共查询到19条相似文献，搜索用时 593 毫秒
1.
《大气和海洋科学快报》2016,(5)
MJO模拟及预报是现阶段大气科学研究的前沿问题。本文利用中科院大气物理所大气环流模式(IAP AGCM4.1)的集合回报结果,分析了MJO潜在可预报性及预报技巧。研究表明IAP AGCM4.1对MJO有着较好的潜在可预报性,且集合预报的潜在可预报性要明显优于单样本预报;就MJO的预报技巧而言,集合预报同样优于单样本预报;模式对MJO的预报技巧还显著依赖于预报初始时刻的MJO状态,初始MJO信号越强,模式对MJO的预报技巧也越高,且更接近可预报性的上限。 相似文献
2.
使用世界气象组织季节内至季节尺度（Subseasonal to Seasonal, S2S）预测项目数据库评估了多个集合预报系统在S2S时间尺度对台风的预报能力。评估的时间段为1999—2010年期间每年5月1日—10月31日。为评估S2S时间尺度台风的预报技巧,使用了台风密集度来描述台风的生成及移动状况。台风密集度定义为一段时间内500 km范围内台风出现的概率。台风密集度由6个S2S集合预报系统后报结果计算得出,它们分别由BoM、CMA、ECMWF、JMA、CNRM和NCEP开发使用。这6个预报系统台风密集度的预报技巧评分表明,当预报时效为11~30天时,ECMWF预报系统的评分为正值,比基于气候状态的参考预报能略好地预报台风。 相似文献
3.
热带大气季节内振荡 (Madden-Julian oscillation,MJO) 是次季节-季节时间尺度气候变率的支配模态。它不仅对低纬度地区天气气候产生重要影响,还能够通过经向传播和激发大气遥相关波列对中高纬度地区产生影响,是延伸期尺度最重要的可预报性来源。因此,MJO预报是次季节-季节气候预测中极为重要的部分,近年来受到国际学术界广泛关注。该文回顾了MJO预报发展历史,概述了当前国际上主要科研业务机构的MJO预报发展现状。目前基于统计方法和气候模式的MJO预报研究取得了较大进展,特别是多个耦合气候模式和一种基于时空投影方法的统计模型均能够显著提升MJO预报技巧 (有效预报可达20 d以上)。该文还介绍了中国气象局国家气候中心在MJO预报技术发展和业务系统研制方面的新进展,当前基于第2代大气环流模式的MJO业务预报填补了国内空白,技巧为16~17 d,而耦合气候模式试验的技巧已达到约20 d。总体来看,利用耦合模式预报MJO是未来发展的主要方向,其中,面向MJO的模式初始化和集合预报新方法研究将是关注重点。 相似文献
4.
热带大气低频振荡 (MJO) 和北半球夏季季节内振荡 (BSISO) 对全球范围天气气候事件有重要影响,是次季节-季节 (S2S) 预报最主要的可预报性来源之一。国家气候中心 (BCC) 基于我国完全自主的T639全球分析场数据、风云三号气象卫星射出长波辐射 (OLR) 资料以及BCC第2代大气环流模式系统的实时预报,发展了MJO实时监测预测一体化业务技术,建立了ISV/MJO监测预测业务系统 (IMPRESS1.0),已投入实时业务运行,在全国气象业务系统得到应用。该文着重介绍该系统提供的MJO和BSISO指数监测预测数据和图形产品,并描述了这些业务产品在2015年对MJO典型个例的实时监测预测应用情况。监测分析和预报检验表明,基于我国自主资料的监测结果能够较为准确地表征MJO和BSISO指数的振荡和演变过程,该系统对MJO和BSISO事件分别至少具备16 d和10 d左右的预报技巧。因此,基于IMPRESS1.0的MJO/BSISO监测预测一体化业务产品可为制作延伸期预报提供重要的参考依据。 相似文献
5.
《大气和海洋科学快报》2016,(1)
热带大气季节内振荡(MJO)预报是国际研究热点,我国尚处于起步阶段。近些年国际上MJO预报水平得到大幅提升,主要得益于包含海气耦合过程的气候模式的使用,这其中模式预报初始化和集合扰动生成方法至关重要。本文发展了适用于国家气候中心第二代气候预测业务模式BCC-CSM1.1(m)的MJO初始化方案,并在此基础上提出了基于不同初始化方案形成扰动的集合预报新方法,可以将MJO有技巧预报时效延长到约20天,为次季节-季节预报提供重要依据。 相似文献
6.
《高原气象》2021,(3)
利用北京气候中心(BCC)次季节-季节(Sub-seasonal to Seasonal,S2S)预测系统20年(1994-2013年)回报试验数据,在评估BCC S2S预测系统对中国西南地区夏季降水次季节预报性能基础上,进而采用基于奇异值分解(Singular Value Decomposition,SVD)的误差订正方案对预测结果进行订正。结果表明:BCC S2S预测系统对西南地区夏季降水的次季节预报技巧随起报时间的提前不断下降,在起报时间提前10天以内具有一定预报技巧,而在起报时间提前10天以上基本无技巧,同时存在明显的区域性和年际差异。采用SVD误差订正方案能够较好改善BCC S2S系统对西南地区夏季降水的次季节预测水平,起报时间提前0～10、11～20、21～30天原始预测结果与观测间的异常相关系数分别为0.50,0.31和0.25,订正后分别提高至0.70,0.75和0.70,同时订正后的预测结果与观测间的空间相关系数在起报时间提前0～10天提高了0.3左右,尤其对起报时间提前11～30天的预测结果改进更加明显,空间相关系数提高了0.6左右。 相似文献
7.
MJO对我国降水影响的季节调制和动力-统计降尺度预测 总被引：1，自引：0，他引：1
利用1981—2016年中国区域CN05.1格点降水资料和EAR-Interim再分析资料,研究了季节循环对于热带大气季节内振荡(MJO)对我国降水影响的调制作用,并基于模式对MJO的预报建立了针对延伸期降水的动力-统计降尺度模型。结果表明,MJO对我国季节内降水异常的影响明显受到季节循环的调制。当MJO对流在热带印度洋活跃时,我国降水偏多的区域随季节由南向北推进;当MJO对流位于海洋性大陆地区时,在秋、冬季我国东部和高原大部分地区降水异常偏少,而到了春、夏季该关系反转。MJO对流和基本气流(特别是副热带西风急流)的位置和强度的变化所引起热带外环流响应的不同是造成这种季节性差异的重要原因。模式检验表明,BCC_AGCM2.2对目标候MJO的预报技巧可达18d以上,在此基础上利用模式预报MJO信息构建了随季节演变滚动的MJO动力-统计降尺度预测模型。独立样本检验表明,该模型在较长时效(10~20d)下对MJO高影响区低频降水异常的预报技巧高于模式的直接预报,特别是在MJO活跃时期对降水预报技巧的提升更加明显,这为MJO信号释用提供了新的思路。 相似文献
8.
《气象》2021,(5)
基于1995—2010年四川气象台站降水资料和世界气象组织次季节—季节(S2S)预测计划中8个模式的回报数据,采用命中率、误警率、Heidke技巧评分、误差和偏差5个指标评估分析了各模式对四川汛期极端降水事件的预测能力。结果表明,S2S各模式对四川极端降水的预测技巧整体较低,表现为"低命中率,高误警率,预测值远小于实际值,偏差较大"的特征。各模式的预测技巧随着起报时间的临近而提高,在天气尺度高于次季节尺度。各模式的最高定性预测技巧出现在川西高原南部,最低出现在盆地东部或攀西地区。预测偏差基本呈现出"盆地大,攀西地区次大,川西高原小"的分布特征,最大值均位于盆地西部沿山地区。各模式在汛期各月的预测技巧不同,定性预测技巧在主汛期尤其是盛夏高于其他时段,但定量预测技巧却在盛夏最低。综合定性和定量预测技巧,英国气象局(UKMO)和意大利国家研究委员会大气科学与气候研究所(CNR-ISAC)的模式分别在天气尺度和次季节尺度中对四川极端降水的预测能力较高。分区来看,对于盆地—攀西地区预测能力较高的模式与全省一致。而在川西高原,韩国气象局(KMA)的模式在天气尺度中预测能力较高,澳大利亚气象局(BoM)和CNR-ISAC的模式则在次季节尺度中预测能力较高。 相似文献
9.
利用再分析资料分析了MJO（Madden-Julian Oscillation）不同位相对春季中国东部降水的影响。结果表明：MJO处于位相3时对应长江及其以南地区降水增多,处于位相7时该区域降水减小。当热带MJO对流从位相1东传至位相4,与其相伴的北向辐散辐合流会在印度东北部—长江及副热带西北太平洋地区的对流层中低层产生明显的辐合异常,且在MJO位相3时中国东部的辐合异常达到最大。从Rossby波源角度分析,这种辐合异常会增强对流层中低层气旋性环流,导致MJO处于位相3时长江流域及其以南地区降水增多。同时,利用现代次季节和季节预报业务系统探讨了MJO与降水的关系对降水预报技巧的影响,发现预报降水和再分析产品的相关系数在MJO处于位相3和7时有所增加。 相似文献
10.
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
11.
Xiouhua Fu June-Yi Lee Pang-Chi Hsu Hiroshi Taniguchi Bin Wang Wanqiu Wang Scott Weaver 《Climate Dynamics》2013,41(3-4):1067-1081
The present study assesses the forecast skill of the Madden–Julian Oscillation (MJO) observed during the period of DYNAMO (Dynamics of the MJO)/CINDY (Cooperative Indian Ocean Experiment on Intraseasonal Variability in Year 2011) field campaign in the GFS (NCEP Global Forecast System), CFSv2 (NCEP Climate Forecast System version 2) and UH (University of Hawaii) models, and revealed their strength and weakness in forecasting initiation and propagation of the MJO. Overall, the models forecast better the successive MJO which follows the preceding event than that with no preceding event (primary MJO). The common modeling problems include too slow eastward propagation, the Maritime Continent barrier and weak intensity. The forecasting skills of MJO major modes reach 13, 25 and 28 days, respectively, in the GFS atmosphere-only model, the CFSv2 and UH coupled models. An equal-weighted multi-model ensemble with the CFSv2 and UH models reaches 36 days. Air–sea coupling plays an important role for initiation and propagation of the MJO and largely accounts for the skill difference between the GFS and CFSv2. A series of forecasting experiments by forcing UH model with persistent, forecasted and observed daily SST further demonstrate that: (1) air–sea coupling extends MJO skill by about 1 week; (2) atmosphere-only forecasts driven by forecasted daily SST have a similar skill as the coupled forecasts, which suggests that if the high-resolution GFS is forced with CFSv2 forecasted daily SST, its forecast skill can be much higher than its current level as forced with persistent SST; (3) atmosphere-only forecasts driven by observed daily SST reaches beyond 40 days. It is also found that the MJO–TC (Tropical Cyclone) interactions have been much better represented in the UH and CFSv2 models than that in the GFS model. Both the CFSv2 and UH coupled models reasonably well capture the development of westerly wind bursts associated with November 2011 MJO and the cyclogenesis of TC05A in the Indian Ocean with a lead time of 2 weeks. However, the high-resolution GFS atmosphere-only model fails to reproduce the November MJO and the genesis of TC05A at 2 weeks’ lead. This result highlights the necessity to get MJO right in order to ensure skillful extended-range TC forecasting. 相似文献
12.
MJO prediction in the NCEP Climate Forecast System version 2 总被引：3，自引：0，他引：3
Wanqiu Wang Meng-Pai Hung Scott J. Weaver Arun Kumar Xiouhua Fu 《Climate Dynamics》2014,42(9-10):2509-2520
The Madden–Julian Oscillation (MJO) is the primary mode of tropical intraseasonal climate variability and has significant modulation of global climate variations and attendant societal impacts. Advancing prediction of the MJO using state of the art observational data and modeling systems is thus a necessary goal for improving global intraseasonal climate prediction. MJO prediction is assessed in the NOAA Climate Forecast System version 2 (CFSv2) based on its hindcasts initialized daily for 1999–2010. The analysis focuses on MJO indices taken as the principal components of the two leading EOFs of combined 15°S–15°N average of 200-hPa zonal wind, 850-hPa zonal wind and outgoing longwave radiation at the top of the atmosphere. The CFSv2 has useful MJO prediction skill out to 20 days at which the bivariate anomaly correlation coefficient (ACC) drops to 0.5 and root-mean-square error (RMSE) increases to the level of the prediction with climatology. The prediction skill also shows a seasonal variation with the lowest ACC during the boreal summer and highest ACC during boreal winter. The prediction skills are evaluated according to the target as well as initial phases. Within the lead time of 10 days the ACC is generally greater than 0.8 and RMSE is less than 1 for all initial and target phases. At longer lead time, the model shows lower skills for predicting enhanced convection over the Maritime Continent and from the eastern Pacific to western Indian Ocean. The prediction skills are relatively higher for target phases when enhanced convection is in the central Indian Ocean and the central Pacific. While the MJO prediction skills are improved in CFSv2 compared to its previous version, systematic errors still exist in the CFSv2 in the maintenance and propagation of the MJO including (1) the MJO amplitude in the CFSv2 drops dramatically at the beginning of the prediction and remains weaker than the observed during the target period and (2) the propagation in the CFSv2 is too slow. Reducing these errors will be necessary for further improvement of the MJO prediction. 相似文献
13.
14.
气候系统模式输出结果是当前开展气候预测业务的重要参考依据之一,如何提高气候系统模式输出结果的可信度是改进气候业务预测能力的关键之一。利用1999—2010年NCEP CFSv2模式每日四次预测未来45天的回算数据,分析了集合样本数对模式预测能力的影响。分析结果表明,模式对月平均500 hPa位势高度的预测技巧在热带地区较高,而中高纬度地区较低;模式对500 hPa位势高度时间异常的预测能力优于空间异常。无论是空间异常还是时间异常,随着模式超前时间的增加,预测技巧均逐渐降低,但是在不同区域和不同月份,模式预测技巧随超前时间的变化存在差异。此外,模式预测技巧存在非常大的年际变率。增加集合样本数,对不同月份和不同起报时间预测技巧的稳定度和预测技巧值均有明显正效果,特别是对亚洲中纬度地区改善度较大。增加集合样本数也可以在一定程度上降低模式预测技巧年际变率。集合样本数增加对于500 hPa位势高度空间异常的改进优于时间异常。 相似文献
15.
热带大气季节内振荡(MJO)实时监测预测业务 总被引：8，自引：2，他引：6
参考目前国际上普遍认可的Wheeler和Hendon设计的MJO监测指标,设计了适合开展实时业务监测的MJO计算方法,初步在国家气候中心建立了逐日的MJO实时监测业务,通过与国外同类监测结果的比较分析表明,监测指标可以很好地描述MJO的强度和传播特征,与国外同类监测产品有很好的一致性。另外,引入了两种统计方法进行了针对MJO指数的实时预测,对预测结果的检验表明,对MJO在两周内有较好的预测技巧,其中利用滞后线性回归方法（PCL）的预测技巧要高于自回归模型（ARM）。 相似文献
16.
Prediction skill of the Madden and Julian Oscillation in dynamical extended range forecasts 总被引：1，自引：0，他引：1
The Madden and Julian Oscillation (MJO) is the most prominent mode of intraseasonal variations in the tropical region. It
plays an important role in climate variability and has a significant influence on medium-to-extended ranges weather forecasting
in the tropics. This study examines the forecast skill of the oscillation in a set of recent dynamical extended range forecasts
(DERF) experiments performed by the National Centers for Environmental Prediction (NCEP). The present DERF experiments were
done with the reanalysis version of the medium range forecast (MRF) model and include 50-day forecasts, initialized once-a-day
(0Z) with reanalyses fields, for the period between 1 January, 1985, and 31 December, 1989. The MRF model shows large mean
errors in representing intraseasonal variations of the large-scale circulation, especially over the equatorial eastern Pacific
Ocean. A diagnostic analysis has considered the different phases of the MJO and the associated forecast skill of the MRF model.
Anomaly correlations on the order of 0.3 to 0.4 indicate that skillful forecasts extend out to 5 to 7 days lead-time. Furthermore,
the results show a slight increase in the forecast skill for periods when convective anomalies associated with the MJO are
intense. By removing the mean errors, the analysis shows systematic errors in the representation of the MJO with weaker than
observed upper level zonal circulations. The examination of the climate run of the MRF model shows the existence of an intraseasonal
oscillation, although less intense (50–70%) and with faster (nearly twice as fast) eastward propagation than the observed
MJO. The results indicate that the MRF model likely has difficulty maintaining the MJO, which impacts its forecast. A discussion
of future work to improve the representation of the MJO in dynamical models and assess its prediction is presented.
Received: 28 December 1998 / Accepted: 27 September 1999 相似文献
17.
Harun A. Rashid Harry H. Hendon Matthew C. Wheeler Oscar Alves 《Climate Dynamics》2011,36(3-4):649-661
Predictions of the Madden?CJulian oscillation (MJO) are assessed using a 10-member ensemble of hindcasts from POAMA, the Australian Bureau of Meteorology coupled ocean?Catmosphere seasonal prediction system. The ensemble of hindcasts was initialised from observed atmosphere and ocean initial conditions on the first of each month during 1980?C2006. The MJO is diagnosed using the Wheeler-Hendon Real-time Multivariate MJO (RMM) index, which involves projection of daily data onto the leading pair of eigenmodes from an analysis of zonal winds at 200 and 850?hPa and outgoing longwave radiation (OLR) averaged about the equator. Forecasts of the two component (RMM1 and RMM2) index are quantitatively compared with observed behaviour derived from NCEP reanalyses and satellite OLR using the bivariate correlation skill, root-mean-square error (RMSE), and measures of the MJO amplitude and phase error. Comparison is also made with a simple vector autoregressive (VAR) prediction model of RMM as a benchmark. Using the full hindcast set, we find that the MJO can be predicted with the POAMA ensemble out to about 21?days as measured by the bivariate correlation exceeding 0.5 and the bivariate RMSE remaining below ~1.4 (which is the value for a climatological forecast). The VAR model, by comparison, drops to a correlation of 0.5 by about 12?days. The prediction limit from POAMA increases by less than 2?days for times when the MJO has large initial amplitude, and has little sensitivity to the initial phase of the MJO. The VAR model, on the other hand, shows a somewhat larger increase in skill for times of strong MJO variability and has greater sensitivity to initial phase, with lower skill for times when MJO convection is developing in the Indian Ocean. The sensitivity to season is, however, greater for POAMA, with maximum skill occurring in the December?CJanuary?CFebruary season and minimum skill in June?CJuly?CAugust. Examination of the MJO amplitudes shows that individual POAMA members have slightly above observed amplitude after a spin-up of about 10?days, whereas examination of the MJO phase error reveals that the model has a consistent tendency to propagate the MJO slightly slower than observed. Finally, an estimate of potential predictability of the MJO in POAMA hindcasts suggests that actual MJO prediction skill may be further improved through continued development of the dynamical prediction system. 相似文献
18.
The Madden–Julian Oscillation (MJO)/Boreal Summer Intraseasonal Oscillation (BSISO) has been considered an important climate mode of variability on subseasonal timescales for East Asian summer. However, it is unclear how well the MJO/BSISO indices would serve as guidance for subseasonal forecasts. Using a probabilistic forecast model determined through multiple linear regression (MLR) with MJO, ENSO, and long-term trend as predictors, we examine lagged impacts of each predictor on East Asia extended summer (May–October) climate from 1982 to 2015. The forecast skills of surface air temperature (T2m) contributed by each predictor is evaluated for lead times out to five weeks. We also provide a systematic evaluation of three commonly used, real-time MJO/BSISO indices in the context of lagged temperature impacts over East Asia. It is found that the influence of the trend provides substantial summertime skill over broad regions of East Asia on subseasonal timescales. In contrast, the MJO influence shows regional as well as phase dependence outside the tropical band of the main action centers of the MJO convective anomalies. All three MJO/BSISO indices generate forecasts that yield high skill scores for week 1 forecasts. For some initial phases of the MJO/BSISO, skill reemerges over some regions for lead times of 3–5 weeks. This emergence indicates the existence of windows of opportunity for skillful subseasonal forecasts over East Asia in summer. We also explore the dynamics that contribute to the elevated skills at long lead times over Tibet and Taiwan–Philippine regions following the initial state of phases 7 and 5, respectively. The elevated skill is rooted in a wave train forced by the MJO convective heating over the Arabian Sea and feedbacks between MJO convection and SSTs in Taiwan–Philippine region. Two out of the three commonly used MJO/BSISO indices tend to identify MJO events that evolve consistently in time, allowing them to serve as reliable predictors for subseasonal forecasts for up to 5 weeks.
相似文献19.
This study evaluates performance of Madden–Julian oscillation (MJO) prediction in the Beijing Climate Center Atmospheric General Circulation Model (BCC_AGCM2.2). By using the real-time multivariate MJO (RMM) indices, it is shown that the MJO prediction skill of BCC_AGCM2.2 extends to about 16–17 days before the bivariate anomaly correlation coefficient drops to 0.5 and the root-mean-square error increases to the level of the climatological prediction. The prediction skill showed a seasonal dependence, with the highest skill occurring in boreal autumn, and a phase dependence with higher skill for predictions initiated from phases 2–4. The results of the MJO predictability analysis showed that the upper bounds of the prediction skill can be extended to 26 days by using a single-member estimate, and to 42 days by using the ensemble-mean estimate, which also exhibited an initial amplitude and phase dependence. The observed relationship between the MJO and the North Atlantic Oscillation was accurately reproduced by BCC_AGCM2.2 for most initial phases of the MJO, accompanied with the Rossby wave trains in the Northern Hemisphere extratropics driven by MJO convection forcing. Overall, BCC_AGCM2.2 displayed a significant ability to predict the MJO and its teleconnections without interacting with the ocean, which provided a useful tool for fully extracting the predictability source of subseasonal prediction. 相似文献