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1.
基于预报误差最小的自回归模型定阶方法研究 总被引：3，自引：1，他引：3
以独立样本的平均绝对预报误差最小为依据。进行了自回归定型阶方法的研究。简称ＭＡＰＥ方法。应用ＭＡＰＥ方法及五种常用的定阶方法，对上海月平均温度序列进行定阶试验计算和预报检验。结果表明，ＭＡＰＥ方法是一种效果较好的自回归模型定价方法，并且简单实用。 相似文献
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最小二乘支持向量机在云量预报中的应用 总被引：2，自引：2，他引：0
基于2003-2006年逐年1、8月WRF区域数值预报产品和单站观测资料,采用最小二乘支持向量机回归方法,结合选取合适的参数和核函数,分别按月通过不同长度样本序列建立了台北和厦门站总云量和低云量短期释用预报模型,利用2007年1、8月样本资料对模型进行了预报和检验,并与神经网络方法进行了对比.结果表明:最小二乘支持向量机回归方法的预报效果要好于神经网络方法;两站不同长度样本的总云量和低云量预报模型,预报效果较好,其预报准确率不会因为训练样本的减少而降低.可见,最小二乘支持向量机回归在云量等气象要素释用预报方面,具有较好的应用前景. 相似文献
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本文总结了平稳时间序列线性自回归模式拟合的各种方法。线性自回归模式的参数是由Durbin 的逐步递推过程进行估计的,该模式的阶数通过 t 检验或 F 检验予以选定。文中还给出用于月降水量预报的说明性实例。 相似文献
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北京地面气温可预报性及缺测资料恢复的研究 总被引：4，自引：1，他引：4
利用１９５１－１９９０年期间北京地面气温资料作未来气温预测可预报性及缺测气温资料恢复的研究，对单月序列使用自回归、选阶自回归、逐步回归和预测残差最小逐步回归及对多月序列使用后两种模型等６种方案进行试验。结果表明，多月序列使用残差最小逐步回归模型有最好的可预报性，且预报方程具有较高的稳定性。本文使用该方案还对１８４１－１９５０年期间的缺测气温资料进行恢复。 相似文献
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模式输出统计预报是根据数值预报的输出结果,应用统计的工具制做局地气象要素预报的一种较客观、定量的预报方法,它将在基层台站广泛应用。目前,国内外的模式输出统计预报中统计工具普遍采用回归分析的方法。由于回归分析特别是逐步回归的计算量大,对于缺少一定计算条件的台站是难以实现的。因而,计算简单的事件概率回归 相似文献
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应用自激励门限自回归模式对旱涝游程序列的模拟和预报 总被引：3，自引：0，他引：3
在用AR、ARMA等线性模式对气候序列进行拟合和预报时,由于气候序列中存在着非线性变化,所以拟合和预报效果往往不太理想。本文首次用非线性自激励门限自回归模式(SETAR)对由北京511年(1470—1980年)历史旱涝记录变换的湿涝(干旱)游程记录进行了模拟和预报,解决了长期以来预报方程不能随转折点变更的问题。拟合和预报结果表明:门限自回归模式的拟合和预报效果比线性AR模式有明显提高。AR模式只能预报出2年长度以下的游程转折点,而SETAR模式能较准确地预报出3年长度以上的游程转折点。这可能是因为在预报过程中SETAR模式能按游程转折点更新模式,而且模式建立时不要求序列具有平稳性的缘故。 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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利用典型相关分析(CCA)方法建立统计气候预测模型，对我国冬季气温进行了预测试验，采用历史资料独立样本检验的方法，对预报技巧给出合理的评定。结果表明，使用CCA方法对我国冬季气温进行短期气候预测，有一定的预报技巧，对于特定地区和特定时期优选的因子场组合，可以取得较为满意的预报效果。大部分地区的季平均预报时效在2个季以内时，最佳预报相关系数在0.5以上。季平均的预报水平明显高于月平均的预报。海温场是所有因子场中最好的预报因子，不仅单独海温场的预报效果较好，而且与其他因子场组合后的预报水平还可以得到进一步提高。 相似文献
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Forecast skill as a function of time lead and time averaging is examined in two 6-member ensembles of seasonal hindcasts.
One ensemble is produced with the second generation general circulation model of the Canadian Centre for Climate Modelling
and Analysis (GCM2) and the other with a reduced resolution version of the numerical weather prediction model of the Canadian
Meteorological Centre (SEF). The integrations are initiated from the NCEP/NCAR reanalyzed data. Monthly sea surface temperature
anomalies observed prior to the forecast period are maintained throughout the forecast season. A statistical forecast improvement
technique, based on the singular value decomposition of forecast and reanalyzed fields, is discussed and evaluated. A simple
analogue of the hindcast integrations is used to examine the behavior of two common skill scores, the correlation skill score
and the explained variance skill score. The maximal skill score and the corresponding optimal forecast in this analogue are
identified. The total skill of the optimal forecast is a sum of two terms, one associated with the initial conditions and
the other with the lower boundary forcing. The two sources of skill operate on different time scales, with initial conditions
being more important in the first one-two weeks and the atmospheric response to the boundary forcing becoming more dominant
for longer time leads and time averages. This suggests that these sources of skill should be considered separately in forecast
optimization. The statistical technique is moderately successful in improving the skill of monthly to seasonal forecasts of
500 hPa height (Z
500) and 700 hPa temperature (T
700) in the Northern Hemisphere and in the North Pacific/North America sector. The improvement is better when the forecasts for
the first week and for the rest of the season are optimized separately. The SEF model produces better Z
500 and T
700 forecasts than GCM2 in the first one-two weeks whereas GCM2 performs slightly better at longer time leads. The skill of zero
time lead forecast decays rapidly with averaging interval for time averages up to about 30–45 days and stabilizes, or even
rises, for longer time averages. Excluding the first week from seasonal forecasts results in substantial degradation of predictive
skill.
Received: 1 November 1999 / Accepted: 24 May 2000 相似文献
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A new way to predict forecast skill 总被引：1，自引：0，他引：1
Forecast skill (Anomaly Correlated Coefficient, ACC) is a quantity to show the forecast quality of the products of numerical weather forecasting models. Predicting forecast skill, which is the foundation of ensemble forecasting, means submitting products to predict their forecast quality before they are used.Checking the reason is to understand the predictability for the real cases. This kind of forecasting service has been put into operational use by statistical methods previously at the National Meteorological Center (NMC), USA (now called the National Center for Environmental Prediction (NCEP)) and European Center for Medium-range Weather Forecast (ECMWF). However, this kind of service is far from satisfactory because only a single variable is used with the statistical method. In this paper, a new way based on the Grey Control Theory with multiple predictors to predict forecast skill of forecast products of the T42L9 of the NMC, China Meteorological Administration (CMA) is introduced. The results show: (1) The correlation coefficients between “forecasted“ and real forecast skill range from 0.56 to 0.7 at different seasons during the two-year period. (2) The grey forecasting model GM(1,8) forecasts successfully the high peaks, the increasing or decreasing tendency, and the turning points of the change of forecast skill of cases from 5 January 1990 to 29 February 1992. 相似文献
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Paul A. Dirmeyer 《Climate Dynamics》2013,41(3-4):1083-1097
The behavior of the water cycle in the Coupled Forecast System version 2 reforecasts and reanalysis is examined. Attention is focused on the evolution of forecast biases as the lead-time changes, and how the lead-time dependent model climatology differs from the reanalysis. Precipitation biases are evident in both reanalysis and reforecasts, while biases in soil moisture grow throughout the duration of the forecasts. Locally, the soil moisture biases may shrink or reverse sign. These biases are reflected in evaporation and runoff. The Noah land surface scheme shows the necessary relationships between evaporation and soil moisture for land-driven climate predictability. There is evidence that the atmospheric model cannot maintain the link between precipitation and antecedent soil moisture as strongly as in the real atmosphere, potentially hampering prediction skill, although there is better precipitation forecast skill over most locations when initial soil moisture anomalies are large. Bias change with lead-time, measured as the variance across ten monthly forecast leads, is often comparable to or larger than the interannual variance. Skill scores when forecast anomalies are calculated relative to reanalysis are seriously reduced over most locations when compared to validation against anomalies based on the forecast model climate at the corresponding lead-time. When all anomalies are calculated relative to the 0-month forecast, some skill is recovered over some regions, but the complex manner in which biases evolve indicates that a complete suite of reforecasts would be necessary whenever a new version of a climate model is implemented. The utility of reforecast programs is evident for operational forecast systems. 相似文献
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为克服T63模式月动力延伸预报中纬向平均环流的系统性误差较大的情形,文章利用NCEP/NCAR逐候再分析500 hPa高度场资料和非线性时空序列预测理论的局域近似法进行逐候纬向平均高度距平场预报.近30组个例的预报效果分析表明,就1～3旬总体而言,非线性时空序列预测方法对纬向平均高度距平场的预报优于持续性预报和模式动力延伸预报,体现了改善纬向平均高度场的能力.尤其是第3旬的预报,当持续性预报偏差与实况偏差明显增大、动力预报技巧相对于第1旬和第2旬降低时,相空间重构结果仍然保持一定的优势. 相似文献
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A pattern projection downscaling method is employed to predict monthly station precipitation. The predictand is the monthly precipitation at 1 station in China, 60 stations in Korea, and 8 stations in Thailand. The predictors are multiple variables from the output of operational dynamical models. The hindcast datasets span a period of 21 yr from 1983 to 2003. A downscaled prediction is made for each model separately within a leave-one-out cross-validation framework. The pattern projection method uses a moving window, which scans globally, in order to seek the most optimal predictor for each station. The final forecast is the average of the model downscaled precipitation forecasts using the best predictors and is referred to as DMME. It is found that DMME significantly improves the prediction skill by correcting the erroneous signs of the rainfall anomalies in coarse resolution predictions of general circulation models. The correlation coefficient between the prediction of DMME and the observation in Beijing of China reaches 0.71; the skill is improved to 0.75 for Korea and 0.61 for Thailand. The improvement of the prediction skills for the first two cases is attributed to three steps: coupled pattern selection, optimal predictor selection, and multi-model downscaled precipitation ensemble. For Thailand, we use the single-predictor prediction, which results in a lower prediction skill than the other two cases. This study indicates that the large-scale circulation variables, which are predicted by the current operational dynamical models, if selected well, can be used to make skillful predictions of local precipitation by means of appropriate statistical downscaling. 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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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