Insights into Convective-scale Predictability in East China: Error Growth Dynamics and Associated Impact on Precipitation of Warm-Season Convective Events

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.

Mesoscale Predictability of Moist Baroclinic Waves: Variable and Scale-dependent Error Growth

This study seeks to quantify the predictability of different forecast variables at various scales through spectral analysis of the difference between perturbed and unperturbed cloud-permitting simulations of idealized moist baroclinic waves amplify- ing in a conditionally unstable atmosphere. The error growth of a forecast variable is found to be strongly associated with its reference-state （unperturbed） power spectrum and slope, which differ significantly from variable to variable. The shallower the reference state spectrum, the more spectral energy resides at smaller scales, and thus the less predictable the variable since the error grows faster at smaller scales before it saturates. In general, the variables with more small-scale components （such as vertical velocity） are less predictable, and vice versa （such as pressure）. In higher-resolution simulations in which more rigorous small-scale instabilities become better resolved, the error grows faster at smaller scales and spreads to larger scales more quickly before the error saturates at those small scales during the first few hours of the forecast. Based on the reference power spectrum, an index on the degree of lack （or loss） of predictability （LPI） is further defined to quantify the predictive time scale of each forecast variable. Future studies are needed to investigate the scale- and variable-dependent predictability under different background reference flows, including real case studies through ensemble experiments.

Optimal Initial Error Growth in the Prediction of the Kuroshio Large Meander Based on a High-resolution Regional Ocean Model

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.

A Case Study of the Error Growth and Predictability of a Meiyu Frontal Heavy Precipitation Event

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 consequently 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.

THE INSTABILITY OF TRAJECTORY AND ERROR GROWTH

The purpose of this paper is to study the dynamical mechanism of error growth in the numerical weather prediction. The error is defined in the sense of generalized energy,simply called energy error.From the spectral form of the primi- tive equations,we have derived the evolution equations of error in detail.The analyses of these equations have shown that the error growth rate is determined by the tangent linear equations.The nonlinear advection caused by the error perturbation itself contributes nothing to the error growth rate,and only redistributes the error.Furthermore,an ap- proach to calculation of the error growth rate has been developed,which can also be used to study the local instability of time-independent basic state as well as time-dependence basic state.This approach is applied to well-known Lorenz's system,and the results are indicative of the correctness and significance of the theoretical analyses.

ERROR GROWTH IN NUMERICAL PREDICTION AND ATMOSPHERIC PREDICTABILITY

The article is to report some results of numerical experiments on the error growth and the atmospheric predictability Experiments with two-level global baroclinic primitive equation spectral model have main results as follows.The magnitude of initial errors directly affects the error growth,but its distribution form has little effect on the growth.The loss of predictability resulting from small-scale error is much greater than that from large-scale error.The small-scale error rapidly grows and is transferred to the large-scale error by interaction between different scale waves,which stimulates the growth of error for the whole system Orographic forcing restrains planetary-scale error(wavenumbers 0—3)but enhances the small-scale error (wavenumbers 8 or greater).Hence,orographic effects on the error growth closely depend on the characteris- tic scale of initial errors,and there may be a critical wavenumber between 4 and 7.The error growth is great- er in Northern Hemisphere than in Southern Hemisphere if initial errors are the same.In the end we give some discussions about model,initialization scheme,etc.,to improve model prediction.

Is Model Parameter Error Related to a Significant Spring Predictability Barrier for El Nio events? Results from a Theoretical Model

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 Nio 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 Nio 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 Nio events. These results indicate that the ability to forecast ENSO could be greatly increased by improving the initialization of the forecast model.

Role of Parameter Errors in the Spring Predictability Barrier for ENSO Events in the Zebiak–Cane Model

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.

Which Features of the SST Forcing Error Most Likely Disturb the Simulated Intensity of Tropical Cyclones?

Among all of the sources of tropical cyclone(TC) intensity forecast errors, the uncertainty of sea surface temperature(SST) has been shown to play a significant role. In the present study, we determine the SST forcing error that causes the largest simulation error of TC intensity during the entire simulation period by using the WRF model with time-dependent SST forcing. The SST forcing error is represented through the application of a nonlinear forcing singular vector(NFSV)structure. For the selected 12 TC cases, the NFSV-type SST forcing errors have a nearly coherent structure with positive(or negative) SST anomalies located along the track of TCs but are especially concentrated in a particular region. This particular region tends to occur during the specific period of the TCs life cycle when the TCs present relatively strong intensity, but are still intensifying just prior to the mature phase, especially within a TC state exhibiting a strong secondary circulation and very high inertial stability. The SST forcing errors located along the TC track during this time period are verified to have the strongest disturbing effect on TC intensity simulation. Physically, the strong inertial stability of TCs during this time period induces a strong response of the secondary circulation from diabatic heating errors induced by the SST forcing error. Consequently, this significantly influences the subsidence within the warm core in the eye region, which,in turn, leads to significant errors in TC intensity. This physical mechanism explains the formation of NSFV-type SST forcing errors. According to the sensitivity of the NFSV-type SST forcing errors, if one increases the density of SST observations along the TC track and assimilates them to the SST forcing field, the skill of TC intensity simulation generated by the WRF model could be greatly improved. However, this adjustment is most advantageous in improving simulation skill during the time period when TCs become strong but are still intensifying just prior to reaching full maturity. In light of this, the region along the TC track but in the time period of TC movement when the NFSV-type SST forcing errors occur may represent the sensitive area for targeting observation for SST forcing field associated with TC intensity simulation.

金融深化、技术创新与经济增长的关系研究——以宁夏为例

金融是现代经济的血脉,创新是引领发展的第一动力。通过金融深化与技术创新驱动经济增长,是加快经济发展的科学有效途径。本文运用协整检验和向量误差修正模型,并引入脉冲响应函数,以宁夏为例研究金融深化、技术创新和经济增长之间的长期稳定关系和短期调整关系,以期为各地建设现代化经济体系提供一定的参考。实证结果表明,金融深化对技术创新有正向促进作用,且对技术创新的影响呈上升趋势;金融深化和技术创新是影响宁夏经济增长的重要因素,金融深化的两个指标变量与经济增长存在长期均衡关系,但作用方向不同,金融相关率与经济增长正相关,而货币化率与经济增长负相关。

CMA全球集合预报系统误差增长及预报性能的尺度依赖特征诊断分析

利用CMA全球集合预报(CMA-GEPS)业务系统2020年6月1日至2021年5月31日一整年的500 hPa位势高度场(H_(500))预报数据,诊断评估了CMA-GEPS在北半球地区误差增长及预报性能的尺度依赖特征。使用谱滤波方法实现H_(500)不同尺度(包括行星尺度、天气尺度与次天气尺度)分量的分离。从集合平均均方根误差(简称集合平均误差)-离散度关系来看,在预报前期(108 h之前),CMA-GEPS集合平均误差小于集合离散度,存在过度发散的问题,主要是由天气尺度分量离散度过大导致;在预报后期(108 h之后),CMA-GEPS集合平均误差大于集合离散度,离散度偏小,是由行星尺度与天气尺度分量离散度不足共同引起。采用Dalcher等1987年修订的误差增长模型对H_(500)集合平均预报误差增长特征进行诊断分析,发现CMA-GEPS误差增长过程合理,初始误差在次天气尺度上增长最快,行星尺度上增长最慢;就绝对(相对)误差而言,模式误差对预报误差的影响随空间尺度的增大而增大(减小)。此外,将使用1989至2018年共计30 a的ERA-Interim再分析逐日数据得到的气候态分布作为参考预报,通过连续分级概率预报技巧评分(Continuously Ranked Probability Skill Score,CRPSS)检验了CMA-GEPS H_(500)及其不同尺度分量的概率预报技巧。结果表明,行星尺度分量概率预报技巧最高,次天气尺度分量最小,未经滤波的H_(500)预报技巧位于行星尺度与天气尺度分量预报技巧之间。上述诊断结果可为CMA-GEPS改进方向提供一定的客观依据。

Cointegration Analysis on the Relation between Urbanization and Economic Growth in China

This paper utilizes cointegration theory,error correcting model and Granger causality testing theory to make an empirical research on the relation between urbanization and GDP in China,and also implements a comparative analysis to the relation between three industries and degree of urbanization,the related coeffecient is 0.97,0.95,0.97,0.97.And the result shows a long-term balance between these two factors,and the promoting effect to tertiary industry by urbanization is more obvious.Urbanization and economic growth are the long-term balanced relations.In the long-term balance,every 1% increment of urbanization can make 4.82% increment of GDP;In short-term balance,if the balance depart from the long-term balance at the i-th term,the model will take automatic reversal adjustment with-0.06 adjusting strength at the(i+1)th term,to make it move to the long-term balance.The economic growth onto urbanization is one-way causality relationship,the primary and secondary industry onto urbanization is also one-way causality relationship.However,the tertiary industry onto urbanization is both-way causality relationship.

鄂西山区日本落叶松人工林全林整体模型研究与应用

为解决林分各测树因子及其生长模型之间的相容性问题,采用非线性度量误差联立方程组法建立鄂西山区日本落叶松人工林全林整体模型。研究结果表明,非线性度量误差联立方程组方法,能保证模型的相容性与无偏性;各林分因子的平均相对误差和相对均方误差均在6.5%以下,模型参数稳定性及预测精度均较高;建立的全林整体模型可用于不同初始条件的日本落叶松人工林林分生长收获预估、定量间伐作业设计以及林分密度控制图绘制。相关研究结果可为研究区日本落叶松人工林高效可持续经营提供科学参考。

旅游业发展与经济增长——基于海南省18市县的面板数据

基于2005—2018年海南18个市县的面板数据,采用误差修正模型分析海南旅游业发展水平与海南经济增长关系的长短期效应。结果:海南旅游饭店接待国内、入境过夜游客人数对海南经济发展的影响分别为正、负;各市县旅游业发展水平与经济发展的关系各有成因与特色;长期均衡方程结果显示旅游业发展水平对海南东中西部地区的经济发展影响差异较大。结论:提出海南旅游业发展应优先发展国内市场、东部地区长期内应瞄准国际市场等建议。

Empirical Analysis on the Influence of Agricultural Products Export on Economic Growth in China

After entering into WTO,export volume of agricultural products in China quickly increases,but its proportion in total trade volume becomes lower and lower,and there are more and more trade barriers of agricultural products. In this paper,based on the data during 1994-2016,error correction model is established to test and analyze the relationship between economic growth and export of agricultural products in China. The results show that change of agricultural products export in China has positive impact on GDP in short time,and they also have longterm stable relationship. When they lag for different periods,economic growth and export of agricultural products have unidirectional causality.

中国技术贸易与经济增长关系研究——基于协整与误差修正模型的分析

第二次世界大战后,国际技术贸易开始形成规模并迅速扩大,已经成为推动各国乃至世界经济发展的助推器。本文从中国技术贸易与经济发展的现状出发,运用协整与误差修正模型对1985—2020年中国技术贸易与经济增长的长期均衡与短期调整关系进行了实证分析。研究认为,技术贸易收支比对GDP增速在短期和长期均有负向影响,但长期影响比短期影响大;技术贸易收支比对GDP增速之间具有短期反向调整机制,但调整力度较小。为顺应全球技术贸易发展趋势,中国应致力于构建国际科技合作新机制,积极参与国际技术贸易规则标准建设,同时加强技术贸易安全管理。

耦合Lorenz模型的吸引子特性及其可预报性分析

通过改变耦合Lorenz模型中控制快、慢子系统之间耦合强度的参数,本文探究了耦合强度对该系统的混沌吸引子特性及可预报性的影响。结果表明:随着耦合增强,快系统中显示出与慢系统类似的低频变化特征,其吸引子也随之变大;而慢系统高频分量变大,导致其变率增强,吸引子轨道变得更加密集。在此基础上,利用非线性局部Lyapunov指数方法定量分析了耦合强度对耦合Lorenz系统可预报性的影响。具体来说,在耦合之后,耦合系统的对数误差增长曲线包含前后两段不同的误差增长率,分别代表快速和慢速误差增长过程。此外,各子系统的可预报性对耦合强度变化响应并不一致,随着对快系统的耦合强度增加,快/慢两个不同尺度系统的可预报上限均减少。然而,增加对慢系统的耦合强度却只能提高快系统的可预报上限,对慢系统的可预报性改变不大。

Bertalanffy-Pütter Models for the Growth of Tropical Trees and Stands

The Bertalanffy-Pütter (BP) five-parameter growth model provides a versatile framework for the modeling of growth. Using data from a growth experiment in literature about the average size-at-age of 24 species of tropical trees over ten years in the same area, we identified their best-fit BP-model parameters. While different species had different best-fit exponent-pairs, there was a model with a good fit to 21 (87.5%) of the data (“Good fit” means a normalized root-mean-squared-error NRMSE below 2.5%. This threshold was the 95% quantile of the lognormal distribution that was fitted to the NRMSE values for the best-fit models for the data). In view of the sigmoidal character of this model despite the early stand we discuss whether the setting of the growth experiment may have impeded growth.

共同富裕视角下和谐消费及其经济增长空间效应

从共同富裕视角深入剖析和谐消费的理论内涵,发现和谐消费包含消费力和谐、消费关系和谐以及和谐消费力与和谐消费关系的有机统一,其中,消费力和谐与消费关系和谐分别反映了共同富裕的效率与公平思想。为进一步明确中国和谐消费的发展状况及其经济增长效应,根据和谐消费的理论内涵,探索性地构建和谐消费力与和谐消费关系的指标体系,运用耦合协调度模型测算2007—2020年中国30个省份的和谐消费水平,并采用空间计量模型实证考察和谐消费的经济增长空间效应。实证结果表明:(1)从时间趋势来看,2007—2020年中国和谐消费水平呈波动上升态势,但整体而言,中国消费和谐度有待进一步提升;从空间分布来看,中国和谐消费水平呈现明显的“东高西低、沿海高内陆低”的空间分异格局。(2)和谐消费对经济增长具有显著的正向空间溢出效应,且在剔除异常值、增加控制变量与更换空间权重矩阵等一系列稳健性检验后结论依旧成立。(3)进一步检验发现,和谐消费能够通过优化产业结构牵引高质量供给,形成促进经济增长的供需联动机制。据此,建议以居民收入提高与社会保障完善为主线,促进消费力和谐;以居民收入差距缩小与社会信用体制健全为抓手,改善消费关系;协同推进需求侧管理与供给侧改革,以高水平供需动态平衡助力经济高质量发展。