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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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Spatial and temporal characteristics of wet spells in the Yangtze River Basin from 1961 to 2003 总被引：1，自引：0，他引：1
G.-J. Zhao G. Hörmann N. Fohrer J.-F. Gao J.-Q. Zhai Z.-X. Zhang 《Theoretical and Applied Climatology》2009,98(1-2):107-117
Daily precipitation records of 147 meteorological stations over the Yangtze River Basin have permitted a detailed analysis of the spatio-temporal distribution of wet spells during the period 1961–2003 by distinguishing average daily amount thresholds of 90th and 95th percentiles. The analysis are based on several time series, namely the number of the days in wet spells, the longest wet spell and the precipitation amount in wet spells. Time series trends analyses are compiled for each station by means of the Mann-Kendall test, for four sub-regions. The results show that the annual precipitation in wet spells is higher in the southeast area and the eastern Tibetan Plateau than in the other parts. The longest wet spells are found in the eastern Tibetan Plateau for both the thresholds. The indices in wet spells for most stations have no significant trends. In contrast, only some stations in eastern Tibetan Plateau and the lower Yangtze River Basin increase significantly, while some in the middle reaches show significant decreasing trends. The regional trends analysis presents a noticeable downward trend in the middle Yangtze River Basin and upward trends in the eastern Tibetan Plateau for both 90th and 95th percentiles, however, the upward trend in the lower Yangtze River Basin and downward trends in the upper Jinshajiang River Basin are not significant. 相似文献
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基于数理统计方法的降水量预测模型建立及应用 总被引：1，自引：0，他引：1
降水量是一个随机事件,但在一个相当长的时间段内又有一定的规律性。由于降水过程存在高度随机性和不确定性,很难用物理成因等方法来确定某一时段确切的降水量值。在国内具有代表性的权马尔科夫链预测降水量方法的基础上,结合数理统计的知识,提出了一种改进的预测降水量思路,即对原始降水量序列进行3 a滑动平均,并考虑序列间的相关程度,以减弱原始序列的随机因素,用新序列进行降水量预测的方法,并对北京、延安等5个站点的降水量序列进行了应用检验。检验结果表明,除了在极端年份(如岢岚站点2006年,偏关站点2006、2009年均为枯水年)时预测有较大误差外,其余年份的预测结果比较令人满意,总体上合格率达到80%。由于权马尔科夫链模型建立时在统计学基础上利用了降水量序列的均值和均方差,预测值是在一定概率条件下趋向于某一状态,而极端条件发生的概率较小,因此在预测极端条件时会出现较大误差。 相似文献
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Summary Comparisons between observed and modelled values of surface temperature, surface precipitation and 500 hPa height for the current climate were made for the southeast United States. Daily values and analyses pertinent to impact assessment, were emphasized. For the model, the time-independent 10-year series of values developed by the Geophysical Fluid Dynamics Laboratory general circulation model were used. Observations were drawn from records for various stations and decades within the model grid-cell. Cumulative frequency distributions of temperature indicated both more clustering close to the mean and greater extremes for the model. The model reproduced the seasonal cycle of day-to-day temperature variability, but introduced a phase shift of about four months. One result was an apparent overabundance of hot spells in the model results. For precipitation the model indicated twice as many raindays as were observed, about the same number of days when precipitation exceeded 5 mm, and fewer days with amounts exceeding 10 mm, effectively decreasing the probability of heavy precipitation while enhancing annual totals. In winter the model appeared to represent the results from an aggregation of stations within the grid-cell, but in summer it was closer to individual station results. The model reproduced the seasonal cycle in the height and standard deviation of the 500 hPa surface, with a damped amplitude in both cases.With 9 Figures 相似文献
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Sayang Mohd Deni Abdul Aziz Jemain Kamarulzaman Ibrahim 《Theoretical and Applied Climatology》2009,97(1-2):109-121
The analysis of the daily rainfall occurrence behavior is becoming more important, particularly in water-related sectors. Many studies have identified a more comprehensive pattern of the daily rainfall behavior based on the Markov chain models. One of the aims in fitting the Markov chain models of various orders to the daily rainfall occurrence is to determine the optimum order. In this study, the optimum order of the Markov chain models for a 5-day sequence will be examined in each of the 18 rainfall stations in Peninsular Malaysia, which have been selected based on the availability of the data, using the Akaike’s (AIC) and Bayesian information criteria (BIC). The identification of the most appropriate order in describing the distribution of the wet (dry) spells for each of the rainfall stations is obtained using the Kolmogorov-Smirnov goodness-of-fit test. It is found that the optimum order varies according to the levels of threshold used (e.g., either 0.1 or 10.0 mm), the locations of the region and the types of monsoon seasons. At most stations, the Markov chain models of a higher order are found to be optimum for rainfall occurrence during the northeast monsoon season for both levels of threshold. However, it is generally found that regardless of the monsoon seasons, the first-order model is optimum for the northwestern and eastern regions of the peninsula when the level of thresholds of 10.0 mm is considered. The analysis indicates that the first order of the Markov chain model is found to be most appropriate for describing the distribution of wet spells, whereas the higher-order models are found to be adequate for the dry spells in most of the rainfall stations for both threshold levels and monsoon seasons. 相似文献
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Experimental Simulations of Extreme Precipitation Based on the Multi-Status Markov Chain Model 
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下载免费PDF全文 A multi-status Markov chain model is proposed to produce daily rainfall, and based on which extreme rainfall is simulated with the generalized Pareto distribution (GPD). The simulated daily rainfall shows high precision at most stations, especially in pluvial regions of East China. The analysis reveals that the multistatus Markov chain model excels the bi-status Markov chain model in simulating climatic features of extreme rainfall. Results from the selected six stations demonstrate excellent simulations in the following aspects:standard deviation of monthly precipitation,daily maximum precipitation,the monthly mean rainfall days,standard deviation of daily precipitation and mean daily precipitation, which are proved to be consistent with the observations. A comparative study involving 78 stations in East China also reveals good consistency in monthly mean rainfall days and mean daily maximum rainfall, except mean daily rainfall. Simulation results at the above 6 stations have shown satisfactory fitting capability of the extreme precipitation GPD method. Good analogy is also found between simulation and observation in threshold and return values. As
the errors of the threshold decrease, so do the di?erences between the return and real values. All the above demonstrates the applicability of the Markov chain model to extreme rainfall simulations. 相似文献
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Neural network based daily precipitation generator (NNGEN-P) 总被引：1，自引：0，他引：1
Jean-Philippe Boulanger Fernando Martinez Olga Penalba Enrique Carlos Segura 《Climate Dynamics》2007,28(2-3):307-324
Daily weather generators are used in many applications and risk analyses. The present paper explores the potential of neural network architectures to design daily weather generator models. Focusing this first paper on precipitation, we design a collection of neural networks (multi-layer perceptrons in the present case), which are trained so as to approximate the empirical cumulative distribution (CDF) function for the occurrence of wet and dry spells and for the precipitation amounts. This approach contributes to correct some of the biases of the usual two-step weather generator models. As compared to a rainfall occurrence Markov model, NNGEN-P represents fairly well the mean and standard deviation of the number of wet days per month, and it significantly improves the simulation of the longest dry and wet periods. Then, we compared NNGEN-P to three parametric distribution functions usually applied to fit rainfall cumulative distribution functions (Gamma, Weibull and double-exponential). A data set of 19 Argentine stations was used. Also, data corresponding to stations in the United States, in Europe and in the Tropics were included to confirm the results. One of the advantages of NNGEN-P is that it is non-parametric. Unlike other parametric function, which adapt to certain types of climate regimes, NNGEN-P is fully adaptive to the observed cumulative distribution functions, which, on some occasions, may present complex shapes. On-going works will soon produce an extended version of NNGEN to temperature and radiation. 相似文献
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Understanding variations in precipitation from a variety of aspects is important for the utilization of water resources. Based on daily precipitation records at 98 meteorological stations in Sichuan province, southwestern China, the spatial and temporal changes in wet/dry spells were investigated by using 14 precipitation indices. The Mann–Kendall trend test is used to detect trends in the index series. Results suggest that the decrease of precipitation in central and eastern Sichuan was significant in terms of decreasing tendencies of wet spell indices. However, the decreasing trend of dry spell indices suggested an increase in precipitation in western Sichuan province. A higher risk of droughts can be expected in autumn and wet spell indices in winter and spring are increasing, implying obvious seasonality and seasonal shifts of change in precipitation within this province. Wet/dry spells with short duration were accounted for a large proportion of spells in Sichuan. The occurrence and fractional contribution of short-duration wet spells were increasing. The same trend was found in dry spells with short and moderate duration in Sichuan 相似文献
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Agne Burauskaite-Harju Anders Grimvall Christine Achberger Alexander Walther Deliang Chen 《Theoretical and Applied Climatology》2012,109(3-4):333-343
We develop new techniques to summarise and visualise spatial patterns of coincidence in weather events such as more or less heavy precipitation at a network of meteorological stations. The cosine similarity measure, which has a simple probabilistic interpretation for vectors of binary data, is generalised to characterise spatial dependencies of events that may reach different stations with a variable time lag. More specifically, we reduce such patterns into three parameters (dominant time lag, maximum cross-similarity, and window-maximum similarity) that can easily be computed for each pair of stations in a network. Furthermore, we visualise such three-parameter summaries by using colour-coded maps of dependencies to a given reference station and distance-decay plots for the entire network. Applications to hourly precipitation data from a network of 93 stations in Sweden illustrate how this method can be used to explore spatial patterns in the temporal synchrony of precipitation events. 相似文献
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High-Frequency and Low-Frequency Variability in Stochastic Daily Weather Generator and Its Effect on Agricultural and Hydrologic Modelling 总被引：1，自引：0，他引：1
The high-frequency and low-frequency variabilities, which are often misreproduced by the daily weather generators, have a significant effect on modelling weather-dependent processes. Three modifications are suggested to improve the reproduction of the both variabilities in a four-variate daily weather generator Met&Roll: (i) inclusion of the annual cycle of lag-0 and lag-1 correlations among solar radiation, maximum temperature and minimum temperature, (ii) use of the 3rd order Markov chain to model precipitation occurrence, (iii) applying the monthly generator (based on a first-order autoregressive model) to fit the low-frequency variability. The tests are made to examine the effects of the three new features on (i) a stochastic structure of the synthetic series, and on (ii) outputs from CERES-Wheat crop model (crop yields) and SAC-SMA rainfall-runoff model (monthly streamflow characteristics, distribution of 5-day streamflow) fed by the synthetic weather series. The results are compared with those obtained with the observed weather series.Results: (i) The inclusion of the annual cycle of the correlations has rather ambiguous effect on the temporal structure of the weather characteristics simulated by the generator and only insignificant effect on the output from either simulation model. (ii) Increased order of the Markov chain improves modelling of precipitation occurrence series (especially long dry spells), and correspondingly improves reliability of the output from either simulation model. (iii) Conditioning the daily generator on monthly generator has the most positive effect, especially on the output from the hydrological model: Variability of the monthly streamflow characteristics and the frequency of extreme streamflows are better simulated. (iv) Of the two simulation models, the improvements related to the three modifications are more pronounced in the hydrological simulations. This may be also due to the fact that the crop growth simulations were less affected by the imperfections of the unmodified version of Met&Roll. 相似文献
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Backcasting long-term climate data: evaluation of hypothesis 总被引：1，自引：0，他引：1
Most often than not, incomplete datasets or short-term recorded data in vast regions impedes reliable climate and water studies. Various methods, such as simple correlation with stations having long-term time series, are practiced to infill or extend the period of observation at stations with missing or short-term data. In the current paper and for the first time, the hypothesis on the feasibility of extending the downscaling concept to backcast local observation records using large-scale atmospheric predictors is examined. Backcasting is coined here to contrast forecasting/projection; the former is implied to reconstruct in the past, while the latter represents projection in the future. To assess our hypotheses, daily and monthly statistical downscaling models were employed to reconstruct past precipitation data and lengthen the data period. Urmia and Tabriz synoptic stations, located in northwestern Iran, constituted two case study stations. SDSM and data-mining downscaling model (DMDM) daily as well as the group method of data handling (GMDH) and model tree (Mp5) monthly downscaling models were trained with National Center for Environmental Prediction (NCEP) data. After training, reconstructed precipitation data of the past was validated against observed data. Then, the data was fully extended to the 1948 to 2009 period corresponding to available NCEP data period. The results showed that DMDM performed superior in generation of monthly average precipitation compared with the SDSM, Mp5, and GMDH models, although none of the models could preserve the monthly variance. This overall confirms practical value of the proposed approach in extension of the past historic data, particularly for long-term climatological and water budget studies. 相似文献
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Modelling Wet and Dry Spells with Mixture Distributions 总被引：1，自引：0，他引：1
Summary The object of the study is to develop a discrete precipitation model which is able to simulate local, daily series of precipitation
occurrences. The model is fitted to the observed data of two stations, Szeged and Szombathely, in Hungary (1951–1995), with
pronounced attention to the reproduction of long dry periods, as characteristic features of the climate in Central Europe.
The point of the approach is to model the duration of consecutive dry and wet series, i.e., spells, instead of individual
wet or dry days. After having comparisons of three different aspects performed, the selected precipitation threshold is 0.1 mm.
This threshold keeps the duration of dry and wet periods more or less balanced, whereas the value of the threshold does not
fundamentally influence either the conditional distribution of macrocirculation types or the local weather statistics related
to the so defined wet or dry days. The duration of both wet and dry spells are found to be independent of the length of either
the preceding (opposite) or the last, but one (identical) state. It is also demonstrated that mixed distributions fairly fit
to the wet and dry spells, whereas the simple geometric does not, especially due to the erroneous lack of long dry sequences.
Weighted sum of two geometric distributions, as well as that of one geometric and one Poisson distribution exhibits good fitting
for the dry spells, whereas only the latter one can be advised to employ for the wet periods. Parameters of the distributions
obviously depend on the season and the site, in question.
Received June 30, 1999 Revised February 3, 2000 相似文献
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多模式集合预报及其降尺度技术在东亚夏季降水预测中的应用 总被引：7，自引：2，他引：5
利用动力季节模式输出的匹配域投影技术和多模式集合预报技术对多个国家和城市的站点月平均降水进行预报。预报变量是北京1个站、韩国60个站和曼谷地区8个站点的月平均降水,预报因子是从多个业务动力季节预报模式输出的多个大尺度变量。模式回报数据和站点观测降水数据时段是1983—2003年。降尺度预报降水的技巧是在交叉验证的框架下进行的。匹配域投影方法是设定一个可以活动的窗口在全球范围内大尺度场上进行扫描,寻求与目标站点降水最优化的因子和最相关的区域,目标站点的降水变率就是由该匹配域上大尺度环流场信息决定的。最终预报是用多个降尺度模式预报结果的集合预报(DMME)。多个降尺度模式预报结果的集合预报能显著地提高站点降水的预报技巧。北京站,多个降尺度模式预报结果的集合预报的预报和观测降水的相关系数可以提高到0.71;韩国地区,多个降尺度模式预报结果的集合预报平均技巧提高到0.75;泰国,多个降尺度模式预报结果的集合预报技巧是0.61。 相似文献
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Stochastic weather generators are statistical models that produce random numbers that resemble the observed weather data on which they have been fitted; they are widely used in meteorological and hydrologi- cal simulations. For modeling daily precipitation in weather generators, first-order Markov chain-dependent exponential, gamma, mixed-exponential, and lognormal distributions can be used. To examine the perfor- mance of these four distributions for precipitation simulation, they were fitted to observed data collected at 10 stations in the watershed of Yishu River. The parameters of these models were estimated using a maximum-likelihood technique performed using genetic algorithms. Parameters for each calendar month and the Fourier series describing parameters for the whole year were estimated separately. Bayesian infor- mation criterion, simulated monthly mean, maximum daily value, and variance were tested and compared to evaluate the fitness and performance of these models. The results indicate that the lognormal and mixed-exponential distributions give smaller BICs, but their stochastic simulations have overestimation and underestimation respectively, while the gamma and exponential distributions give larger BICs, but their stochastic simulations produced monthly mean precipitation very well. When these distributions were fitted using Fourier series, they all underestimated the above statistics for the months of June, July and August. 相似文献
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研究汛期短时强降水特征,对于南方低山丘陵地区山洪灾害的预报具有重要指导意义。以怀化市为研究区域,基于该区域11个国家站和403个区域自动气象站的2012-2017年4-9月期间逐小时降水量以及相对应的NCEP资料,分析了怀化市短时强降水的时空分布特征,得出了产生短时强降水天气系统模型,结果显示：①汛期短时强降水发生频率较高,时间集中,分布不均。主要出现在5～7月,占4～9月的72.9%,其次在8～9月;北部频数多,中南部少,西部最少,辰溪、麻阳和怀化三县交界处及沅陵县的大合坪附近是频发区域。②短时强降水日变化呈单峰型,4～10时最容易发生,峰值在8时,谷值在23时。③强度越强出现的频次越少;北部的强度和次数大于其它区域;50～79.9 mm/h,占总站数的68.4%;各月国家站的极值乘以2约等于区域站极值。④低涡型短时强降水出现概率最高,低涡位置和移动路径是短时强降水预报的关键点。 相似文献
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Summary Wet and dry spell properties of monthly rainfall series at five meteorology stations in Turkey are examined by plotting successive
wet and dry month duration versus their number of occurrences on the double-logarithmic paper. Straight line relationships
on such graphs show that power-laws govern the pattern of successive persistent wet and dry monthly spells. Functional power
law relationships between the number of dry and wet spells for a given monthly period are derived from the available monthly
precipitation data. The probability statements for wet and dry period spells are obtained from the power law expressions.
Comparison of power-law behaviours at five distinct sites in Turkey provides useful interpretation about the temporal and
spatial rainfall pattern. As in temperate areas such as Turkey the rainfall amounts change mostly due to one-month-long dry
or wet spells.
Received August 29, 1995 Revised November 9, 1995 相似文献