Is Model Parameter Error Related to a Significant Spring Predictability Barrier for El Nio 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 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.     

EXPERIMENTS ON ASSIMILATION OF INITIAL VALUES IN NUMERICAL PREDICTION OF A WARM-SECTOR PRECIPITATION IN SOUTH CHINA

In order to understand the impact of initial conditions upon prediction accuracy of short-term forecast and nowcast of precipitation in South China, four experiments i.e. a control, an assimilation of conventional sounding and surface data, testing with nudging rainwater data and the assimilation of radar-derived radial wind, are respectively conducted to simulate a case of warm-sector heavy rainfall that occurred over South China, by using the GRAPES_MESO model. The results show that (1) assimilating conventional surface and sounding observations helps improve the 24-h rainfall forecast in both the area and order of magnitude; (2) nudging rainwater contributes to a significant improvement of nowcast, and (3) the assimilation of radar-derived radial winds distinctly improves the 24-h rainfall forecast in both the area and order of magnitude. These results serve as significant technical reference for the study on short-term forecast and nowcast of precipitation over South China in the future.     

The Sensitive Regions Identified by CNOPs of Three Typhoon Events

In this paper, several sets of observing system simulation experiments (OSSEs) were designed for three typhoon cases to determine whether or not the additional observation data in the sensitive regions identified by conditional nonlinear optimal perturbations (CNOPs) could improve the short-range forecast of typhoons. The results show that the CNOPs capture the sensitive regions for typhoon forecasts, which implies that conducting additional observation in these specific regions and eliminating initial errors could reduce forecast errors. It is inferred from the results that dropping sondes in the CNOP sensitive regions could lead to improvements in typhoon forecasts.     

A self-similar flow behind a shock wave in a gravitating or non-gravitating gas with heat conduction and radiation heat-flux

The propagation of a spherical shock wave in an ideal gas with heat conduction and radiation heat-flux, and with or without self-gravitational effects, is investigated. The initial density of the gas is assumed to obey a power law. The heat conduction is expressed in terms of Fourier’s law and the radiation is considered to be of the diffusion type for an optically thick grey gas model. The thermal conductivity and the absorption coefficient are assumed to vary with temperature and density, and the total energy of the wave to vary with time. Similarity solutions are obtained and the effects of variation of the heat transfer parameters, the variation of initial density and the presence of self-gravitational field are investigated.     

Measurement of the Dielectric Properties of Volcanic Scoria and Basalt at 9370 MHz

Dielectric data for volcanic scoria and basalt on the earth at microwave frequency are extremely sparse, and also crucial for volcanic terrains imaging, and development. In consideration of their similarity to lunar regolith (soils and rocks) in chemical and mineral composition, the dielectric data is significative for passive and active microwave remote sensing on the Moon. This study provides the data about the dielectric properties of three kinds of scoria and two kinds of basalt in China. The method put forward in this paper is also applicable for measuring the dielectric properties of dry rocks and other granular ground materials with low complex dielectric constants. Firstly, the authors measured the ε‘ and tanδ values of strip specimens prepared from the mixture of scoria or basalt powder and polythene with the resonant cavity perturbation method at 9370 MHz. Secondly, from the ε‘ and tang values of the mixture, the ε‘s and tanδs values of sofid scoria and basalt were calculated using Lichtenecker‘s mixture formulae. Finally, the effective complex dielectric constants, ε‘e and tanδs, of scoria at different bulk densities were calculated. The results have shown that the ε‘s and tang svalues of all solid basaltic materials measured (both solid basaltic scoria or basalt) are approximately 7 and 0.05,respectively. With increasing bulk density of scoria, the ε‘s and tanδs values of scoria increase significantly.     

On the Application of a Genetic Algorithm to the Predictability Problems Involving "On-Off" Switches

The lower bound of maximum predictable time can be formulated into a constrained nonlinear opti- mization problem, and the traditional solutions to this problem are the filtering method and the conditional nonlinear optimal perturbation (CNOP) method. Usually, the CNOP method is implemented with the help of a gradient descent algorithm based on the adjoint method, which is named the ADJ-CNOP. However, with the increasing improvement of actual prediction models, more and more physical processes are taken into consideration in models in the form of parameterization, thus giving rise to the on-off switch problem, which tremendously affects the effectiveness of the conventional gradient descent algorithm based on the ad- joint method. In this study, we attempted to apply a genetic algorithm (GA) to the CNOP method, named GA-CNOP, to solve the predictability problems involving on-off switches. As the precision of the filtering method depends uniquely on the division of the constraint region, its results were taken as benchmarks, and a series of comparisons between the ADJ-CNOP and the GA-CNOP were performed for the modified Lorenz equation. Results show that the GA-CNOP can always determine the accurate lower bound of maximum predictable time, even in non-smooth cases, while the ADJ-CNOP, owing to the effect of on-off switches, often yields the incorrect lower bound of maximum predictable time. Therefore, in non-smooth cases, using GAs to solve predictability problems is more effective than using the conventional optimization algorithm based on gradients, as long as genetic operators in GAs are properly configured.     

Application of the Conditional Nonlinear Optimal Perturbation Method to the Predictability Study of the Kuroshio Large Meander

A reduced-gravity barotropic shallow-water model was used to simulate the Kuroshio path variations.The results show that the model was able to capture the essential features of these path variations.We used one simulation of the model as the reference state and investigated the effects of errors in model parameters on the prediction of the transition to the Kuroshio large meander (KLM) state using the conditional nonlinear optimal parameter perturbation (CNOP-P) method.Because of their relatively large uncertainties,three model parameters were considered:the interfacial friction coefficient,the wind-stress amplitude,and the lateral friction coefficient.We determined the CNOP-Ps optimized for each of these three parameters independently,and we optimized all three parameters simultaneously using the Spectral Projected Gradient 2 (SPG2) algorithm.Similarly,the impacts caused by errors in initial conditions were examined using the conditional nonlinear optimal initial perturbation (CNOP-I) method.Both the CNOP-I and CNOP-Ps can result in significant prediction errors of the KLM over a lead time of 240 days.But the prediction error caused by CNOP-I is greater than that caused by CNOP-P.The results of this study indicate not only that initial condition errors have greater effects on the prediction of the KLM than errors in model parameters but also that the latter cannot be ignored.Hence,to enhance the forecast skill of the KLM in this model,the initial conditions should first be improved,the model parameters should use the best possible estimates.     

纵向数据混合效应模型的Bayes局部影响

基于分层先验思想,研究了适应于纵向数据的混合效应模型的Bayes局部影响,并根据纵向数据既包含个体又包含个体不同状态的特点,提出了两种便于合理分析数据的扰动方案,导出模型在上述各种扰动下效应参数的Bayes局部影响度量,最后给出实例。     

甘肃北山晚古生代花岗岩体内基性岩墙群弧形展布的成因及其构造意义

在甘肃北山一个东西向展布、椭圆状的晚古生代花岗岩体内主要发育着3期岩墙群,其中中期基性岩墙群最密集,呈现出弧形展布的特征,在该侵入体北部呈北北东向趋势,向南逐渐偏转为北北西向。关于中期岩墙群弧形展布的原因有两种可能的解释,即：纵弯褶皱和应力场扰动。野外观察不支持第一种解释,因为在岩墙或围岩内都没有观察到与褶皱有关的变形构造。本文利用Poly3D软件模拟了近东西向隐伏断层活动对上覆应力场的扰动。计算结果表明,当北北西-南南东向的区域性拉张叠加上沿着隐伏断层发生不协调的右行走滑时,水平大主压应力迹线在隐伏断层上方出现类似的弧形展布。这意味着稍早的花岗岩侵入体冷凝冷却并没有完全焊接地壳内一些大尺度断层,地壳断块的相对独立活动可以造成沿着这些断层发生与区域变形场不协调的右行走向滑动,进而形成岩墙群弧形展布所需要的地应力场。