共查询到20条相似文献，搜索用时 31 毫秒
1.
Time-Dependent Nonlinear Forcing Singular Vector-Type Tendency Error of the Zebiak-Cane Model 
下载免费PDF全文
下载免费PDF全文 Based on the Zebiak-Cane model, the timedependent nonlinear forcing singular vector （NFSV）-type tendency errors with components of 4 and 12 （denoted by NFSV-4 and NFSV-12） are calculated for predetermined El Nifio events and compared with the constant NFSV （denoted by NFSV-1） from their patterns and resultant prediction errors. Specifically, NFSV-1 has a zonal dipolar sea surface temperature anomaly （SSTA） pattern with negative anomalies in the equatorial eastern Pacific and positive anomalies in the equatorial central-western Pa- cific. Although the first few components in NFSV-4 and NFSV-12 present patterns similar to NFSV-1, they tend to extend their dipoles farther westward; meanwhile, the positive anomalies gradually cover much smaller regions with the lag times. In addition, the authors calculate the predic- tion errors caused by the three kinds of NFSVs, and the results indicate that the prediction error induced by NFSV-12 is the largest, followed by the NFSV-4. However, when compared with the prediction errors caused by random tendency errors, the NFSVs generate significantly larger prediction errors. It is therefore shown that the spatial structure of tendency errors is important for producing large prediction errors. Furthermore, in exploring the tendency errors that cause the largest prediction error for E1 Nifio events, the timedependent NFSV should be evaluated. 相似文献
2.
一个ENSO动力-相似误差订正模式及其后报初检验 总被引：5，自引：1，他引：4
为有效利用历史资料中的相似信息,减小模式误差对ENSO这类跨季节-年际尺度预测问题的影响提高动力模式的预测水平.作者利用一种基于统计相似的模式误差订正方法,以国家气候中心简化海气耦合模式为平台建立了相应的动力-相似误差订正(DAEC)模式,并着重探讨了系统相似程度(全相似或部分相似)、误差重估周期以及相似样本个数等因素对预报效果的影响.结果表明,利用该方法可以有效地改善原有模式的预报性能,其中 "全相似" 比 "部分相似" 更能反映海气耦合系统的相似程度,从而对模式误差做出更为准确的估计,使预报误差明显减小.海洋和大气的误差重估周期对结果也有较大影响,在不同相似程度下分别存在着某种最优配置使得预报效果达到最佳.另外,在对相似样本存在状况及影响的研究中则发现在当前资料长度内整体上只存在着有限个相似样本,在此范围内随着样本取样数目的增加DAEC模式的预报性能逐渐提高. 相似文献
3.
Based on the high-resolution Regional Ocean Modeling System(ROMS) and the conditional nonlinear optimal perturbation(CNOP) method, this study explored the effects of optimal initial errors on the prediction of the Kuroshio large meander(LM) path, and the growth mechanism of optimal initial errors was revealed. For each LM event, two types of initial error(denoted as CNOP1 and CNOP2) were obtained. Their large amplitudes were found located mainly in the upper 2500 m in the upstream region of the LM, i.e., southeast of Kyushu. Furthermore, we analyzed the patterns and nonlinear evolution of the two types of CNOP. We found CNOP1 tends to strengthen the LM path through southwestward extension. Conversely,CNOP2 has almost the opposite pattern to CNOP1, and it tends to weaken the LM path through northeastward contraction.The growth mechanism of optimal initial errors was clarified through eddy-energetics analysis. The results indicated that energy from the background field is transferred to the error field because of barotropic and baroclinic instabilities. Thus, it is inferred that both barotropic and baroclinic processes play important roles in the growth of CNOP-type optimal initial errors. 相似文献
4.
5.
Optimal Precursor Perturbations of El Nino in the Zebiak-Cane Model for Different Cost Functions 下载免费PDF全文
下载免费PDF全文 XU Hui 《大气和海洋科学快报》2014,(4):297-303
Optimal precursor perturbations of El Nino in the Zebiak-Cane model were explored for three different cost functions. For the different characteristics of the eastern-Pacific （EP） El Nino and the central-Pacific （CP） El Nino, three cost functions were defined as the sea surface temperature anomaly （SSTA） evolutions at prediction time in the whole tropical Pacific, the Nino3 area, and the Nino4 area. For all three cost functions, there were two optimal precursors that developed into El Nino events, called Precursor Ⅰ and Precursor Ⅱ. For Precursor Ⅰ, the SSTA component consisted of an east-west （positive-negative） dipole spanning the entire tropical Pacific basin and the thermocline depth anomaly pattern exhibited a tendency of deepening for the whole of the equatorial Pacific. Precursor Ⅰ can develop into an EP-El Nino event, with the warmest SSTA occurring in the eastern tropical Pacific or into a mixed El Nino event that has features between EP-El Nino and CP-El Nino events. For Precursor Ⅱ, the thermocline deepened anomalously in the eastern equatorial Pacific and the amplitude of deepening was obviously larger than that of shoaling in the central and western equatorial Pacific. Precursor Ⅱ developed into a mixed El Nino event. Both the thermocline depth and wind anomaly played important roles in the development of Precursor Ⅰ and Precursor Ⅱ. 相似文献
6.
7.
8.
舍入误差对大气环流模式模拟结果的影响 总被引：6，自引：2，他引：6
此文旨在研究气候数值模式的长期计算时受舍入误差的影响。通过对大气环流谱模式SAMIL采用不同CPU数计算时获得的长时间积分结果进行分析，发现使用不同CPU数进行单精度计算时，其十年平均月平均500 hPa高度场随机误差在正负6～8 gpm范围内，而使用双精度计算时相应的误差为正负3～4 gpm。对于气候平均场而言，作者的试验表明SAMIL在并行计算时由于计算顺序改变而引起的误差在可接受范围之内。然而，虽然舍入误差的全球平均值不大，但其误差分布的差别范围并不小。数值试验得到的不同模拟结果之间误差大小与模拟结果的自身年际变化大小在同样的量级，因此对于“年际变化”这样的问题来说，其影响是不可忽略的，必须要使用集合预报的办法来减小误差的影响。文中列出了3种研究复杂数值模式舍入误差的实验方法，指出其一定条件下的等效性和不同适用范围，对于其他模式的舍入误差影响研究有一定的参考价值。在舍入误差分析的基础上，介绍了一种新型的专门针对舍入误差的集合预报方法（舍入误差平均集合，RME），指出了其在气候模拟研究中的应用价值。 相似文献
9.
With the Zebiak-Cane (ZC) model, the initial error that has the largest effect on ENSO prediction is explored by conditional nonlinear optimal perturbation (CNOP). The results demonstrate that CNOP-type errors cause the largest prediction error of ENSO in the ZC model. By analyzing the behavior of CNOP- type errors, we find that for the normal states and the relatively weak EI Nino events in the ZC model, the predictions tend to yield false alarms due to the uncertainties caused by CNOP. For the relatively strong EI Nino events, the ZC model largely underestimates their intensities. Also, our results suggest that the error growth of EI Nino in the ZC model depends on the phases of both the annual cycle and ENSO. The condition during northern spring and summer is most favorable for the error growth. The ENSO prediction bestriding these two seasons may be the most difficult. A linear singular vector (LSV) approach is also used to estimate the error growth of ENSO, but it underestimates the prediction uncertainties of ENSO in the ZC model. This result indicates that the different initial errors cause different amplitudes of prediction errors though they have same magnitudes. CNOP yields the severest prediction uncertainty. That is to say, the prediction skill of ENSO is closely related to the types of initial error. This finding illustrates a theoretical basis of data assimilation. It is expected that a data assimilation method can filter the initial errors related to CNOP and improve the ENSO forecast skill. 相似文献
10.
With the Zebiak-Cane （ZC） model, the initial error that has the largest effect on ENSO prediction is explored by conditional nonlinear optimal perturbation （CNOP）. The results demonstrate that CNOP-type errors cause the largest prediction error of ENSO in the ZC model. By analyzing the behavior of CNOPtype errors, we find that for the normal states and the relatively weak E1 Nifio events in the ZC model, the predictions tend to yield false alarms due to the uncertainties caused by CNOP. For the relatively strong E1 Nino events, the ZC model largely underestimates their intensities. Also, our results suggest that the error growth of E1 Nifio in the ZC model depends on the phases of both the annual cycle and ENSO. The condition during northern spring and summer is most favorable for the error growth. The ENSO prediction bestriding these two seasons may be the most difficult. A linear singular vector （LSV） approach is also used to estimate the error growth of ENSO, but it underestimates the prediction uncertainties of ENSO in the ZC model. This result indicates that the different initial errors cause different amplitudes of prediction errors though they have same magnitudes. CNOP yields the severest prediction uncertainty. That is to say, the prediction skill of ENSO is closely related to the types of initial error. This finding illustrates a theoretical basis of data assimilation. It is expected that a data assimilation method can filter the initial errors related to CNOP and improve the ENSO forecast skill. 相似文献
11.
不同模式误差方案在集合Kalman滤波土壤湿度同化中的比较试验 总被引：2，自引：2，他引：0
本文主要目的是探讨不同模式误差方案在土壤湿度同化中的性能。基于集合Kalman滤波同化方法和AVIM (Atmosphere-Vegetation Interaction Model) 陆面模式, 利用理想试验对膨胀因子方案 (Covariance Inflation, 简称CI)、 直接随机扰动方案 (Direct Random Disturbance, 简称DRD)、 误差源扰动方案 (Source Random Disturbance, 简称SRD) 等3种模式误差方案的同化效果进行了比较, 讨论了各方案在不同观测误差、 观测层数、 观测间隔情况下的同化性能。试验结果表明在观测误差估计完全准确的情况下, 3种方案都能获得较好的同化效果, 并且SRD方案相对于真值的均方根误差最小。当观测误差估计不准确时, SRD方案的同化效果仍能基本得以保持, 而CI和DRD方案则对观测误差估计更为敏感, 同化效果下降明显。当同化多层观测时, CI和DRD方案由于难以保持不同层观测之间的匹配关系, 同化结果反而变差, 而SRD方案能有效协调同化多层观测, 增加观测层后同化结果有了进一步的改善。当观测时间间隔较大时, CI和DRD方案的同化效果显著下降; 而SRD方案由于包含了一定的误差订正功能, 在观测稀疏时仍能保持较好的同化效果。 相似文献
12.
In this study, the relationship between the limit of predictability and initial error was investigated using two simple chaotic systems:the Lorenz model, which possesses a single characteristic time scale, and the coupled Lorenz model, which possesses two different characteristic time scales. The limit of predictability is defined here as the time at which the error reaches 95% of its saturation level; nonlinear behaviors of the error growth are therefore involved in the definition of the limit of predictability. Our results show that the logarithmic function performs well in describing the relationship between the limit of predictability and initial error in both models, although the coefficients in the logarithmic function were not constant across the examined range of initial errors. Compared with the Lorenz model, in the coupled Lorenz model-in which the slow dynamics and the fast dynamics interact with each other-there is a more complex relationship between the limit of predictability and initial error. The limit of predictability of the Lorenz model is unbounded as the initial error becomes infinitesimally small; therefore, the limit of predictability of the Lorenz model may be extended by reducing the amplitude of the initial error. In contrast, if there exists a fixed initial error in the fast dynamics of the coupled Lorenz model, the slow dynamics has an intrinsic finite limit of predictability that cannot be extended by reducing the amplitude of the initial error in the slow dynamics, and vice versa. The findings reported here reveal the possible existence of an intrinsic finite limit of predictability in a coupled system that possesses many scales of time or motion. 相似文献
13.
根据非线性局部Lyapunov指数的方法, 以Logistic映射和Lorenz系统的试验数据序列为例, 研究了在初始误差存在的情况下, 随机误差对混沌系统可预报性的影响。结果表明: 初始误差和随机误差对可预报期限影响所起的作用大小主要取决于两者的相对大小。当初始误差远大于随机误差时, 系统的可预报期限主要由初始误差决定, 可以不考虑随机误差对预报模式可预报性的影响; 反之, 当随机误差远大于初始误差时, 系统的可预报期限主要由随机误差决定; 当初始误差和随机误差量级相当时, 两者都对系统的可预报期限起重要作用。在后两种情况下, 在考虑初始误差对可预报性影响的同时还必须考虑随机误差的作用。此外, 我们在已知系统精确的控制方程和误差演化方程的条件下, 研究了随机误差对可预报性的影响, 理论所得结果与试验数据所得结果相似。这表明在随机误差较小的情况下, 对系统可预报期限的估计相对准确, 但在随机误差较大的情况下, 可预报期限的估计误差也较大。本文利用三种不同的滤波方法对序列进行了试验, 结果表明, Lanczos高通滤波得到的高频序列与原始加入的噪声序列无论是在强度上还是在演变趋势上都表现得相当一致, 其能有效地去除高频噪音继而提高对系统的可预报期限的估计, 这对实际气象观测资料如何有效地去除噪音具有一定的启发意义。 相似文献
14.
The microphysical ＂three-layer＂ model for stratiform clouds over a midlatitude location in Northwest China is investigated by combining in situ airborne Particle Measuring Systems, Inc. （PMS）, radar measurements, and the NCAR/Penn State Mesoscale Model Version 5 （MM5） simulation with a two-moment microphysics scheme. The coexistence of measured supercooled liquid water and small ice particles produces snow particles below the cloud top in the second layer. Peak number concentration and mean diameter of cloud water and raindrop appear in the third warm layer. A thin dry layer just below the melting layer is also observed. The predicted precipitation is tested by equitable threat score. The melting layer is clearly defined in the radar image and model radar reflectivity output is agreement with the observations. The model results provide features of the microphysical structure for every layer of ＂three-layer＂ model at Yan＇an station. For both observation and model simulation, the ＂three-layer＂ model explains the stratiform precipitation formation completely and comprehensively. 相似文献
15.
为了将格点观测融合产品用于模式预报产品的滚动订正中,获得精准的预报效果,使用国家气象信息中心HRCLDAS（High Resolution China Meteorological Administration Land Data Assimilation System）业务系统产生的高频次格点风场融合产品作为实况资料,采用两种风场模型和8种格点误差订正方案,对模式风预报产品进行订正预报试验,试验选择欧洲中期天气预报中心10 m风预报产品的2017年1月1日—2月28日以及2017年6月1日—7月31日两个时间段,进行了预报模拟试验,对8种格点误差订正方案的订正结果进行检验,同时将订正场插值到站点,使用国家级2400个地面气象站风场资料进行站点检验,结果表明:无论从格点还是站点检验的平均绝对偏差、准确率、绝对偏差分布频率结果看,采用基于模式和实况因子的全格点滑动建模订正方案具有最佳的订正效果。 相似文献
16.
ABSTRACT The impact of both initial and parameter errors on the spring predictability barrier （SPB） is investigated using the Zebiak Cane model （ZC model）. Previous studies have shown that initial errors contribute more to the SPB than parameter errors in the ZC model. Although parameter errors themselves are less important, there is a possibility that nonlinear interactions can occur between the two types of errors, leading to larger prediction errors compared with those induced by initial errors alone. In this case, the impact of parameter errors cannot be overlooked. In the present paper, the optimal combination of these two types of errors [i.e., conditional nonlinear optimal perturbation （CNOP） errors] is calculated to investigate whether this optimal error combination may cause a more notable SPB phenomenon than that caused by initial errors alone. Using the CNOP approach, the CNOP errors and CNOP-I errors （optimal errors when only initial errors are considered） are calculated and then three aspects of error growth are compared： （1） the tendency of the seasonal error growth; （2） the prediction error of the sea surface temperature anomaly; and （3） the pattern of error growth. All three aspects show that the CNOP errors do not cause a more significant SPB than the CNOP-I errors. Therefore, this result suggests that we could improve the prediction of the E1 Nifio during spring by simply focusing on reducing the initial errors in this model. 相似文献
17.
GRAPES_MESO模式对一次强降水过程的预报及误差分析 总被引：2，自引：0，他引：2
本文应用西南低涡大气科学试验加密观测资料,常规探空与地面资料,自动站资料等,分析国家数值预报中心运行的GRAPES_MESO中尺度模式对2010年7月14～19日四川强降水过程预报能力.结果表明,模式降水预报能一定程度反映实况降水.在模式误差分析基础上,指出造成降水预报偏差的可能原因是模式预报的高度场持续偏低,预报低值系统偏强,高值系统偏弱,不利于四川上空的辐合低值系统维持;预报的登陆台风强度偏强,台风外围气流与副高外围环流结合,导致西南低空急流较强,加之,模式预报盆地水汽场在西部偏多,东部偏少,对流层中低层冷空气活动偏弱,暖湿气流活动较强,急流带北移较快,辐合流场位置偏北偏东,导致了积分后期预报降水与实况出现较大偏差,盆地东北部降水偏弱,预报降水落区偏东、偏北.探空分析还指出,盆地测站温度偏差较大,可能是受复杂地形条件下插值误差以及观测误差影响所致,由于盆地测站风向受周边地形影响较大,各站和各层分析风的不确定性较大.误差分析揭示了高度场预报偏低,温度场偏高,地面气压偏低等基本特征,误差的来源需要作进一步的数值试验与动力诊断分析. 相似文献
18.
基于WRF四维变分伴随模式建立数值预报敏感初始误差计算流程并对台风北冕 (0809) 进行了分析。结果表明:基于线性化近似的伴随敏感分析方法对台风系统在24 h内适用。构造敏感初始误差的参考系数存在一个合理的取值范围,参考系数取为0.08效果最好。在初始场中去除敏感初始误差能够有效减少预报误差,改善台风路径预报效果,依据24 h预报误差计算出的敏感初始误差订正对24 h后台风数值预报效果也有明显影响。另外,敏感初始误差分布在台风中心附近,伴随台风系统环流且各物理量分布形态相似。对流层下层和中上层的敏感初始误差均对数值预报效果有所影响,对流层中上层的作用略大于对流层下层。敏感初始误差中各物理量对数值预报改善的贡献各不相同,相对而言,风场的贡献最大。 相似文献
19.
A Case Study of the Error Growth and Predictability of a Meiyu Frontal Heavy Precipitation Event 下载免费PDF全文
下载免费PDF全文 The Advanced Regional Eta-coordinate Model (AREM) is used to explore the predictability of a heavy rainfall event along the Meiyu front in China during 3-4 July 2003.Based on the sensitivity of precipitation prediction to initial data sources and initial uncertainties in different variables,the evolution of error growth and the associated mechanism are described and discussed in detail in this paper.The results indicate that the smaller-amplitude initial error presents a faster growth rate and its growth is characterized by a transition from localized growth to widespread expansion error.Such modality of the error growth is closely related to the evolvement of the precipitation episode,and consequcntly remarkable forecast divergence is found near the rainband,indicating that the rainfall area is a sensitive region for error growth.The initial error in the rainband contributes significantly to the forecast divergence,and its amplification and propagation are largely determined by the initial moisture distribution.The moisture condition also affects the error growth on smaller scales and the subsequent upscale error cascade.In addition,the error growth defined by an energy norm reveals that large error energy collocates well with the strong latent heating,implying that the occurrence of precipitation and error growth share the same energy source-the latent heat.This may impose an intrinsic predictability limit on the prediction of heavy precipitation. 相似文献
20.
Satellite data obtained over synoptic data-sparse regions such as an ocean contribute toward improving the quality of the initial state of limited-area models. Background error covariances are crucial to the proper distribution of satellite-observed information in variational data assimilation. In the NMC (National Meteorological Center) method, background error covariances are underestimated over data-sparse regions such as an ocean because of small differences between different forecast times. Thus, it is necessary to reconstruct and tune the background error covariances so as to maximize the usefulness of the satellite data for the initial state of limited-area models, especially over an ocean where there is a lack of conventional data. In this study, we attempted to estimate background error covariances so as to provide adequate error statistics for data-sparse regions by using ensemble forecasts of optimal perturbations using bred vectors. The background error covariances estimated by the ensemble method reduced the overestimation of error amplitude obtained by the NMC method. By employing an appropriate horizontal length scale to exclude spurious correlations, the ensemble method produced better results than the NMC method in the assimilation of retrieved satellite data. Because the ensemble method distributes observed information over a limited local area, it would be more useful in the analysis of high-resolution satellite data. Accordingly, the performance of forecast models can be improved over the area where the satellite data are assimilated. 相似文献