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1.
用前期海温预报四川夏季气温的EOF迭代方案 总被引：1，自引：0，他引：1
应用ＥＯＦ迭代方案，考虑时次历史资料，在全球海区海温与四川盆地气温非同步联的工，以海温为预报因子进行了夏季气温的长期预报。结果表明：西太平洋高温区等关键海区海温的导常对未来四川盆地夏季气温变化有重要影响由此建立的引入多时次海温的ＥＯＦ迭代长期温度预报方法，具有较强的预报能力。 相似文献
2.
云南省40年来气温场变化的基本特征 总被引：1，自引：1，他引：1
根据云南省内分布相对均匀，资料年代较长的１８个代表站的１９５１－１９９４４上气温资料，用ＥＯＦ方法分析了４０年来云南省气温场变化的基本特征。 相似文献
3.
ＴＨＥＳＩＭＵＬＡＴＩＯＮＯＦＡＳＰＲＩＮＧＰＲＥＣＩＰＩＴＡＴＩＯＮＰＲＯＣＥＳＳＦＯＲＩＣＥＳＥＥＤＩＮＧＩＮＮＯＲＴＨＣＨＩＮＡＷａｎｓＸｉａｏｂｉｎ（汪晓滨），ＨｕＺｈｉｊｎ（胡志晋）ａｎｄＹｏｕＬａｉｇｕａｎｇ（游来光）（Ｉｎｓｔｉｔｕｔｅ... 相似文献
4.
中国西北地区和蒙古国40年气温时空特征及其变化趋势 总被引：5，自引：3，他引：5
利用我国西北地区及蒙古国共５９个台站（作ＥＯＦ分析时取２５个站）１９５１￣１９９０年逐月平均气温资料，采用ＥＯＦ方法分析了该地区４０年来气温场不同季节的空间分布特征及其随时间变化的规律。分析发现气温场的空间分布主要有三种类型：（１）全区一致型，（２）南北差异型，（３）东西差异型；各月、季、年的变化周期主要集中在三个时段：２￣４年，５￣８年和１０￣１３年；夏季以短周期为主，冬季和年主要是长周期。气温 相似文献
5.
《中国气象科学研究院年报》1994,(1)
ＴＨＥＥＦＦＥＣＴＯＦＴＨＥＳＬＩＧＨＴＬＹＩＮＣＬＩＮＥＤＴＥＲＲＡＩＮＯＮＴＨＥＷＩＮＤＳＨＥＡＲＮＥＡＲＴＨＥＳＵＲＦＡＣＥＺｈａｎｇＹｏｎｇｐｉｎｇ（张永萍），ＬｉＸｉｎｇｓｈｅｎｇ（李兴生），ＺｈｏｕＸｉｕｊｉ（周秀骥）ａｎｄＢｉａｎＬｉｎ... 相似文献
6.
《中国气象科学研究院年报》1994,(1)
ＡＣＨＡＲＡＣＴＥＲＩＳＴＩＣＡＮＡＬＹＳＩＳＯＦＡＥＲＯＳＯＬＳＦＲＯＭＳＡＮＤＳＴＯＲＭＳＹａｎｇＤｏｎｇｚｈｅｎ（杨东贞），ＷａｎｇＣｈａｏ（王超）ａｎｄＹｕＸｉａｏｌａｎ（于晓岚）ＩｎｓｔｉｔｕｔｅｏｆＡｔｍｏｓｐｈｅｒｉｃＣｈｅｎｍｉｓｔｒ... 相似文献
7.
河南省季降水量时空规律客观分析 总被引：2，自引：0，他引：2
应用ＥＯＦｓ展开技术对河南省春、夏、秋季降水量的空间分布规律和时间变化进行了研究，在分析典型场特点的基础上对１９５１～１９９３年季降水场年型进行了分类。 相似文献
8.
ＲｅｖｉｅｗｏｆｔｈｅＲｅｓｅａｒｃｈｅｓｏｎＣｈａｎｇｍａａｎｄＦｕｔｕｒｅＯｂｓｅｒｖａｔｉｏｎａｌＳｔｕｄｙ（ＫＯＲＭＥＸ）Ｊａｉ－ＨｏＯｈ，Ｗｏｎ－ＴａｅＫｗｏｎａｎｄＳａｎｇ－ＢｏｍＲｙｏＭＥＴＲＩ，ＫｏｒｅａＭｅｔｅｏｒｏｌｏｇｉｃａｌＡ... 相似文献
9.
《中国气象科学研究院年报》1994,(1)
ＯＢＳＥＲＶＡＴＩＯＮＦＯＲＴＯＧＡ－ＣＯＡＲＥＡＮＤＲＥＬＥＶＡＮＴＲＥＳＥＡＲＣＨＬｉＪｉ（李骥），ＬｕＥｒ（陆尔）ａｎｄＤｉｎｇＹｉｈｕｉ（丁一汇）ＣｅｎｔｅｒｆｏｒＣｌｉｍａｔｉｃＲｅｓｅａｒｃｈＩ．ＲＥＰＯＲＴＯＮＴＯＧＡＯＢＳＥＲＶＡＴＩ... 相似文献
10.
近百年北半球陆面降水资料的插补及初步分析 总被引：6，自引：2，他引：4
采用ＥＯＦｓ展开方法插补延长了一个北半球陆面月降水资料,并讨论了该资料插补工作的合理性。利用该资料初步分析了近百年北半球陆面降水的基本特点。 相似文献
11.
12.
经验正交函数展开精度的稳定性研究 总被引：7，自引：3，他引：7
在文献[1]中,我们已从理论和数值模拟两个方面研究了用经验正交函数作基函数缩减气候数值模式自由度的可行性与有效性。用理论模型作数值试验的结果是令人满意的,应用于实际气候数值模拟,一个还需考虑的关键问题是大气外强迫等各种因子变化允许的范围内,对实际资料作EOF展开的稳定性问题。本文分别用1951—1984年500 hPa月平均高度距平场资料,1966—1975年500 hPa候平均高度距平场资料,1965—1978年夏季500 hPa逐日高度距平场资料作EOF展开,并提出了经验正交函数展开精度稳定性的判断方法,旨在证明实际资料EOF展开在大气外强迫等各种因子变化的允许范围内是稳定的,以便为我们用实际资料的经验正交函数作基函数建立一个合理的简化动力模型提供坚实的资料基础。 相似文献
13.
利用EOF相空间分析东亚梅雨旱涝长期过程的初步研究 总被引：11，自引：1，他引：11
利用非线性动力学中的相空间概念,分析了梅雨旱涝3～5年循环的长期过程.对全球热带850hPa的纬向风场距平所作的EOF,第1特征向量显示了Walker环流异常在赤道球圈上的分布;第2特征向量主要显示热带-副热带之间的环流异常的经向分布.在第1时间系数和第2时间系数所定义的2维相空间中,由相轨线分析,得到梅雨涝年主要集中于第2象限,而旱年则相对多在第4象限.说明梅雨旱涝年际变异的主导模态和热带大气环流的主要特征向量有着密切的关联.它们显示了以3～5年时间尺度的大气环流演变的长期特征性过程. 相似文献
14.
Fragments of deep-ocean tidal records up to 3 days long belong to the same functional sub-space, regardless of the record’s origin. The tidal sub-space basis can be derived via Empirical Orthogonal Function (EOF) analysis of a tidal record of a single buoy. Decomposition of a tsunami buoy record in a functional space of tidal EOFs presents an efficient tool for a short-term tidal forecast, as well as for an accurate tidal removal needed for early tsunami detection and quantification [Tolkova, E., 2009. Principal component analysis of tsunami buoy record: tide prediction and removal. Dyn. Atmos. Oceans 46 (1–4), 62–82] EOF analysis of a time series, however, assumes that the time series represents a stationary (in the weak sense) process. In the present work, a modification of one-dimensional EOF formalism not restricted to stationary processes is introduced. With this modification, the EOF-based de-tiding/forecasting technique can be interpreted in terms of a signal passage through a filter bank, which is unique for the sub-space spanned by the EOFs. This interpretation helps to identify a harmonic content of a continuous process whose fragments are decomposed by given EOFs. In particular, seven EOFs and a constant function are proved to decompose 1-day-long tidal fragments at any location. Filtering by projection into a reduced sub-space of the above EOFs is capable of isolating a tsunami wave within a few millimeter accuracy from the first minutes of the tsunami appearance on a tsunami buoy record, and is reliable in the presence of data gaps. EOFs with ∼3-day duration (a reciprocal of either tidal band width) allow short-term (24.75 h in advance) tidal predictions using the inherent structure of a tidal signal. The predictions do not require any a priori knowledge of tidal processes at a particular location, except for recent 49.5 h long recordings at the location. 相似文献
15.
This study describes a method of calculating the mean squared error (MSE) incurred when estimating the spherical harmonic
coefficients of a climatological field that is sampled at a small network of points. The method can also be applied to the
coefficients of any other set of orthonormal basis functions that are defined on the sphere. It, therefore, provides a formalism
that can be applied in a variety of contexts, such as in climate change detection, where inferences are attempted about fingerprint
coefficients that are imperfectly estimated from observational data. By incorporating the fingerprint as part of a set of
basis functions, the methodology can be used to estimate the sampling error in the fingerprint coefficient. The MSE is expressed
in terms of the spherical harmonics (or other orthonormal expansion) of the empirical orthogonal functions (EOFs), the locations
of the points in the network and a set of weights that are applied at these points. The weights are optimised by minimising
the expected MSE. The method is applied to a number of network configurations using monthly-mean screen temperature and 500 mb
height simulated by the Canadian Climate Centre 2nd generation general circulation model in an ensemble of six 10-year simulations.
In comparison with uniform weighting, optimal weighting can reduce the MSE by an order of magnitude or more for some spherical
harmonic coefficients and some network configurations. Also, the MSEs vary seasonally for each network. In particular, the
relative MSE of low order spherical harmonic coefficients is found to be larger in DJF than in JJA. We demonstrate how MSEs
improve with increasing network density and identify graphically, the coefficients that can be estimated reliably with each
network configuration.
Received: 19 April 1996 / Accepted: 10 April 1997 相似文献
16.
There is strong evidence that Indian Ocean sea surface temperatures (SSTs) influence the climate variability of Southern Asia and Africa; hence, accurate prediction of these SSTs is a high priority. In this study, we use canonical correlation analysis (CCA) to design empirical models to assess the predictability of tropical Indian Ocean SST from sea level pressure (SLP) and SST themselves with lead-times up to one year. One model uses the first twelve empirical orthogonal functions (EOFs) of SLP over the Indian Ocean using different lead-times to predict SST. A CCA model with EOFs of SST as the predictor at the same lead-times is compared to SLP as a predictor and shows the auto-correlation of the system. A CCA using the first five extended empirical orthogonal functions (EEOFs) of sea level pressure over the Indian Ocean basin for an interval of two years combined with SST EOFs as predictors is found to produce the greatest correlation between forecast and observed SSTs. This model obtains higher skill by explicitly considering the development in time of SLP anomalies in the region. The skill of this model, assessed from retroactive forecasts of an 18 year period, shows improvement relative to other empirical forecasts particularly for the central and eastern Indian Ocean and boreal autumn months preceding the Southern Hemisphere summer rainfall season. This is likely due to the limited domain of this model identifying modes of variability that are more pronounced in these areas during this season. Finally, a nonlinear canonical correlation analysis (NLCCA) derived from a neural network is used to analyze the leading nonlinear modes. These nonlinear modes differ from the linear CCA modes with distinct cold and warm SST phases suggesting a nonlinear relationship between SST and SLP over the tropical Indian Ocean. 相似文献
17.
Based on a combined data set of sea surface temperature, zonal surface wind stress and upper ocean heat content the dynamics of the El Niño phenomenon is investigated. In a reduced phase space spanned by the first four EOFs two different stochastic models are estimated from the data. A nonlinear model represented by a simulated neural network is compared with a linear model obtained with the principal oscillation pattern (POP) analysis. While the linear model is limited to damped oscillations onto a fix point attractor, the nonlinear model recovers a limit cycle attractor. This indicates that the real system is located above the bifurcation point in parameter space supporting self-sustained oscillations. The results are discussed with respect to consistency with current theory. 相似文献
18.
EOF/PCA诊断气象变量场问题的新探讨 总被引：13，自引：3，他引：10
进一步论证了经验正交函数/主分量分析(EOF/PCA)在气象变量场诊断中的物理内涵,证明基于EOF/PCA的R型和Q型展开,可描述为气象变量场主要振荡型分解和主要空间分布型分解两种方案.前者表明,气象变量场的准周期振荡可分解为各主分量的周期振荡,它们各自等价于不同网格点(或站点)以其载荷为权重的迭加周期振荡,因此,气象变量场准周期振荡可视为来自不同周期源(网格点或站点)的准周期振荡逐层叠加的结果;后者表明,气象变量场的水平空间分布可视为各种主要空间分布型的叠加,而Q型展开才是对各种主要空间分布型的正交分解.由此深化了EOF/PCA气象变量场诊断的物理内涵. 相似文献
19.
S. K. Deb H. C. Upadhyaya O. P. Sharma A. Chakraborty 《Meteorology and Atmospheric Physics》2006,94(1-4):43-64
Summary Hindcasts for the Indian summer monsoons (ISMs) of 2002 and 2003 have been produced from an ensemble of numerical simulations
performed with a global model by changing SST. Two sets of ensemble simulations have been produced without vegetation: (i)
by prescribing the weekly observed SST from ECMWF (European Centre for Medium Range Weather Forecasting) analyses, and (ii)
by adding weekly SST anomalies (SSTA) of April to the climatological SST during the simulation period from May to August.
For each ensemble, 10 simulations have been realized with different initial conditions that are prepared from ECMWF data with
five each from April and May analyses of both the years. The predicted June–July monsoon rainfall over the Indian region shows
good agreement with the GPCP (observed) pentad rainfall distribution when 5 member ensemble is taken from May initial conditions.
The All-India June–July simulated rainfall time series matches favourably with the observed time series in both the years
for the five member ensemble from May initial condition but drifts away from observation with April initial conditions. This
underscores the role of initial conditions in the seasonal forecasting. But the model has failed to capture the strong intra-seasonal
oscillation in July 2002. Heating over equatorial Indian Ocean for June 2002 in a particular experiment using 29th May 12
GMT as initial conditions shows some intra-seasonal oscillation in July 2002 rainfall, as in observation. Further evaluation
of the seasonal simulations from this model is done by calculating the empirical orthogonal functions (EOFs) of the GPCP rainfall
over India. The first four EOFs explain more than 80% of the total variance of the observed rainfall. The time series of expansion
coefficients (principal components), obtained by projecting on the observed EOFs, provide a better framework for inter-comparing
model simulations and their evaluation with observed data. The main finding of this study is that the All-India rainfall from
various experiments with prescribed SST is better predicted on seasonal scale as compares to prescribed SST anomalies. This
is indicative of a possible useful seasonal forecasts from a GCM at least for the case when monsoon is going to be good. The
model responses do not differ much for 2002 and 2003 since the evolution of SST during these years was very similar, hence
July rainfall seems to be largely modulated by the other feedbacks on the overall circulation. 相似文献
20.
On the basis of the 1950–2001 NCEP reanalysis data, space-time variability of the surface pressure (SP), surface air temperature (SAT), and precipitation fields in Eurasia is studied in connection with the 1976–1977 climate shift. The effect of the shift manifests itself in the change in the space-time structure of empirical orthogonal functions (EOFs) in all these fields from September to April. For SP and SAT, during this period, only two first EOFs are stable with respect to the climate shift. Also, for SAT and SP, the second EOFs are stable from November to April and from September to December, respectively. For the precipitation field, even the first EOFs are unstable during the whole period, with the exception of January and February. Instability with respect to the climate shift appears first in change in the EOF spatial pattern of the fields. Stability of the first modes of the Eurasian meteorological fields to the 1976–1977 climate shift is caused by a relative stability of the North Atlantic Oscillation, which explains up to 70 and 30% of variance of the first and second EOFs, respectively, of the hydrometeorological fields in the region. 相似文献