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1.
This study investigated the regime-dependent predictability using convective-scale ensemble forecasts initialized with different initial condition perturbations in the Yangtze and Huai River basin(YHRB) of East China. The scale-dependent error growth(ensemble variability) and associated impact on precipitation forecasts(precipitation uncertainties) were quantitatively explored for 13 warm-season convective events that were categorized in terms of strong forcing and weak forcing. The forecast error growth in the strong-forcing regime shows a stepwise increase with increasing spatial scale,while the error growth shows a larger temporal variability with an afternoon peak appearing at smaller scales under weak forcing. This leads to the dissimilarity of precipitation uncertainty and shows a strong correlation between error growth and precipitation across spatial scales. The lateral boundary condition errors exert a quasi-linear increase on error growth with time at the larger scale, suggesting that the large-scale flow could govern the magnitude of error growth and associated precipitation uncertainties, especially for the strong-forcing regime. Further comparisons between scale-based initial error sensitivity experiments show evident scale interaction including upscale transfer of small-scale errors and downscale cascade of larger-scale errors. Specifically, small-scale errors are found to be more sensitive in the weak-forcing regime than those under strong forcing. Meanwhile, larger-scale initial errors are responsible for the error growth after 4 h and produce the precipitation uncertainties at the meso-β-scale. Consequently, these results can be used to explain underdispersion issues in convective-scale ensemble forecasts and provide feedback for ensemble design over the YHRB.  相似文献   

2.
The limits of predictability of El Niño and the Southern Oscillation (ENSO) in coupled models are investigated based on retrospective forecasts of sea surface temperature (SST) made with the National Centers for Environmental Prediction (NCEP) coupled forecast system (CFS). The influence of initial uncertainties and model errors associated with coupled ENSO dynamics on forecast error growth are discussed. The total forecast error has maximum values in the equatorial Pacific and its growth is a strong function of season irrespective of lead time. The largest growth of systematic error of SST occurs mainly over the equatorial central and eastern Pacific and near the southeastern coast of the Americas associated with ENSO events. After subtracting the systematic error, the root-mean-square error of the retrospective forecast SST anomaly also shows a clear seasonal dependency associated with what is called spring barrier. The predictability with respect to ENSO phase shows that the phase locking of ENSO to the mean annual cycle has an influence on the seasonal dependence of skill, since the growth phase of ENSO events is more predictable than the decay phase. The overall characteristics of predictability in the coupled system are assessed by comparing the forecast error growth and the error growth between two model forecasts whose initial conditions are 1 month apart. For the ensemble mean, there is fast growth of error associated with initial uncertainties, becoming saturated within 2 months. The subsequent error growth follows the slow coupled mode related the model’s incorrect ENSO dynamics. As a result, the Lorenz curve of the ensemble mean NINO3 index does not grow, because the systematic error is identical to the same target month. In contrast, the errors of individual members grow as fast as forecast error due to the large instability of the coupled system. Because the model errors are so systematic, their influence on the forecast skill is investigated by analyzing the erroneous features in a long simulation. For the ENSO forecasts in CFS, a constant phase shift with respect to lead month is clear, using monthly forecast composite data. This feature is related to the typical ENSO behavior produced by the model that, unlike the observations, has a long life cycle with a JJA peak. Therefore, the systematic errors in the long run are reflected in the forecast skill as a major factor limiting predictability after the impact of initial uncertainties fades out.  相似文献   

3.
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   

4.
混沌系统的局域特征与可预报性   总被引：1，自引：0，他引：1  
李志锦  纪立人 《气象学报》1995,53(3):271-280
讨论了混沌系统的时间和空间的局域特征。首先分析了研究时间和空间局域特征的必要性。接着引进了有限时间不稳定和局域时间不稳定的概念，并对有关的计算问题进行了研究。对Ｌｏｒｅｎｚ系统的具体计算表明，随着轨线在混沌吸引子上的演变，局域不稳定特征有很大的变化，相应误差增长也有很大的变化。相应于误差迅速增长的轨线部分局限于很有限的相空间范围内，而且同误差增长缓慢的轨线部分占据的相空间区域截然可分。每一个例的可预报性依赖于轨线在相空间中所处的区域。混沌系统的这种局域特征可以是导致个例业务预报技巧之间有很大差别的主要原因。  相似文献   

5.
《Atmospheric Research》2010,95(4):736-742
The aim of this work is to evaluate the quality, in terms of location errors, of multi-model poor man's ensemble (PME) forecasts against the single model ones over the Calabria region. Several strategies were adopted to combine precipitation forecasts by three limited area models (LAMs), namely the mean, the median, and a probabilistic matching approach. The Contiguous Rain Area (CRA) analysis was the method selected to detect and quantify the location errors of the forecast precipitation patterns with respect to the corresponding rain gauge-based analyses. Two best-fit criteria, the minimization of mean squared error and the maximization of correlation coefficient, were chosen for matching forecast and observed features. The ability to forecast correctly the precipitation patterns was then quantified by means of a summary measure, the CRA mean shift (CMS). It condenses the outcomes of the twenty-month CRA analyses with a unique value. A bootstrap procedure was applied to test the statistical significance of differences among CMS indices of LAMs and PMEs. Despite the ensemble forecasts display a general improvement, which results in a lower CMS index, with respect to the single LAMs, such improvement was not statistically significant for most ensembles. When the best-fit criterion is the maximization of correlation coefficient, no ensemble was statistically significant better than single models. Instead when the minimization of mean squared error was chosen as best-fit criterion, two out of four PMEs were significantly better than at least a LAM.  相似文献   

6.
The localized features on chaotic attractor in phase space and predictability are investigated in thepresent study.It will be suggested that the localized features in phase space have to be considered indetermining the predictability.The notions of the local instability including the finite-time and local-time instabilities which determine the growth rate of error are introduced,and the calculation methodsare discussed in detail.The results from the calculation of the 3-component Lorenz model show thatsuch instability,correspondingly the growth rate of error,varies dramatically as the trajectoriesevolve on the chaotic attractor.The region in which the growth rate of error is small is localizedconsiderably,and is separable from the region in which the growth rate is large.The localpredictability is of important interest.It is also suggested that such localized features may be the maincause for a great deal of case-to-case variability of the predictive skill in the operational forecasts.  相似文献   

7.
《大气与海洋》2013,51(3):203-215
Abstract

The forecast skill of the Canadian Meteorological Centre (CMC) operational global forecast/analysis system is assessed as a function of scale for the traditional forecast variable of 500‐hPa geopotential height using results from January 2002. These results are compared to an earlier analysis of forecasts from the European Centre for Medium‐range Weather Forecasts (ECMWF) which indicated unexpectedly enhanced skill at high wavenumbers (small scales) especially in the mean forecast component identified with local topographical structures. The global rms error for the CMC forecasts is dominated by the transient component compared to the mean and continues to grow with time during the six days of the forecast. Geographically the transient error grows most rapidly in middle and high latitude regions of large natural variability. The relative error behaves differently and grows most rapidly initially in tropical regions and is inferred to exhibit both climatological and flow‐dependent error growth.

In terms of spherical harmonic two‐dimensional wavenumber n, low wavenumber (large scale) 500‐hPa geopotential height structures are dominated by the mean component but beyond wavenumber 10 to 15 the transient component dominates and exhibits an approximately n–5 spectral slope consistent with a quasi‐two dimensional turbulence enstrophy cascading subrange. Error grows slowly for the large scales dominated by mean climatological structures but these are not of interest for daily weather forecasting. Transient error grows rapidly at small scales and penetrates toward larger scales with time in keeping with the expected predictability behaviour. An expression of the form f(n, τ) = 1 – e–τ/τp(n) is fitted to the growth of relative error as a function of wavenumber and forecast range and gives a scale dependent predictability timescale for the transient component that varies as τp ? n?3/2, although the generality of the relationship is not known.

The mean component at intermediate/high wavenumbers exhibits an apparent region of enhanced skill in the CMC system apparently connected to the topography. The result supports the possibility that some small‐scale mean flow structures, although containing only a minor amount of variance, are maintained in the face of errors in other scales. The results do not support the level of enhanced skill found in an earlier analysis of ECMWF results suggesting them to be an artefact of the analysis/forecast system in use at the time.  相似文献   

8.
The Predictability of a Squall Line in South China on 23 April 2007   总被引：1，自引：0，他引：1  
This study investigated the predictability of a squall line associated with a quasi-stationary front on 23 April 2007 in South China through deterministic and probabilistic forecasts. Our results show that the squall-line simulation was very sensitive to model error from horizontal resolution and uncertainties in physical parameterization schemes. At least a 10-km grid size was necessary to decently capture this squall line. The simulated squall line with a grid size of 4.5 km was most sensitive to long-wave radiation parameterization schemes relative to other physical schemes such as microphysics and planetary boundary layer. For a grid size from 20 to 5 km, a cumulus parameterization scheme degraded the squall-line simulation (relative to turning it off), with a more severe degradation to grid size <10 km than >10 km. The sensitivity of the squall-line simulation to initial error was investigated through ensemble forecast. The performance of the ensemble simulation of the squall line was very sensitive to the initial error. Approximately 15% of the ensemble members decently captured the evolution of the squall line, 25% failed, and 60% dislocated the squall line. Using different combinations of physical parameterization schemes for different members can improve the probabilistic forecast. The lead time of this case was only a few hours. Error growth was clearly associated with moist convection development. A linear improvement in the performance of the squall line simulation was observed when the initial error was decreased gradually, with the largest contribution from initial moisture field.  相似文献   

9.
Within a theoretical ENSO model, the authors investigated whether or not the errors superimposed on model parameters could cause a significant ``spring predictability barrier' (SPB) for El Nino events. First, sensitivity experiments were respectively performed to the air--sea coupling parameter, α and the thermocline effect coefficient μ. The results showed that the uncertainties superimposed on each of the two parameters did not exhibit an obvious season-dependent evolution; furthermore, the uncertainties caused a very small prediction error and consequently failed to yield a significant SPB. Subsequently, the conditional nonlinear optimal perturbation (CNOP) approach was used to study the effect of the optimal mode (CNOP-P) of the uncertainties of the two parameters on the SPB and to demonstrate that the CNOP-P errors neither presented a unified season-dependent evolution for different El Nino events nor caused a large prediction error, and therefore did not cause a significant SPB. The parameter errors played only a trivial role in yielding a significant SPB. To further validate this conclusion, the authors investigated the effect of the optimal combined mode (i.e. CNOP error) of initial and model errors on SPB. The results illustrated that the CNOP errors tended to have a significant season-dependent evolution, with the largest error growth rate in the spring, and yielded a large prediction error, inducing a significant SPB. The inference, therefore, is that initial errors, rather than model parameter errors, may be the dominant source of uncertainties that cause a significant SPB for El Nino events. These results indicate that the ability to forecast ENSO could be greatly increased by improving the initialization of the forecast model.  相似文献   

10.
基于时空不确定性的对流尺度集合预报效果评估检验   总被引：3，自引：0，他引：3  
针对对流尺度天气系统的高度非线性特征和高分辨率模式预报结果存在时、空不确定性现象,以及当前邻域概率法主要考虑高分辨率预报结果的空间位移误差,而不能有效解决预报结果存在时间超前与滞后问题,将时间因素引入到邻域概率法中,结合一次强飑线过程进行对流尺度集合预报试验,并基于改进后的新型邻域概率法与分数技巧评分,对降水预报进行了不同时、空尺度的效果评估检验。结果表明:（1）邻域集合概率法和概率匹配平均法在极端降水的分数技巧评分远高于传统集合平均,弥补了集合平均对极端降水预报能力偏低的缺陷。（2）对于此类飑线过程的对流尺度天气系统而言,邻域半径为15—45 km的空间尺度能够改善降水位移误差的空间不确定性,并使其预报效果达到最优,其中15—30 km的邻域半径对于尺度更小的大量级降水事件预报能力更强。（3）对流尺度降水预报考虑时间尺度与降水强度存在着对应关系,不同时间尺度可以捕获到不同量级降水的时间不确定性。同时,时间尺度与空间尺度对于降水预报效果的影响是相互关联的。（4）改进的邻域概率法能够同时体现高分辨率模式预报结果在对流尺度降水事件上存在的时、空不确定性,实现了对流尺度降水在时、空尺度上的综合评估,并能为不同量级降水提供与其时、空尺度相匹配的概率预报结果。   相似文献   

11.
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   

12.
How to accurately address model uncertainties with consideration of the rapid nonlinear error growth characteristics in a convection-allowing system is a crucial issue for performing convection-scale ensemble forecasts. In this study, a new nonlinear model perturbation technique for convective-scale ensemble forecasts is developed to consider a nonlinear representation of model errors in the Global and Regional Assimilation and Prediction Enhanced System (GRAPES) Convection-Allowing Ensemble Prediction System (CAEPS). The nonlinear forcing singular vector (NFSV) approach, that is, conditional nonlinear optimal perturbation-forcing (CNOP-F), is applied in this study, to construct a nonlinear model perturbation method for GRAPES-CAEPS. Three experiments are performed: One of them is the CTL experiment, without adding any model perturbation; the other two are NFSV-perturbed experiments, which are perturbed by NFSV with two different groups of constraint radii to test the sensitivity of the perturbation magnitude constraint. Verification results show that the NFSV-perturbed experiments achieve an overall improvement and produce more skillful forecasts compared to the CTL experiment, which indicates that the nonlinear NFSV-perturbed method can be used as an effective model perturbation method for convection-scale ensemble forecasts. Additionally, the NFSV-L experiment with large perturbation constraints generally performs better than the NFSV-S experiment with small perturbation constraints in the verification for upper-air and surface weather variables. But for precipitation verification, the NFSV-S experiment performs better in forecasts for light precipitation, and the NFSV-L experiment performs better in forecasts for heavier precipitation, indicating that for different precipitation events, the perturbation magnitude constraint must be carefully selected. All the findings above lay a foundation for the design of nonlinear model perturbation methods for future CAEPSs.  相似文献   

13.
误差非线性的增长理论及可预报性研究   总被引：2，自引：9，他引：2  
丁瑞强  李建平 《大气科学》2007,31(4):571-576
对非线性系统的误差发展方程不作线性化近似，直接用原始的误差发展方程来研究初始误差的发展，提出了误差非线性的增长理论。首先，在相空间中定义一个非线性误差传播算子，初始误差在这个算子的作用下，可以非线性发展成任意时刻的误差；然后，在此基础上，引入了非线性局部Lyapunov指数的概念。由平均非线性局部Lyapunov指数可以得到误差平均相对增长随时间的演变情况；对于一个混沌系统，误差平均相对增长被证明将趋于一个饱和值，利用这个饱和值，混沌系统的可预报期限可以被定量地确定。误差非线性的增长理论可以应用于有限尺度大小初始扰动的可预报性研究，较误差的线性增长理论有明显的优越性。  相似文献   

14.
Many studies have explored the importance and influence of planetary boundary layer processes on tropical cyclones (TCs). However, few studies have focused on the influence of land surface processes on the activity of TCs. To test the effect of initial perturbations of land surface processes on TCs, a land surface process perturbation module is built in a global ensemble prediction system. Ensemble experiments for the TCs that occurred from 12 UTC 22 August to 18 UTC 24 November, 2006 show that consideration of the uncertainties within the land surface process could increase the predictability of the global ensemble prediction system. Detailed analysis on TC Xangsane (2006) indicates that the perturbation of land surface processes may increase the variation of sensible heat flux and latent heat flux. Meanwhile, the effect from land surface perturbation can be transferred to the upper atmosphere, which leads to better TC forecasts.  相似文献   

15.
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   

16.
李俊  杜钧  许建玉  王明欢 《湖北气象》2020,39(2):176-184
针对2018年4月22日发生在湖北西部山地的一次特大暴雨过程,采用降尺度方案和显式对流参数化方案模式,开展了高分辨率对流许可尺度(3 km)的集合预报试验,并对全球集合预报(GEFS)和对流尺度集合预报(SSEF)的降水预报进行了对比评估,结果表明:(1)SSEF集合平均的雨量和落区预报均优于GEFS。(2)SSEF各成员的降水离散度分布更合理,因而具有更优的降水区间预报,其“离散度-误差关系”更优,能更好地给出预报误差的分布及其可能的大小。(3)SSEF的概率预报在所有空间尺度上均优于GEFS,且在短历时强降水上的优势更加明显。由此可见,针对此类山地暴雨过程,对流尺度集合预报相对于全球集合预报具有巨大的改进潜力。  相似文献   

17.
Summary The growth of error energy from initially uncertain states is a characteristic of global forecast models that is absent or markedly diminished in limited area forecasts. The enhanced regional predictability is presently studied with a limited area boundary layer model applied to a European region centered on the Alps. The results are remarkably insensitive to initial data, and a qualitative explanation of this is sought in terms of Thompson's (1957) and Lorenz's (1969) predictability analysis. It appears that the high predictability of regional models is an artifact of the overwhelming role that the prespecification of external boundaries plays in this problem. In cases that Dirichlet boundary conditions are imposed at the perimeter of the limited forecast region, the larger scale flow components, including most of the advecting flow are determined completely independently of internal dynamics and vorticity fluctuations, a condition that does not promote uncertainty growth.The simplest relaxation of this constraint is accomplished by imposing Neumann boundary conditions with zero gradient of forecast variables at the outer boundary. In this case the boundary values depend completely upon the interior forecast, and there is no theoretical reason to expect that error growth should be limited. Nevertheless, present results show that the only significant forecast errors associated with initial uncertainties in these cases are trapped near external boundaries. An explanation of this phenomenon and its generality is discussed. Our forecast results and analysis of error spread from boundaries suggest that topography may enhance local predictability.Although the predictability of a regional boundary layer model is high with respect to initial errors of even rather large magnitude, the same is not true with respect to large uncertainties in the representation of topography and surface, radiative and dissipative effects. Substantial variations of the parameterization of these processes through changes of the model equations produce boundary layer solution divergence with doubling time scales as short as one day. The uncertainty growth associated with smaller (and more realistic) perturbations of these processes remains to be studied.
Über die Voraussagbarkeit von bodennahen Strömungen während ALPEX
Zusammenfassung Das Anwachsen der Fehler auf Grund anfänglich unsicherer Zustände ist ein Charakteristikum globaler Vorhersagemodelle. Diese Beschränkung ist nicht vorhanden oder stark vermindert in Vorhersagen für begrenzte Gebiete. Die verbesserte regionale Vorhersagbarkeit wird gegenwärtig an einem Grenzschichtmodell untersucht, welches auf einen Teil Europas mit den Alpen im Zentrum angewandt wird. Die Ergebnisse sind auffallend im empfindlich gegenüber den Anfangsdaten. Eine qualitative Erklärung dafür kann anhand der Vorhersagbarkeitsanalyse von Thompson (1957) und Lorenz (1969) durchgeführt werden. Die hohe Vorhersagbarkeit regionaler Modelle erscheint als Ergebnis der überwältigenden Rolle, die die Vorgabe der äußeren Ränder in diesem Problem spielt. In Fällen, wo Dirichlet-Randbedingungen an der Peripherie des begrenzten Vorhersagegebietes aufgezwungen werden, erfolgt die Bestimmung der großräumigen Strömungskomponenten inklusive des größten Teils der advektierenden Strömung, unabhängig von der internen Dynamik und den Wirbelfluktuationen. Diese Bedingung fördert das Anwachsen von Unsicherheiten nicht.Die einfachste Lockerung dieser Beschränkung wird durch Einführung von Neumann-Randbedingungen mit der Vorhersagevariablen ohne Gradienten an den äußeren Rändern erreicht. In diesem Fall hängen die Randwerte vollständig von der Vorhersage im Inneren ab und es besteht kein theoretischer Grund, eine Beschränkung des Fehlerwachstums zu erwarten. Dennoch zeigen die gegenwärtigen Ergebnisse, daß die einzigen wesentlichen Vorhersagefehler in Zusammenhang mit Anfangsunsicherheiten in diesen Fällen auf den Randbereich beschränkt sind. Eine Erklärung dieses Phänomens und seine Allgemeingültigkeit wird diskutiert. Unsere Vorhersageergebnisse und die Analyse der Fehlerausbreitung von den Rändern aus legt nahe, daß die topographie die lokale Vorhersagbarkeit verbessern kann.Obwohl die Vorhersagbarkeit eines regionalen Grenzschichtmodells in bezug auf die verhältnismäßig großen Anfangsfehler hoch ist, ist dies nicht so in bezug auf die großen Unsicherheiten in der Wiedergabe der Topographie und der Oberfläche sowie Strahlungs- und dissipativer Effekte. Wesentliche Variationen der Parametrisierung dieser Prozesse durch Änderungen der Modellgleichungen erzeugen Divergenzen in den Grenzschichtlösungen, die sich schon in einem Tag verdoppeln. Das Wachstum der Unsicherheit verbunden mit kleineren (und realistischeren) Störungen dieser Prozesse bleibt noch zu untersuchen.

With 14 Figures  相似文献   

18.
Summary ?The paper deals with an alternative formulation of the so-called NMC (National Meteorological Center, now National Centers for Environmental Prediction) statistics to compute the background error covariance matrix to be used in a mesoscale variational analysis. While the standard method uses differences of forecasts valid for the same time, but starting from different analysis times, the new formulation required the recomputation of the short-term forecast with the initial and lateral boundary data that come from the long-term run. In the frame of a limited-area model, this approach forces the error variances at large scales to decrease drastically, because those scales are controlled by the (constant data) lateral boundary coupling. As a result, the background cost function acts more scale selectively, with an emphasis on medium scales. The analysis increments obtained from the 3D-VAR system show that the analysis increments are sharper and more concentrated with the new formulation, both in single observation and in full observation experiments. This work is part of a wider project for building a variational assimilation system inside the ALADIN model. The complete system should concentrate on mesoscale features and it should not reanalyse those scales that were already treated by the global model (ARPEGE). Some difficulties and perspectives are drawn in the concluding discussion. Received February 12, 2001; revised July 24, 2001  相似文献   

19.
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...  相似文献   

20.
The AREMv2.3 mesoscale numerical model is used to explore storm processes in South China during the pre-rainy season in 2006 by imposing perturbations on the initial fields of physical variables (temperature, humidity, and wind fields). Sensitivity experiments are performed to examine the impacts of initial uncertainties on precipitation, on the error growth, and on the predictability of mesoscale precipitation in South China. The primary conclusion is that inherent initial condition uncertainties can signi...  相似文献   

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