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主分量分析在热带气旋强度客观预报中的试用 总被引：3，自引：0，他引：3
为了解主分量因子分析在热带气旋强度客观预报中的应用效能,在NCEP再分析资料、T106L19模式产品和热带气旋历史观测资料基础上,采用主分量因子分析技术,结合多元线性回归和BP型人工神经网络,开展了西北太平洋热带气旋的强度客观预报技术研究试验。试验包含完全预报法原理下的预报因子有无主分量分析、线性与非线性预报方法建模等方面的比较。结果表明,预报因子的主分量分析通过降低线性回归和BP人工神经网络模型的维数,提高因子间独立性,可减小模型强度预报平均绝对误差,提高模型实际预报能力。 相似文献
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采用逐步多元线性回归模型、一元线性回归模型、综合法模型和比值法模型对分布于全国31个台站的降水量资料进行了模型订正效果的试验与分析。结果表明:（1）逐步多元线性回归模型对年降水量序列的订正效果较好,其相对拟合误差总平均在0.08以下,而其它3种模型与月降水量序列的订正效果较差,相对拟合误差总平均在0.11以上;（2）若将年降水量序列相对拟合误差控制在0.10以内,则要求确定订正方程式的平行资料年数为10年或以上,要求订正站与基本站序列的相关系数在0.85以上;（3）较湿润地区拟合误差较小,反之较大。 相似文献
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支持向量机（SVM）方法在气象预报中的个例试验 总被引：4，自引：0，他引：4
SVM（Support Vector Machine）方法的基本思想是：通过非线性映射把样本空间映射到一个高维乃至于无穷维的特征空间（Hilbert空间），在特征空间中寻求最优划分或回归线性超平面，从而解决样本空间中的高度非线性分类和回归等问题。利用1998～2000年9～11月T106—36小时预报的各种输出产品构造因子，以单站有无降水为预报对象，采用MOS法方式构造样本，通过训练建立了单站的晴雨SVM分类预报模型，利用1990～2000年4～9月ECMWF北半球的500hPa高度、850hPa温度、地面气压的0小时分析场资料，确定关键区域，构造预报因子，以PP法方式构造样本，通过训练建立了四川盆地内单站气温的SVM回归预报模型，并进行了模拟试验，结果表明，无论是单站晴雨的SVM分类预报模型还是单站平均气温的SVM回归预报模型都显示出了艮好的预报能力。 相似文献
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严小华 《沙漠与绿洲气象(新疆气象)》1994,(1)
介绍了灰色系统GM（1，n）模型的建模原理及方法；建立了巢湖区域主汛期（6—8月）降水长期预测GM（1，3）灰色预测模型，并将之与GM（1，1）模型及传统的线性回归预报方程进行比较讨论。 相似文献
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变分四维同化方法若干问题的数值试验 总被引：9，自引：5，他引：4
资料误差和模式误差都可能影响变分四维同化的结果,针对这一问题利用浅水方程模式进行了变分四维同化的数值模拟试验。试验分三种情况进行：（１）仅修正初始场,（２）仅修正模式;（３）二者同时修正。试验结果表明,当模式有误差时,传统的变分四维同化方法（仅修正初始场）可能将模式的误差混淆到初始场中去,尽管在同化期间可得到较好的拟合,但却不一定能保证同化后有好的预报。如果不修正初始场而修正模式,当模式误差不大时 相似文献
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利用1999—2009年安徽省淮河以南地区60个县市站夏季逐日降水资料和安庆市探空站逐日资料,研究了中低层不同风向配置下局地降水与大尺度降水场之间的关系,以3种不同预报对象及相应的预报因子分别采用神经网络和线性回归方法设计6种预报模型对观测资料进行逼近和优化,从而实现空间降尺度.分析对比6种预报模型46站逐日降水量的拟合和预报效果,结果表明:采取相同的预报对象及预报因子的BP神经网络模型在拟合和预报效果上均好于线性回归模型,可见夏季降水场之间以非线性相关为主;神经网络模型预报结果同常用的Cressman插值预报相比,能很好地反映出降水的基本分布及局地特征;预报对象为单站降水序列的神经网络模型在以平原、河流为主要地形的区域预报效果较好,预报对象为REOF主成分的神经网络模型则在山地和丘陵地形区域预报效果较好. 相似文献
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引言加拿大气象中心（CMC）技术发展科（TDS）在线性回归技术基础上开发了降水概率（POP）、总云量以及地面温度预报系统。MOS数据库是1980年9月建立的，此数据库中储存着世界时OO（OOZ）和12时间ZZ）的资料，每12小时的实况分析资料和预报资料，并将所有资料保存GO小时。1983年5月数据库进行了扩展，除了储存每12小时所进行的实况分析资料和预报资料外，增刀a了每6小时直到48小时的预报场的存储，这些储存在MOS数据库中的预报场，是由力。拿大港模式（SPEC）（Daley等，1976）作出的。1985年秋季，数据库再次扩展，包括由区域… 相似文献
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采用ＧＭＳ－４卫星的红外和可见光展宽云图资料，取０－４０°Ｎ、１００－１１２°Ｅ范围估算逐时降水率场。针对青藏高原东北边坡复杂地形，将卫星信息与其相匹配的数字化地形模型结合，取陕、甘、宁三省（区）和青海省东部１９０站的１小时雨量资料。先后采用多次二级判别、多元线性回归和多级逐步判别等统计模型，通过反复试验，最后选定多级逐步判别方案，制作反演雨强等级场的自动处理显示系统。该系统在３８６微机上通过ＶＧＡ卡实现假彩色图像显示，满足现时预报需要。 相似文献
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气象台站的迁移常导致气候序列的非均一性。本文对互助站迁站前后气温序列作均一性检验及订正。采用t检验和SNHT法,对互助站迁站前后的气温序列进行均一性检验,结果表明站址迁移对互助站温度序列均一性的影响非常显著。对差值法、一元线性回归、逐步多元线性回归以及确立订正方程式的参考站平行资料年限对互助站气温序列订正效果进行分析,结果表明用参考站15年平行观测资料建立的逐步多元线性回归订正效果较好。用逐步多元线性回归法对互助站迁站前的气温序列进行订正,经订正后互助站气候倾向率为0.31℃/10a,消除了序列的不均一现象。 相似文献
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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. 相似文献
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New statistical models for long-range forecasting of southwest monsoon rainfall over India 总被引：2，自引：0，他引：2
The India Meteorological Department (IMD) has been issuing long-range forecasts (LRF) based on statistical methods for the
southwest monsoon rainfall over India (ISMR) for more than 100 years. Many statistical and dynamical models including the
operational models of IMD failed to predict the recent deficient monsoon years of 2002 and 2004. In this paper, we report
the improved results of new experimental statistical models developed for LRF of southwest monsoon seasonal (June–September)
rainfall. These models were developed to facilitate the IMD’s present two-stage operational forecast strategy. Models based
on the ensemble multiple linear regression (EMR) and projection pursuit regression (PPR) techniques were developed to forecast
the ISMR. These models used new methods of predictor selection and model development. After carrying out a detailed analysis
of various global climate data sets; two predictor sets, each consisting of six predictors were selected. Our model performance
was evaluated for the period from 1981 to 2004 by sliding the model training period with a window length of 23 years. The
new models showed better performance in their hindcast, compared to the model based on climatology. The Heidke scores for
the three category forecasts during the verification period by the first stage models based on EMR and PPR methods were 0.5
and 0.44, respectively, and those of June models were 0.63 and 0.38, respectively. Root mean square error of these models
during the verification period (1981–2004) varied between 4.56 and 6.75% from long period average (LPA) as against 10.0% from
the LPA of the model based on climatology alone. These models were able to provide correct forecasts of the recent two deficient
monsoon rainfall events (2002 and 2004). The experimental forecasts for the 2005 southwest monsoon season based on these models
were also found to be accurate. 相似文献
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影响我国夏季汛期降水异常的因子繁多,不同因子之间复杂的相互作用制约我国夏季降水季节预测水平。目前动力模式对降水预测技巧水平较低,如何开发客观统计预报方法,提高我国夏季降水预报技巧依然存在挑战。该文基于最小二乘法拟合和交叉检验方法,提出一种搜索预测因子潜在预测技巧的方法（潜在技巧分布图）,并基于该方法开发预测因子自动选择器,建立中国夏季降水异常自动统计预测模型。与传统线性相关分析相比,潜在技巧分布图不受极端气候事件影响,可直观展现具有显著预测技巧的前兆信号,而预测因子自动选择器则能从潜在技巧分布图中自动筛选最优预测因子,获得逐年不同的预测因子,更符合中国夏季降水异常影响因子多样性的客观事实。在完全剔除预测当年信息的回报试验中,该预测模型对1999—2019年中国夏季汛期降水异常的历史回报技巧明显高于动力模式。通过方差订正,历史回报降水的PS评分从71.00分提高到82.10分,显示了该模型的潜在预报潜力。 相似文献
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Given the coarse resolution of global climate models, downscaling techniques are often needed to generate finer scale projections of variables affected by local-scale processes such as precipitation. However, classical statistical downscaling experiments for future climate rely on the time-invariance assumption as one cannot know the true change in the variable of interest, nor validate the models with data not yet observed. Our experimental setup involves using the Canadian regional climate model (CRCM) outputs as pseudo-observations to estimate model performance in the context of future climate projections by replacing historical and future observations with model simulations from the CRCM, nested within the domain of the Canadian global climate model (CGCM). In particular, we evaluated statistically downscaled daily precipitation time series in terms of the Peirce skill score, mean absolute errors, and climate indices. Specifically, we used a variety of linear and nonlinear methods such as artificial neural networks (ANN), decision trees and ensembles, multiple linear regression, and k-nearest neighbors to generate present and future daily precipitation occurrences and amounts. We obtained the predictors from the CGCM 3.1 20C3M (1971–2000) and A2 (2041–2070) simulations, and precipitation outputs from the CRCM 4.2 (forced with the CGCM 3.1 boundary conditions) as predictands. Overall, ANN models and tree ensembles outscored the linear models and simple nonlinear models in terms of precipitation occurrences, without performance deteriorating in future climate. In contrast, for the precipitation amounts and related climate indices, the performance of downscaling models deteriorated in future climate. 相似文献
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基于ASD（automated statistical downscaling）统计降尺度模型提供的多元线性回归和岭回归两种统计降尺度方法,采用RCP4.5（representative concentration pathways 4.5）和RCP8.5情景下全球气候模式MPI-ESM-LR输出的预报因子数据、NCEP/NCAR再分析数据和秦岭山地周边10个气象站观测数据,评估两种统计降尺度方法在秦岭山地的适用性及预估秦岭山地未来3个时期（2006-2040年、2041-2070年和2071-2100年）的平均气温和降水。结果表明:率定期和验证期内,两种统计降尺度方法均可以较好地模拟研究区域的平均气温和降水的变化特征,且多元线性回归的模拟效果优于岭回归。在未来气候情景下,两种统计降尺度方法预估的研究区域平均气温均呈明显上升趋势,气温增幅随辐射强迫增加而增大。降水方面,21世纪未来3个时期降水均呈不明显减少趋势,但季节分配发生变化。综合考虑两种统计降尺度方法在秦岭山地对平均气温和降水的模拟效果和情景预估结果,认为多元线性回归降尺度方法更适用于秦岭山地气候变化的降尺度预估研究。 相似文献
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1. Introduction In recent decades, extreme weather events seem to be growing in frequency and risk due to water-related disasters. According to the World Meteorological Or- ganization report (ISDR and WMO, 2004) on World Water Day, 22 March 2004, the economic losses caused by water-related disasters, including floods, droughts and tropical cyclones, are on an increasing trend as follows: the yearly mean in the 1970s was about 131 billion US dollars, 204 billion dollars in the 1980s, and … 相似文献
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利用逐日气温和降水量数据、NCEP/NCAR再分析资料以及预报场资料,通过分析提取我国南方区域持续性低温雨雪过程及其预报因子,使用粒子群-神经网络方法建立非线性的统计集合预报模型 (PSONN-EPM),对我国南方区域持续性低温雨雪过程进行预报试验。结果表明:以过程的冷湿程度及影响范围为标准,将低温雨雪过程分为一般过程和严重过程,并建立不同的预报模型效果较好。通过10 d独立样本预报试验看,基于粒子群-神经网络方法建立的集合预报模型比基于逐步回归方法建立的预报模型的预报平均相对误差小,对严重过程预报能力高于对一般过程预报,且这种非线性统计集合建模方法在建模过程中不需要调整神经网络参数,在实际预报业务中值得尝试。 相似文献
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基于BCC_CSM模式的中国东部夏季降水预测检验及订正 总被引：1，自引：1，他引：0
基于国家气候中心第二代季节预测模式的历史回报试验数据,检验了模式对我国东部夏季降水的预测能力,探讨了预测误差形成的可能原因,并应用降尺度方法提高了模式的降水预测技巧。分析表明:（1）模式能在一定程度上把握我国东部夏季降水时空变率的两个主要模态（偶极子型模态和全区一致型模态）,但是不同超前时间的预测在刻画模态方差贡献、异常空间分布特征、时间系数的年际变化等方面存在明显误差;（2）模式能够合理预测大尺度环流和海表温度（SST）的变化特征,但是对中国东部夏季降水的总体预测技巧有限,这与模式不能准确刻画西太平洋副热带高压、大陆高压、中高纬阻塞高压等环流系统以及热带太平洋、印度洋SST变率对中国东部降水模态的影响有关;（3）针对1991~2003年回报试验数据中的500 hPa位势高度、850 hPa纬向风和经向风、SST变量,在全球范围内寻找并定位与中国东部站点降水关系最密切的预报因子,进而建立针对降水预测的单因子线性回归、多因子逐步和多元回归模型。采用2004~2013年回报试验对所建立的降水预测模型进行了独立检验,结果表明:所建立的降尺度预测模型能显著提高中国东部地区夏季降水的预报技巧。以6月1日起报试验为例,预测的第一模态（第二模态）与观测的空间相关系数由原始的0.12（0.48）提高到了0.58（0.80）,时间相关系数则从0.47（0.15）提高到0.80（0.67）;其它超前时间的预测试验中,降尺度预测模型的降水预测技巧相比模式原始预测技巧也同样明显提高。 相似文献
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Summary ¶The climate system can be regarded as a dynamic nonlinear system. Thus, traditional linear statistical methods fail to model the nonlinearities of such a system. These nonlinearities render it necessary to find alternative statistical techniques. Since artificial neural network models (NNM) represent such a nonlinear statistical method their use in analyzing the climate system has been studied for a couple of years now. Most authors use the standard Backpropagation Network (BPN) for their investigations, although this specific model architecture carries a certain risk of over-/ underfitting. Here we use the so called Cauchy Machine (CM) with an implemented Fast Simulated Annealing schedule (FSA) (Szu, 1986) for the purpose of attributing and detecting anthropogenic climate change instead. Under certain conditions the CM-FSA guarantees to find the global minimum of a yet undefined cost function (Geman and Geman, 1986).In addition to potential anthropogenic influences on climate (greenhouse gases (GHG), sulphur dioxide (SO2)) natural influences on near surface air temperature (variations of solar activity, explosive volcanism and the El Niño/Southern Oscillation phenomenon) serve as model inputs. The simulations are carried out on different spatial scales: global and area weighted averages. In addition, a multiple linear regression analysis serves as a linear reference.It is shown that the adaptive nonlinear CM-FSA algorithm captures the dynamics of the climate system to a great extent. However, free parameters of this specific network architecture have to be optimized subjectively. The quality of the simulations obtained by the CM-FSA algorithm exceeds the results of a multiple linear regression model; the simulation quality on the global scale amounts up to 81% explained variance. Furthermore the combined anthropogenic effect corresponds to the observed increase in temperature Jones et al. (1994), updated by Jones (1999a), for the examined period 1856–1998 on all investigated scales. In accordance to recent findings of physical climate models, the CM-FSA succeeds with the detection of anthropogenic induced climate change on a high significance level. Thus, the CM-FSA algorithm can be regarded as a suitable nonlinear statistical tool for modeling and diagnosing the climate system. 相似文献