共查询到19条相似文献，搜索用时 125 毫秒
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利用1991-2011年黄淮地区夏季降水、NCEP/NCAR再分析资料和国家气候中心第2代动力气候模式（BCC_CSM1.1m）夏季回报结果,研究黄淮地区夏季降水降尺度预测模型和可预报性来源。诊断发现,黄淮地区夏季降水与同期南亚高压、乌拉尔山附近阻塞高压、西风急流、西太平洋赤道上空200 hPa纬向风场呈明显正相关。分析BCC_CSM1.1m对夏季环流的回报结果发现,模式对200 hPa和500 hPa位势高度场、200 hPa纬向风场和850 hPa经向风场上影响黄淮地区夏季降水的部分关键区域有较好的模拟能力。利用模式预报技巧较高且对黄淮地区夏季降水的影响有物理含义的环流特征作为预测因子,对比预测因子进行独立性筛选前后分别建立的降尺度预测模型发现,黄淮地区夏季降水预测与实况的距平符号一致率由61%提高到72%。预测技巧来源分析发现,降尺度预测能力与BCC_CSM1.1m对影响黄淮地区夏季降水的3个关键因子乌拉尔山附近环流、南亚高压、西太平洋赤道上空西风强弱的预测技巧密切相关,尤其是模式对西太平洋赤道上空西风的模拟能力起到决定性作用。 相似文献
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夏季亚洲对流层温度异常与中国东部夏季降水紧密相关并可能作为降水的有效预报因子。基于欧盟ENSEMBLES计划的季节预测试验耦合模式每年5月1日开始的回报试验，分析了其对1960～2005年夏季亚洲对流层中上层温度（以200～500 hPa厚度替代，简称对流层温度）年际变率的预测结果，发现模式集合平均对夏季亚洲对流层温度年际变率具有较高的预报技巧，可以合理回报其前两个EOF（Empirical Orthogonal Function）主导模态（EOF1、EOF2），只是未能回报出EOF2高纬度的温度异常，模式集合平均预测的第一模态主成分（PC1）和第二模态主成分（PC2）与再分析资料的时间相关系数分别达到0.63和0.77。再分析资料中前两个EOF模态分别由ENSO（El Ni?o–Southern Oscillation）发展年印度夏季降水异常所激发的丝绸之路遥相关波列和ENSO衰减年西北太平洋夏季降水异常对应的太平洋—日本遥相关波列导致。ENSEMBLES计划可以合理预测出相应的海温异常及遥相关波列，进而合理预测出前两个EOF模态。对流层温度PC1和PC2分别表征了欧亚大陆与周围海洋之间的纬向和经向热力对比异常，模式对由PC1的预报技巧远高于前人定义的纬向热力对比的东亚夏季风指数，对前人定义的经向热力对比指数的预测技巧与PC2相当。将PC1和前人定义的经向热力对比指数作为预报因子，建立了中国夏季降水的动力—统计降尺度预测模型，交叉检验的结果表明该预报模型显著提高了东北和长江流域上游夏季降水的预报技巧。本文提出的亚洲对流层温度年际变率的EOF1及PC1，既能较好表征纬向热力对比与中国东部夏季降水显著相关，又能被模式合理预测，可以作为我国中高纬度地区，特别是东北地区降水的重要预测因子之一。 相似文献
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基于山东省123个国家级气象观测站1961—2015年夏季降水资料、1991—2015年NCEP再分析资料,分析了山东夏季降水变化特征及其与大气环流的关系。结果表明,在山东夏季降水偏多（少）时,乌拉尔山阻塞高压偏弱（强）,副热带高压偏强（弱）,南亚高压偏强（弱）,并且200 hPa经向风场有明显的类似丝绸之路遥相关型的波列结构。利用国家气候中心第二代海气耦合模式3月起报的未来夏季海平面气压场建立降尺度预测模型,该模型对山东夏季降水的预测符号一致率达到64%,有一定的预测能力,进一步分析发现,模式对关键区环流因子的模拟预测能力显著影响降尺度预测方法的准确率。 相似文献
4.
中国东部夏季降水与东亚垂直环流结构及其预测试验 总被引：2，自引：1，他引：1
本文在分析中国东部夏季降水的时空分布特征基础上, 从东亚高、中、低层大尺度环流异常着手, 选取对中国东部夏季降水异常有显著影响的大气环流预报因子, 分别应用逐步回归和最优子集回归法两种统计降尺度方法, 以动力气候模式CAM3.1预报输出的大气环流预报因子为基础, 以中国东部夏季降水的典型空间分布型为预报对象, 建立动力与统计相结合的中国东部夏季降水预测模型, 并对1981～2000年的中国东部夏季降水进行回报试验。结果表明: 中国东部夏季降水具有4类典型的空间分布型式, 且具有显著的准2年和年代际尺度振荡周期; 东亚高、中、低层大气环流异常的特定配置, 对东部夏季降水的空间分布型有显著影响; 使用两种降尺度方案建立的动力与统计相结合的预测模型对中国东部夏季降水异常具有一定的预报技巧, 可以在一定程度上提高动力模式对中国东部夏季降水的预报效果。 相似文献
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利用国家气候中心第二代气候模式预测业务系统(BCC-CPSv2)预测产品,引入印度洋海温信号,采用组合降尺度方法建立了西北地区东部汛期降水预测模型。该预测模型对1991—2017年西北地区东部夏季降水的回报技巧较BCC-CPSv2预测技巧显著提高,空间相关系数由0.42提高到0.75,均方根误差明显减小,最多下降达80%。预测模型对降水空间分布型的预测能力较好,很好地回报了典型年份（1987年和2010年）夏季的降水距平百分率分布。通过抓住气象变量的空间分布特征,组合降尺度方法可以修正动力模式产品的预测误差,为西北地区东部夏季降水预测提供科学依据和技术支持,具有较好的应用前景。 相似文献
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基于1982-2017年NCEP_CFSv2（NCEP Climate Forecast System version 2）模式预测资料对黑龙江省夏季降水进行降尺度预测。通过分析黑龙江省夏季降水与同期环流因子的关系、模式对关键区环流因子的预测,选取模式模拟与再分析资料相关较好、黑龙江降水实况与再分析资料关系较好的环流因子作为预测因子,结合最优子集回归法筛选因子,建立降尺度预测模型,最后采用交叉检验法进行预测效果检验和独立样本预测。结果表明：模式降尺度预测与实况的距平符号-致率为69%,6 a独立样本预测中有5 a预测正确,优于目前的业务预测效果。进-步研究发现,在模式能够准确预测环流因子的情况下,模式降尺度可以较好地预测黑龙江省夏季降水的趋势。此外,模式降尺度在拉尼娜年预测效果较好。 相似文献
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针对江苏夏季旱涝和高温热浪等异常气候的预测难题，以江苏夏季站点降水和气温为预测目标，建立了一种基于全球动力模式BCC_CSM1.1（m）和最优可预测气候模态和异常相对倾向（SMART）原理结合的统计降尺度季节气候预测方法。利用历史观测资料和SVD方法提取决定中国夏季降水异常相对倾向的同期热带地区向外长波辐射（Outgoing Longwave Radiation，OLR）和北半球中高纬500 hPa位势高度场异常相对倾向的最优可预测气候模态，并利用逐步回归法构建其与同期江苏站点降水和气温异常相对倾向同期关系的统计降尺度模型；将动力模式对最优可预测气候模态的预测带入统计降尺度模型，实现对区域降水和气温异常相对倾向的预测；最后通过引入观测的近期背景异常实现对降尺度的降水和气温总距平的预测。通过对1991—2019年江苏夏季降水和气温的回报检验表明，本文建立的统计降尺度模型效果较BCC_CSM1.1（m）动力模式的直接预测效果有显著提高，为区域精细化季节气候预测提供了一种有效的手段。 相似文献
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BP-CCA方法用于四川盆地夏季日降水量的可预报性研究 总被引：1，自引：0，他引：1
基于BP-CCA方法,首先讨论了多个因子对四川盆地夏季降水降尺度模型的可预报性,然后选取最佳预报因子并进行集合,最终基于T639模式建立最优多因子降尺度预报模型.结果表明,分别以东亚夏季10m纬向风、700hPa纬向风和700hPa相对湿度为预报因子的降尺度模型对四川盆地夏季降水的预报技巧较高,而将三个因子集合的多因子降尺度预报模型具有更好的预报能力.进一步将该方法应用于T639模式预报的预报因子场,发现多因子降尺度模型对降水的预报效果要优于T639模式直接输出的结果. 相似文献
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内蒙古夏季降水变率的优势模态及其环流特征 总被引：3，自引：1，他引：2
本文基于1961～2013年的中国气象局降水资料以及NCEP/NCAR再分析资料,针对内蒙古地区夏季降水,利用经验正交函数分解法（EOF）和合成分析法,考察了年际尺度上内蒙古地区夏季降水分布的不同类型,并分析其前两个模态的典型环流形势以及相关的春夏季节转换特征。内蒙古夏季降水在年际尺度上主要分为整体一致型和东西反向型。整体一致型模态以贝加尔湖北侧和南侧对流层中上层环流的反位相变化、源于伊朗北部-中亚地区的中纬度遥相关型波列以及东亚地区“东高西低”或“西高东低”的环流形势为主要特征。而东西反向型模态以东欧平原-乌拉尔山东侧-内蒙古东部及东北地区为异常中心的波状环流以及东亚沿岸中高纬地区偶极子环流异常（“北高南低”或“北低南高”）为主要特征。这两种夏季降水模态的正负位相分别对应着亚洲中高纬环流春夏季节转换提前和滞后的情形。这些结论有助于进一步认识内蒙古地区夏季降水异常及其典型环流特征,从而为其预测提供参考依据。 相似文献
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Assessing the Seasonal Predictability of Summer Precipitation over the Huaihe River Basin with Multiple APCC Models 总被引：2，自引：0，他引：2 
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下载免费PDF全文 Seasonal rainfall predictability over the Huaihe River basin is evaluated in this paper on the basis of 23-year(1981-2003) retrospective forecasts by 10 climate models from the Asia-Pacific Economic Cooperation(APEC) Climate Center(APCC) multi-model ensemble(MME) prediction system.It is found that the summer rainfall variance in this basin is largely internal,which leads to lower rainfall predictability for most individual climate models.By dividing the 10 models into three categories according to their sea surface temperature(SST) boundary conditions including observed,predicted,and persistent SSTs,the MME deterministic predictive skill of summer rainfall over Huaihe River basin is investigated.It is shown that the MME is effective for increasing the current seasonal forecast skill.Further analysis shows that the MME averaged over predicted SST models has the highest rainfall prediction skill,which is closely related to model’s capability in reproducing the observed dominant modes of the summer rainfall anomalies in Huaihe River basin.This result can be further ascribed to the fact that the predicted SST MME is the most effective model ensemble for capturing the relationship between the summer rainfall anomalies over Huaihe River basin and the SST anomalies(SSTAs) in equatorial oceans. 相似文献
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A timescale decomposed threshold regression(TSDTR) downscaling approach to forecasting South China early summer rainfall(SCESR) is described by using long-term observed station rainfall data and NOAA ERSST data. It makes use of two distinct regression downscaling models corresponding to the interannual and interdecadal rainfall variability of SCESR.The two models are developed based on the partial least squares(PLS) regression technique, linking SCESR to SST modes in preceding months on both interannual and interdecadal timescales. Specifically, using the datasets in the calibration period 1915–84, the variability of SCESR and SST are decomposed into interannual and interdecadal components. On the interannual timescale, a threshold PLS regression model is fitted to interannual components of SCESR and March SST patterns by taking account of the modulation of negative and positive phases of the Pacific Decadal Oscillation(PDO). On the interdecadal timescale, a standard PLS regression model is fitted to the relationship between SCESR and preceding November SST patterns. The total rainfall prediction is obtained by the sum of the outputs from both the interannual and interdecadal models. Results show that the TSDTR downscaling approach achieves reasonable skill in predicting the observed rainfall in the validation period 1985–2006, compared to other simpler approaches. This study suggests that the TSDTR approach,considering different interannual SCESR-SST relationships under the modulation of PDO phases, as well as the interdecadal variability of SCESR associated with SST patterns, may provide a new perspective to improve climate predictions. 相似文献
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一种分离时间尺度的统计降尺度模型的建立和应用——以华北汛期降水为例 总被引：4，自引：2，他引：2
针对预报量变化中存在受不同物理因子控制的不同时间尺度变率特征, 本文提出了分离时间尺度的统计降尺度模型。应用滤波方法, 将不同尺度的变率分量分开, 在各自对应的时间尺度上利用不同的大尺度气候因子分别建立降尺度模型。华北汛期 (7～8月) 降水具有年际变率和年代际变率, 本文以华北汛期降水为例利用分离时间尺度的统计降尺度模型进行预测研究。采用的预报因子来自海平面气压场、 500 hPa位势高度场、 850 hPa经向风场和海表温度场以及一些已知的大尺度气候指数。利用基于交叉检验的逐步回归法建立模型。结果表明, 年际尺度上, 华北汛期降水与前期6月赤道中东太平洋海温以及同期中国东部的低层经向风密切相关; 年代际尺度上, 在东印度洋—西太平洋暖池海温的作用下, 华北降水与前期6月西南印度洋海平面气压有同步变化关系。年际模型和年代际模型的结果相加得到对总降水量的降尺度结果。1991～2008年的独立检验中, 模型估计的降水和观测降水的相关系数是0.82, 平均均方根误差是14.8%。结合模式的回报资料, 利用降尺度模型对1991～2001年的华北汛期降水进行回报试验。相比于模式直接预测的降水, 降尺度模型预测的结果有明显改进。改进了模式预测中年际变率过小的问题, 与观测降水的相关系数由0.12提高到0.45。 相似文献
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An Experiment of a Statistical Downscaling Forecast Model for Summer Precipitation over China 总被引：1，自引：0，他引：1 下载免费PDF全文
下载免费PDF全文 A combination of the optimal subset regression (OSR) approach, the coupled general circulation model of the National Climate Center (NCC-CGCM) and precipitation observations from 160 stations over China is used to construct a statistical downscaling forecast model for precipitation in summer. Retroactive forecasts are performed to assess the skill of statistical downscaling during the period from 2003 to 2009. The results show a poor simulation for summer precipitation by the NCC- CGCM for China, and the average spatial anomaly correlation coefficient (ACC) is 0.01 in the forecast period. The forecast skill can be improved by OSR statistical downscaling, and the OSR forecast performs better than the NCC-CGCM in most years except 2003. The spatial ACC is more than 0.2 in the years 2008 and 2009, which proves to be relatively skillful. Moreover, the statistical downscaling forecast performs relatively well for the main rain belt of the summer precipitation in some years, including 2005, 2006, 2008, and 2009. However, the forecast skill of statistical downscaling is restricted to some extent by the relatively low skill of the NCC- CGCM. 相似文献
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Predicting Western Pacific Subtropical High Using a Combined Tropical Indian Ocean Sea Surface Temperature Forecast 下载免费PDF全文
下载免费PDF全文 Weather and climate in East China are closely related to the variability of the western Pacific subtropical high（WPSH）, which is an important part of the Asian monsoon system. The WPSH prediction in spring and summer is a critical component of rainfall forecasting during the summer flood season in China. Although many attempts have been made to predict WPSH variability, its predictability remains limited in practice due to the complexity of the WPSH evolution. Many studies have indicated that the sea surface temperature（SST） over the tropical Indian Ocean has a significant effect on WPSH variability. In this paper, a statistical model is developed to forecast the monthly variation in the WPSH during the spring and summer seasons on the basis of its relationship with SST over the tropical Indian Ocean. The forecasted SST over the tropical Indian Ocean is the predictor in this model, which differs significantly from other WPSH prediction methods. A 26-year independent hindcast experiment from 1983 to 2008 is conducted and validated in which the WPSH prediction driven by the combined forecasted SST is compared with that driven by the persisted SST. Results indicate that the skill score of the WPSH prediction driven by the combined forecasted SST is substantial. 相似文献
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An Approach for Improving Short-Term Prediction of Summer Rainfall over North China by Decomposing Interannual and Decadal Variability简 总被引：1，自引：0，他引：1
A statistical downscaling approach was developed to improve seasonal-to-interannual prediction of summer rainfall over North China by considering the effect of decadal variability based on observational datasets and dynamical model outputs.Both predictands and predictors were first decomposed into interannual and decadal components.Two predictive equations were then built separately for the two distinct timescales by using multivariate linear regressions based on independent sample validation.For the interannual timescale,850-hPa meridional wind and 500-hPa geopotential heights from multiple dynamical models＇ hindcasts and SSTs from observational datasets were used to construct predictors.For the decadal timescale,two well-known basin-scale SST decadal oscillation （the Atlantic Multidecadal Oscillation and the Pacific Decadal Oscillation） indices were used as predictors.Then,the downscaled predictands were combined to represent the predicted/hindcasted total rainfall.The prediction was compared with the models＇ raw hindcasts and those from a similar approach but without timescale decomposition.In comparison to hindcasts from individual models or their multi-model ensemble mean,the skill of the present scheme was found to be significantly higher,with anomaly correlation coefficients increasing from nearly neutral to over 0.4 and with RMSE decreasing by up to 0.6 mm d-1.The improvements were also seen in the station-based temporal correlation of the predictions with observed rainfall,with the coefficients ranging from-0.1 to 0.87,obviously higher than the models＇ raw hindcasted rainfall results.Thus,the present approach exhibits a great advantage and may be appropriate for use in operational predictions. 相似文献
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延伸期预报是无缝隙预测系统中的薄弱环节,如何提高灾害天气过程的延伸期预报技巧是国际热点及前沿问题。本研究基于2005年12月—2014年8月的观测/再分析资料,通过奇异值分解方法,揭示了与中国南方低频降水变化高度耦合的热带对流和中纬度波列信号。利用中国气象局参加国际次季节至季节预报计划模式（BCC-CPS-S2Sv2模式,简称BCC S2S模式）的回报数据,对中国南方低频降水异常场进行统计降尺度,构建了一套动力-统计相结合的延伸期降水预测模型。独立预测时段（2014年12月—2019年8月）的结果表明,BCC S2S模式可以提前10—15 d预报中国南方大部分区域的异常降水;提前15—20 d以上预报时,动力-统计结合预报模型对冬季（夏季）华南沿海地区（长江以北地区）的降水时间演变、降水空间分布及极端强降水事件的预报技巧均优于BCC S2S模式。文中提出的思路和方法可广泛应用于其他区域气象要素和极端天气事件的延伸期预报。 相似文献
18.
Chaofan LI Riyu LU Nick DUNSTONE Adam A.SCAIFE Philip E.BETT Fei ZHENG 《大气科学进展》2021,38(12):2055-2066
During June and July of 2020, the Yangtze River basin suffered from extreme mei-yu rainfall and catastrophic flooding. This study explores the seasonal predictability and associated dynamical causes for this extreme Yangtze River rainfall event, based on forecasts from the Met Office GloSea5 operational forecast system. The forecasts successfully predicted above-average rainfall over the Yangtze River basin, which arose from the successful reproduction of the anomalous western North Pacific subtropical high (WNPSH). Our results indicate that both the Indian Ocean warm sea surface temperature (SST) and local WNP SST gradient were responsible for the westward extension of the WNPSH, and the forecasts captured these tropical signals well. We explore extratropical drivers but find a large model spread among the forecast members regarding the meridional displacements of the East Asian mid-latitude westerly jet (EAJ). The forecast members with an evident southward displacement of the EAJ favored more extreme Yangtze River rainfall. However, the forecast Yangtze River rainfall anomaly was weaker compared to that was observed and no member showed such strong rainfall. In observations, the EAJ displayed an evident acceleration in summer 2020, which could lead to a significant wind convergence in the lower troposphere around the Yangtze River basin, and favor more mei-yu rainfall. The model forecast failed to satisfactorily reproduce these processes. This difference implies that the observed enhancement of the EAJ intensity gave a large boost to the Yangtze River rainfall, hindering a better forecast of the intensity of the event and disaster mitigation. 相似文献
19.
In this study,monthly NCEP/NCAR reanalysis data and NOAA ERSST as well as observed precipitation data from 160 stations in China were used to investigate coupled modes affecting the rainfall over China and sea surface temperature (SST) in the Pacific during boreal summertime based on singular value decomposition (SVD) method.The SVD analysis revealed three remarkable coupled modes:rainfall over North China associated with an ENSO-like SST pattern (ENSO-NC),rainfall over the Yangtze River valley associated with SST anomalies in the western tropical Pacific (WTP-YRV),and rainfall over the Yellow River loop valley associated with tropical Pacific meridional mode-like SST pattern (TPMM-YRLV).These coupled SVD modes appear robust and closely correlated with the single field.Furthermore,the covariabilities among of the three coupled modes have different characteristics at the decadal time scale.In addition,the possible atmospheric teleconnections of the coupled rainfall and SST modes were discussed.For the ENSO-NC mode,anomalous low-pressure and high-pressure over the Asian continent induces moisture divergence over North China and reduces summer rainfall there.For the WTP-YRV mode,East Asia-Pacific teleconnection induces moisture convergence over the Yangtze River valley and enhances the summer rainfall there.The TPMM SST and the summer rainfall anomalies over the YRVL are linked by a circumglobal,wave-train-like,atmospheric teleconnection. 相似文献