitation total) in the Yangtze River basin, China was investigated. The investigation mainly includes the calibration and validation of SDSM model on downscaling daily precipitation, the validation of modeling extreme precipitation indices using independent period of the NCEP reanalysis data, and the projection of future regional scenarios of extreme precipitation indices. The results showed that: (1) there existed good relationship between the observed and simulated extreme precipitation indices during validation period of 1991–2000, the amount and the change pattern of extreme precipitation indices could be reasonably simulated by SDSM. (2) Under both scenarios A2 and B2, during the projection period of 2010–2099, the changes of annual mean extreme precipitation indices in the Yangtze River basin would be not obvious in 2020s; while slightly increase in the 2050s; and significant increase in the 2080s as compared to the mean values of the base period. The summer might be the more distinct season with more projected increase of each extreme precipitation indices than in other seasons. And (3) there would be distinctive spatial distribution differences for the change of annual mean extreme precipitation indices in the river basin, but the most of Yangtze River basin would be dominated by the increasing trend.  相似文献   

20.
A prerequisite of a successful statistical downscaling is that large-scale predictors simulated by the General Circulation Model (GCM) must be realistic. It is assumed here that features smaller than the GCM resolution are important in determining the realism of the large-scale predictors. It is tested whether a three-step method can improve conventional one-step statistical downscaling. The method uses predictors that are upscaled from a dynamical downscaling instead of predictors taken directly from a GCM simulation. The method is applied to downscaling of monthly precipitation in Sweden. The statistical model used is a multiple regression model that uses indices of large-scale atmospheric circulation and 850-hPa specific humidity as predictors. Data from two GCMs (HadCM2 and ECHAM4) and two RCM experiments of the Rossby Centre model (RCA1) driven by the GCMs are used. It is found that upscaled RCA1 predictors capture the seasonal cycle better than those from the GCMs, and hence increase the reliability of the downscaled precipitation. However, there are only slight improvements in the simulation of the seasonal cycle of downscaled precipitation. Due to the cost of the method and the limited improvements in the downscaling results, the three-step method is not justified to replace the one-step method for downscaling of Swedish precipitation.  相似文献   

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