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1.
云南地区季降水量和气温的潜在可预报性分析 总被引：1，自引：1，他引：0
利用云南地区42年气候资料,使用低频白噪声延伸法和方差分析方法,估计了该地区季节降水量和季节气温的气候噪声方差和潜在可预报性。分析结果表明：（1）云南季降水量的气候噪声方差随着季节降水量的增加而增加,空间上主要是由南往北减小,夏季降水量的气候噪声方差显著大于其他季节,季气温的气候噪声方差则随着季节气温的减小而增加,空间上春、冬季由东往西减小而夏、秋季由南往北增加;冬季气温的气候噪声方差显著大于其他季节;（2）云南季降水量和季气温的潜在可预报性同样具有显著的季节变化和空间变化,云南春季的降水量和气温的潜在可预报性均显著大于其他季节,夏季降水量和气温的潜在可预报性均较其他三个季节小;春、秋季降水量潜在可预报性西部大于东部,夏季北部大于南部,冬季则是南部大于北部,云南季气温除夏季外均是西部大于东部。（3）季风和冷空气活动可能对云南地区的季降水量和气温的潜在可预报性有重要影响。 相似文献
2.
中国季降水量的气候噪声和潜在可预报性估计 总被引：2，自引：1，他引：1
利用中国130个测站1961—2004年的日降水量资料,使用低频白噪声延伸法和方差分析法估计了中国季降水量的气候噪声方差和潜在可预报性。结果表明：中国季降水量的气候噪声方差由南向北、由沿海向内陆逐渐减小,且有明显的季节变化,夏季最高,其次是春秋季,冬季最小,而且内陆的季节变化比东南沿海的季节变化显著。季降水量的潜在可预报性有较大的季节和区域差异,但总体来说,全国大部分地区的季降水量是潜在可预报的。以绝对误差小于均方差0.68倍作为预测正确标准,全国大部分地区季降水量的预报正确率上限为50%-60%。 相似文献
3.
土壤湿度对东亚夏季气候潜在可预报性影响的数值模拟 总被引：5，自引：0，他引：5
利用全球大气环流模式NCARCAM3进行了在给定的观测海温条件下的22a（1979—2000年）5—8月的2组集合试验。运用方差分析方法,分析了在气候态和年际变化的表层土壤湿度情况下,CAM3模式模拟的东亚夏季气候潜在可预报性及其差异。结果表明：在给定的观测海温条件下,采用气候态的土壤湿度时,CAM3模式模拟的东亚夏季气候的潜在可预报性偏低;而采用年际变化的土壤湿度时,模拟的夏季气候潜在可预报性有所提高,尤其是在中国西北地区;后者模拟的中国西北地区夏季降水和气温的潜在可预报性比前者的模拟结果提高0．1以上。其原因可能是：采用年际变化的土壤湿度时,模式可以更好地模拟出中国西北地区的地表蒸发量和湍流热通量的年际变化,进而使得模式对该地区夏季气候的预报技巧得到提高。 相似文献
4.
使用LASG/IAP GOALS耦合模式中的全球大气环流模式分量AGCMR15L9的计算结果，在其他外强迫维持气候值不变的情况下，用方差分析的方法，以外部方差与总方差之比Re作为衡量标准，考察该模式关于海冰的季节和跨季节潜在可预报性的大小。结果发现，从总体上看，北半球海冰变化所造成的潜在可预报性较小，只有在大气低层的一些气候要素，如温度、湿度的结果中，才存在Re＞0.5的现象。潜在可预报性结果的局地特征比较明显，高值往往发生在海冰年际变率大的区域里。与中低纬海温在中高纬地区的影响相比，不排除海冰的作用更大的可能性。另外，如果分区域看，北半球某些区域的海冰，在若干挑选出的其区域海冰面积发生大异常年份中的潜在可预报性可能会比不做挑选的总体结果要大。这说明北半球某些区域海冰在面积发生较大异常的时候，可能对同期或（及）后期环流有着比较重要的影响。 相似文献
5.
中国区域月平均温度和降水的模式可预报性分析 总被引：8，自引：1，他引：8
基于中国台站降水和温度观测资料、中国气象局国家气候中心月动力延伸预报的回算和预测结果讨论了中国区域月平均温度和降水模式可预报性的时空变化特征。文中以持续性预报来表征中国区域月平均温度和降水受外强迫影响下的可预报性,持续性预报技巧存在明显的年际和年代际变化特征;春末夏初和秋季预报评分相对偏低;在中国区域气候变暖和平均降水强度极值增加的背景下,温度的持续性预报评分有明显提高,降水的持续性预报略有下降。月动力延伸预报对月降水和温度的预报能力也存在明显的年际和年代际变化特征;与持续性预报相比,月动力延伸温度预报总体优于持续性预报,降水预报在初春略差,温度预报在8月相对最低。近20余年,月动力延伸预报相对于持续性预报的温度和降水的均方根误差技巧均大于零,其年际变化表现为模式对降水的预测略有提高。两种预报评估结果的空间分布分析表明月动力延伸预报达到显著性水平的正相关区域总体上比持续性预报的范围大,并基本涵盖了持续性预报的高相关区。原因是可预测信息部分来源于外强迫异常的影响,部分来源于对大气内部动力过程的模拟。 相似文献
6.
短期气候预测的主要对象是气温和降水的月、季平均量和总量，需要做预报是因为它们有年际变率，但是年际变率究竟有多少是可以被预报出来的呢？通常情况下将总的年际变率划分为主要来源于大气下边界条件持续性外源强迫引起的可预报成分和由于大气内部不稳定性产生的日际天气振荡引起的不可预报成分，前者称气候信号，后者称气候噪声，我们用两个成分的方差之比给出潜在可预报性的测度。本文用低频白噪声延伸法估计了吉林省夏季（6～8月）降水量潜在可预报的气候信号方差和天气噪声方差。结果表明，我省各地均存在潜在可预报性信号。以绝对误差小于均方差0．68倍做为预报正确的标准，我省70%的站预报正确率上限达60％以上。 相似文献
7.
中国区域月气候预测方法和预测能力评估 总被引：5，自引：0，他引：5
利用我国台站降水和温度观测资料,评估了BCC_AGCM1.0月动力延伸预报的回算和预测、国家气候中心月气候预测业务统计方法、持续性预报以及业务发布预报对中国区域月气候要素的预测能力。结果表明,业务发布月平均温度和降水预测的技巧平均低于动力方法和统计方法的预测结果。温度的持续性预报和最优气候值统计方法预报技巧高于其它统计预报方法,考虑了综合相似特征的统计方法对降水预测有相对的优势。动力延伸预报的三种超前预报时间的预测结果总体高于统计方法,在月气候预测能力上具有明显优势。月尺度预测动力和统计方法评估的年际变化特征表明月降水和温度的可预报性一般在El Nio状态下较高,而在La Nia发生时偏低。 相似文献
8.
针对当前暴雨预报检验采用二分类事件检验方法存在的双重惩罚导致评分过低,没有考虑到中国暴雨可预报性时、空分布不均,不便于对比分析不同区域暴雨预报能力差异等问题,为了发展基于可预报性的新型暴雨预报评分方法,在综合分析影响预报员暴雨预报信心的主要因素（暴雨气候统计特征、天气影响系统运动尺度特征及数值模式预报能力等）基础上,利用2008—2016年4—10月中国国家气象信息中心5 km×5 km分辨率的多源降水融合格点分析资料、站点降水观测资料和中国国家级业务区域模式降水预报资料以及扩展空间暴雨样本统计方法,构建了一种新型的中国暴雨可预报性综合指数（Synthetic Predictability Index of Heavy Rainfall,以下简称SPI）数学模型,以定量描述中国各区域的暴雨可预报性特征。SPI数学模型由暴雨气候频率、暴雨面积比率和模式暴雨预报成功指数（Threat Score,TS）3个分量组成,计算了2008—2016年4—10月SPI的3个分量及其时、空变化特征。分析结果显示:暴雨面积比率对SPI的时间和空间变化影响最大,两者偏相关系数大于0.9;其次是暴雨气候频率的影响,两者偏相关系数值为0.8左右;第三是模式暴雨预报TS评分的影响,两者的偏相关系数为0.7左右。分析还发现,SPI大值区随季节而变化,空间分布不均匀:4—5月,可预报性大值区主要分布在华南地区;6—7月,主要分布在江淮流域;7月中旬至8月,大值中心从江淮北部移到华北和东北地区;9月,副热带高压南撤,大值中心也相应南撤。 相似文献
9.
我国的降水资源及其稳定性与潜在可预报性（Ⅱ）潜在可预报性 总被引：5，自引：0，他引：5
本文探讨了年降水量气候噪声估计的方法，并利用我国分布较均匀的１６２个测站１９６０－１９９１年降水资料，讨论了年降水量的潜在可预报性，以便进一步研究月，季降水量的可预报性。结果得出；黄河以南和长江流域的广大地区，特别是四川东部和江淮地区，是我国降水量气候噪声最大的地区；华北，西北以及华南地区降水量的潜在可预报性较大黄河以南和长江流域中下游地区降水量的潜在可预报性较小。 相似文献
10.
为了研究中国不同区域气候变化特征,将全国按照气候区域划分为11个气候区,并利用1951—2009年中国194个国家基本/基准站月、年气温和降水观测资料,对全国及每个气候区平均温度及降水量的年和季节变化特征进行分析。结果表明：中国及各地区增温趋势均为极显著增加,尤其近20 a增温速度更快;而2007年成为有记录以来最暖的一年;中国冬季平均温度上升趋势最明显,春季次之,夏季几乎没有变化。中国平均年总降水量20世纪50年代最多,2000年代最少;而华北地区的年降水量减少最快;在四季降水中,中国只有夏季降水量波动略有增加,且各区域降水分布具有明显的南北差异特征。 相似文献
11.
Summary In this paper, based on the data at 162 stations selected over China from 1960 to 1991 the climatic noise and potential predictability of monthly mean temperature have been studied. The method of estimating climatic noise is based on the idea of Yamamoto et al. (1985) and the potential predictability is expressed by the ratio of the estimated inter-annual variation to the estimated natural variation (or climatic noise). Generally the climatic noise of monthly mean temperature increases with latitude and altitude and varies with season. The continental air from Siberia and Mongolia plays a significant role and the ocean acts as an adjustor and a reductor in the climatic noise except for the tropical Pacific ocean in transitional season. The potential predictability is diversified from month to month and one station to another, but generally the monthly mean temperature over China is potentially predictable at statistical significance level 0.10. The results suggest that we could not ask a climate model to predict the climate with satisfactory results worldwide in all seasons and that the regional model could be a hopeful way to predict the climate.With 3 Figures 相似文献
12.
In this paper,based on the data at 70 stations selected evenly over China for 31 years from 1961-1991.three methods to estimate climatic noise have been discussed and then the climatic noise and potential predictability of monthly precipitation(January.July.April and October)have been examined.The estimating of climatic noise is based on the method of Madden and improved methods of Trenberth and Yamamoto et al.(1985).The potential predictability is approximated by the ratio of the estimated interannual variation to the natural variation.Generally.the climatic noise of monthly precipitation over China has obvious seasonal variation and it is greater in summer than in winter,a bit greater in autumn than in spring.In most areas,the climatic noise is prominently decreasing from south to north and from coast to inland.The potential predictability of monthly precipitation also has obvious seasonal and regional difference,but the potential predictability is greater in winter than in summer in most parts of China.Whereas the comparison of spring and autumn is not obvious.Comparing with the method of Madden,the estimated values of climatic noise based on the improved methods of Trenberth and Yamamoto et al.are relatively lower. 相似文献
13.
利用全国518个站1960—2011年逐日气温观测资料和160个站1983—2012年月尺度气温客观预测数据,基于非线性局部Lyapunov指数和非线性误差增长理论,研究中国区域月尺度气温可预报性期限对资料序列长度的依赖性。结果表明:气温可预报性期限对资料序列的长度有一定程度的依赖性,在西北、东北及华中地区尤为明显。平均而言,45年的资料序列长度才能够得到稳定合理的可预报性期限。为了验证气温可预报期限计算结果的可信度,将月尺度气温的可预报性期限与客观气候预测方法的预报评分技巧进行对比,发现两者结果非常一致。其中,由观测资料得到的1月气温的可预报性期限明显低于7月,1月客观气候预测方法的预报评分技巧也明显低于7月,且1月 (7月) 预报评分的空间分布型与1月 (7月) 气温可预报性期限的空间分布型较为一致。因此,利用非线性局部Lyapunov指数和台站逐日观测资料分析气温的可预报性期限结果是可信的。 相似文献
14.
The precipitation is a primary element which directly affects the agricultural production of thecountry with one fifth of the world population.With the economic development the water resourcestress is getting greater.In this paper,based on the data at 162 stations selected evenly over Chinafrom 1960 to 1991 the stability and potential predictability of annual precipitation have been stud-ied.The eastern and southern parts of the country having abundant precipitation enjoy more stableprecipitation.The north and northwest parts of the country where the precipitations are deficienthave unstable precipitations.The potential predictability approximates to the ratio of the estimatedinterannual variance to the climatic noise.Generally the annual precipitation over China is poten-tially predictable.In the area between the Huanghe River and Changjiang River and the east ofnortheastern China the potential predictability is the lowest in the country.In the north and north-west of the country the potential predictability is greater.The southeastern coast has relatively lowvalues of potential predictability.Also,the method of estimating climatic noise of annual precipita-tion has been discussed from the idea of Yamamoto et al.(1985)in order to estimate the potentialpredictability. 相似文献
15.
Estimating the potential predictability of Australian surface maximum and minimum temperature 总被引：1，自引：1，他引：0
A study is made of the potential predictability of seasonal means in Australian surface maximum and minimum temperature using
monthly data from December 1950 to November 2000. Because the usual assumption of stationarity cannot be applied to the observations
at all stations and for all seasons, a modification to an existing methodology is proposed. Here, we show that, to a first
order, monthly mean variances within a season can be modeled by a linear relationship, and inter-monthly correlations can
be assumed to be stationary. The intraseasonal component of variability can then be estimated using monthly data. Removing
the intraseasonal variance from the total interannual variance allows an estimate of the potential predictability to be made.
Surface maximum and minimum temperature has high potential predictability over most of northern Australia in the four main
seasons. However, there is high potential predictability only in some of the four seasons for the centre and south of Australia.
Surface minimum temperature is generally more potentially predictable than surface maximum temperature. The spatial and temporal
patterns of potential predictability are generally consistent with published patterns of hindcast skill from a statistical
forecast scheme. A comparison between the intraseasonal variance of Australian surface maximum and minimum temperature estimated
using the stationary variance assumption and the linear assumptions showed qualitatively and quantitatively similar patterns
of distribution. 相似文献
16.
Climate predictability on interannual to decadal time scales: the initial value problem 总被引：1，自引：1，他引：0
M. Collins 《Climate Dynamics》2002,19(8):671-692
Any initial value forecast of climate will be subject to errors originating from poorly known initial conditions, model imperfections, and by "chaos" in the sense that, even if the initial conditions were perfectly known, infinitesimal errors can amplify and spoil the forecast at some lead time. Here the latter source of error is examined using a "perfect model" approach whereby small perturbations are made to a coupled atmosphere-ocean general circulation model and the spread of nearby model trajectories, on time and space scales appropriate to seasonal-decadal climate variability, is measured to assess the lead time at which the error saturates. The study therefore represents an estimate of the upper limit of the predictability of climate (appropriate to the initial value problem) given a perfect model and near perfect knowledge of the initial conditions. It is found that, on average, surface air temperature anomalies are potentially predictable on seasonal to interannual time scales in the tropical regions and are potentially predictable on decadal time scales over the ocean in the North Atlantic. For mid-latitude surface air temperature anomalies over land, model trajectories rapidly diverge and there is little sign of any potential predictability on time scales greater than a season or so. For mean sea level pressure anomalies, there is potential predictability on seasonal time scales in the tropics, and for some global scale annual-decadal anomalies, although not those associated with the North Atlantic Oscillation. For precipitation, the only potential for predictability is for seasonal time anomalies associated with the El-Niño Southern Oscillation. For the majority of the highly populated regions of the world, climate predictability on interannual to decadal time scales based in the initial value approach is likely to be severely limited by chaotic error growth. It is found however that there can be cases in which the potential predictability can be higher than average indicating that there is perhaps some utility in making initial value forecasts of climate in those regions which show low predictability on average. 相似文献
17.
Recent Advances in Predictability Studies in China （1999-2002） 总被引：10，自引：2，他引：8
Since the last International Union of Geodesy and Geophysics (IUGG) General Assembly (1999), the predictability studies in China have made further progress during the period of 1999-2002. Firstly, three predictability sub-problems in numerical weather and climate prediction are classified, which are concerned with the maximum predictability time, the maximum prediction error, and the maximum allowable initial error, and then they are reduced into three nonlinear optimization problems. Secondly, the concepts of the nonlinear singular vector (NSV) and conditional nonlinear optimal perturbation (CNOP) are proposed,which have been utilized to study the predictability of numerical weather and climate prediction. The results suggest that the nonlinear characteristics of the motions of atmosphere and oceans can be revealedby NSV and CNOP. Thirdly, attention has also been paid to the relations between the predictability and spatial-temporal scale, and between the model predictability and the machine precision, of which the investigations disclose the importance of the spatial-temporal scale and machine precision in the study of predictability. Also the cell-to-cell mapping is adopted to analyze globally the predictability of climate,which could provide a new subject to the research workers. Furthermore, the predictability of the summer rainfall in China is investigated by using the method of correlation coefficients. The results demonstrate that the predictability of summer rainfall is different in different areas of China. Analysis of variance, which is one of the statistical methods applicable to the study of predictability, is also used to study the potential predictability of monthly mean temperature in China, of which the conclusion is that the monthly mean temperature over China is potentially predictable at a statistical significance level of 0.10. In addition,in the analysis of the predictability of the T106 objective analysis/forecasting field, the variance and the correlation coefficient are calculated to explore the distribution characteristics of the mean-square errors.Finally, the predictability of short-term climate prediction is investigated by using statistical methods or numerical simulation methods. It is demonstrated that the predictability of short-term climate in China depends not only on the region of China being investigated, but also on the time scale and the atmospheric internal dynamical process. 相似文献
18.
中国富士苹果种植的气候适宜性研究 总被引：4，自引：0，他引：4
基于中国1981-2010年2084个气象台站资料和203个富士苹果种植区分布的地理数据,利用最大熵(MaxEnt)模型和ArcGIS平台,从物种分布机理与品质两方面研究了影响富士苹果在中国分布的主导气候因子及适宜范围,并对其气候适宜性进行区域划分和评价。结果表明,影响富士苹果在中国分布的主导气候因子有8个,富士苹果地理分布的气候适宜范围分别为年日照时数2000-2500 h、年平均气温7-14℃、≥10℃积温3000-4800℃·d、最冷月平均气温-7-0℃、夏季气温平均日较差8-12℃、年降水量400-800 mm、夏季平均气温20-26℃、夏季平均空气相对湿度60%-78%。中国富士苹果的气候适宜区主要分布在黄土高原、环渤海湾和黄河古道,其中,黄土高原区的陕西、山西和甘肃气候适宜度最高,而山东和河北两省富士苹果规模化种植还有较大发展空间。 相似文献