排序方式： 共有49条查询结果，搜索用时 15 毫秒
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基于1982-2017年NCEP_CFSv2（NCEP Climate Forecast System version 2）模式预测资料对黑龙江省夏季降水进行降尺度预测。通过分析黑龙江省夏季降水与同期环流因子的关系、模式对关键区环流因子的预测,选取模式模拟与再分析资料相关较好、黑龙江降水实况与再分析资料关系较好的环流因子作为预测因子,结合最优子集回归法筛选因子,建立降尺度预测模型,最后采用交叉检验法进行预测效果检验和独立样本预测。结果表明：模式降尺度预测与实况的距平符号-致率为69%,6 a独立样本预测中有5 a预测正确,优于目前的业务预测效果。进-步研究发现,在模式能够准确预测环流因子的情况下,模式降尺度可以较好地预测黑龙江省夏季降水的趋势。此外,模式降尺度在拉尼娜年预测效果较好。 相似文献
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Representation and quantification of uncertainty in climate change impact studies are a difficult task. Several sources of uncertainty arise in studies of hydrologic impacts of climate change, such as those due to choice of general circulation models (GCMs), scenarios and downscaling methods. Recently, much work has focused on uncertainty quantification and modeling in regional climate change impacts. In this paper, an uncertainty modeling framework is evaluated, which uses a generalized uncertainty measure to combine GCM, scenario and downscaling uncertainties. The Dempster–Shafer (D–S) evidence theory is used for representing and combining uncertainty from various sources. A significant advantage of the D–S framework over the traditional probabilistic approach is that it allows for the allocation of a probability mass to sets or intervals, and can hence handle both aleatory or stochastic uncertainty, and epistemic or subjective uncertainty. This paper shows how the D–S theory can be used to represent beliefs in some hypotheses such as hydrologic drought or wet conditions, describe uncertainty and ignorance in the system, and give a quantitative measurement of belief and plausibility in results. The D–S approach has been used in this work for information synthesis using various evidence combination rules having different conflict modeling approaches. A case study is presented for hydrologic drought prediction using downscaled streamflow in the Mahanadi River at Hirakud in Orissa, India. Projections of n most likely monsoon streamflow sequences are obtained from a conditional random field (CRF) downscaling model, using an ensemble of three GCMs for three scenarios, which are converted to monsoon standardized streamflow index (SSFI-4) series. This range is used to specify the basic probability assignment (bpa) for a Dempster–Shafer structure, which represents uncertainty associated with each of the SSFI-4 classifications. These uncertainties are then combined across GCMs and scenarios using various evidence combination rules given by the D–S theory. A Bayesian approach is also presented for this case study, which models the uncertainty in projected frequencies of SSFI-4 classifications by deriving a posterior distribution for the frequency of each classification, using an ensemble of GCMs and scenarios. Results from the D–S and Bayesian approaches are compared, and relative merits of each approach are discussed. Both approaches show an increasing probability of extreme, severe and moderate droughts and decreasing probability of normal and wet conditions in Orissa as a result of climate change. 相似文献
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空间尺度转换是近年来区域生态水文研究领域的一个基本研究问题。其需要主要是源于模型的输入数据与所能提供的数据空间尺度不一致以及模型所代表的地表过程空间尺度与所观测的地表过程空间尺度不吻合。综述了目前区域生态水文模拟研究中常用的空间尺度转换研究方法,包括向上尺度转换和向下尺度转换。详细论述了2种向下尺度转换方法: 统计学经验模型和动态模型。前者是通过将GCM大尺度数据与长期的历史观测数据比较从而建立统计学相关模型, 然后利用这个统计学经验模型进行向下的空间尺度转换. 然而动态模型并不直接对GCM数据进行向下尺度的转换,而是对与GCM进行动态耦合的区域气候模型(RCM) 的输出数据进行空间尺度转换. 通常后者所获得的数据精度要比前者高,但是一个主要缺点就是并不是全球所有的研究区域都有对应的RCM。还详细论述了2种向上尺度转换方法: 统计学经验模型和斑块模型。前者是建立一个能代表小尺度信息在大尺度上分布的密度分布概率函数, 然后利用这个函数在所需的大尺度上进行积分而求得大尺度所需的信息。而后者是根据相似性最大化原则将大尺度划分为若干个可操作的小尺度斑块,然后将计算的每个小尺度斑块的信息平均化得到大尺度所需的信息。通常在计算这种斑块化的小尺度信息的时候,对每个小尺度也会采用统计学经验模型来计算代表整个斑块小尺度的信息。建议用斑块模型与统计学经验模型相集合的方法来实现向上的空间尺度转换 相似文献
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基于政府间气候变化委员会第四次评估报告(IPCC-AR4)所采用的20个气候模式在未来大气温室气体中等排放情景(A1B)下模拟结果的集合平均以及一个全球气候模式模拟输出驱动下的动力降尺度(downscaling)分析结果,对青藏高原地区未来30～50年的气候变化趋势进行了预估研究.结果表明,从2030-2049年相对于1980-1999年气候平均值的变化来看,青藏高原大部分地区年平均地面气温的升温幅度在1.4～2.2℃之间,高海拔地区的增温一般更为显著,西藏西部的冬季增暖将达到2.4℃以上.降水量的变化相对较小,青藏高原大部分地区和全年多数季节降水可能增加,但未来30～50年青藏高原地区降水率增量通常不超过5%.考虑到未来大气温室气体排放程度、多模式集合预估以及区域尺度气候模拟等多方面均可能存在不确定性,这里给出的青藏高原未来气候变化预估结果应适时检验和修正. 相似文献
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A soil–vegetation–atmosphere transfer model (SVAT), interactions between the soil–biosphere–atmosphere (ISBA) of Météo France, is modified and applied to the Athabasca River Basin (ARB) to model its water and energy fluxes. Two meteorological datasets are used: the archived forecasts from the Meteorological Survey of Canada’s Global Environmental Multiscale Model (GEM) and the European Centre for Mid-range Weather Forecasts global re-analysis (ERA-40), representing spatial scales typical of a weather forecasting model and a global circulation model (GCM), respectively. The original treatment of soil moisture and rainfall in ISBA (OISBA) is modified to statistically account for sub-grid heterogeneity of soil moisture and rainfall to produce new, highly non-linear formulations for surface and sub-surface runoff (MISBA). These new formulations can be readily applied to most existing SVATs. Stand alone mode simulations using the GEM data demonstrate that MISBA significantly improves streamflow predictions despite requiring two fewer parameters than OISBA. Simulations using the ERA-40 data show that it is possible to reproduce the annual variation in monthly, mean annual, and annual minimum flows at GCM scales without using downscaling techniques. Finally, simulations using a simple downscaling scheme show that the better performance of higher resolution datasets can be primarily attributed to improved representation of local variation of land cover, topography, and climate. 相似文献
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Downscaling has an important role to play in remote sensing. It allows prediction at a finer spatial resolution than that of the input imagery, based on either (i) assumptions or prior knowledge about the character of the target spatial variation coupled with spatial optimisation, (ii) spatial prediction through interpolation or (iii) direct information on the relation between spatial resolutions in the form of a regression model. Two classes of goal can be distinguished based on whether continua are predicted (through downscaling or area-to-point prediction) or categories are predicted (super-resolution mapping), in both cases from continuous input data. This paper reviews a range of techniques for both goals, focusing on area-to-point kriging and downscaling cokriging in the former case and spatial optimisation techniques and multiple point geostatistics in the latter case. Several issues are discussed including the information content of training data, including training images, the need for model-based uncertainty information to accompany downscaling predictions, and the fundamental limits on the representativeness of downscaling predictions. The paper ends with a look towards the grand challenge of downscaling in the context of time-series image stacks. The challenge here is to use all the available information to produce a downscaled series of images that is coherent between images and, thus, which helps to distinguish real changes (signal) from noise. 相似文献
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Large scale climate and rainfall seasonality in a Mediterranean Area: Insights from a non‐homogeneous Markov model applied to the Agro‐Pontino plain 
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下载免费PDF全文 Francesco Cioffi Federico Conticello Upmanu Lall Lucia Marotta Vito Telesca 《水文研究》2017,31(3):668-686
In the context of climate change and variability, there is considerable interest in how large scale climate indicators influence regional precipitation occurrence and its seasonality. Seasonal and longer climate projections from coupled ocean–atmosphere models need to be downscaled to regional levels for hydrologic applications, and the identification of appropriate state variables from such models that can best inform this process is also of direct interest. Here, a Non‐Homogeneous Hidden Markov Model (NHMM) for downscaling daily rainfall is developed for the Agro‐Pontino Plain, a coastal reclamation region very vulnerable to changes of hydrological cycle. The NHMM, through a set of atmospheric predictors, provides the link between large scale meteorological features and local rainfall patterns. Atmospheric data from the NCEP/NCAR archive and 56‐years record (1951–2004) of daily rainfall measurements from 7 stations in Agro‐Pontino Plain are analyzed. A number of validation tests are carried out, in order to: 1) identify the best set of atmospheric predictors to model local rainfall; 2) evaluate the model performance to capture realistically relevant rainfall attributes as the inter‐annual and seasonal variability, as well as average and extreme rainfall patterns. Validation tests show that the best set of atmospheric predictors are the following: mean sea level pressure, temperature at 1000 hPa, meridional and zonal wind at 850 hPa and precipitable water, from 20°N to 80°N of latitude and from 80°W to 60°E of longitude. Furthermore, the validation tests show that the rainfall attributes are simulated realistically and accurately. The capability of the NHMM to be used as a forecasting tool to quantify changes of rainfall patterns forced by alteration of atmospheric circulation under climate change and variability scenarios is discussed. 相似文献
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基于集合预报产品的降尺度降水预报试验 总被引：7，自引：2，他引：5
利用降水距平百分率的降尺度预报方法和1951-2008 NCEP资料及我国降水资料,建立了降水距平百分率的预报模型,基于T106L19模式的月动力延伸集合预报结果,进行了2007-2009年3 a的预报试验和效果检验.结果表明,基于集合预报产品的统计降尺度方法对降水距平百分率的预报技巧高于模式降水的预报技巧;500 hPa月平均高度场的预报技巧直接影响到降水距平百分率的预报技巧,平均环流的预报技巧越高,降水距平百分率的预报技巧越高;无论集合成员数为多少,集合预报的结果都明显优于控制预报,随着集合成员数的增多,预报技巧呈增大的趋势;我国降水具有显著的季节性和区域性,以江淮地区的降水距平百分率预报技巧最高,华南地区的预报技巧其次. 相似文献
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江苏省汛期强降水过程的延伸期预报试验 总被引：1，自引：1，他引：0
针对汛期延伸期降水预报问题,根据大气低频振荡特性,运用低频天气图预报方法,通过分析关键区低频天气系统(低频气旋和低频反气旋)的活动特征,建立低频系统与强降水过程间的对应关系,通过低频系统的活动特征来预报降水过程。在2011年7—9月江苏省延伸期强降水过程预报试验中,低频天气图预报方法的预报效果较好,且预报时效为10~30 d,可以在延伸期业务预报中加以应用。此外,还运用模式统计降尺度方法预报降水落区,为强降水过程的发生提供背景依据和参考信息,具有一定的实用意义。 相似文献