Some Annual Variation Characteristics for the Northern Hemispheric Monthly Mean Precipitation Fields

By the utilization of monthly precipitation data from all stations in the Northern Hemisphere annexed to the "World Survey of climatology, Vol. 1-15", the distributions of the maximum precipitation months (MPM), the annual relative precipitation (ARP) and the monthly relative precipitation (percent of annual) in January and July are respectively mapped. Moreover the distributions of intermonthly relative precipitation variabilities from January to December are plotted as well. From these figures, the precipitation in the Northern Hemisphere may be classified into three types(continental, oceanic and transitional types) and 17 regions. The precipitation regime may also be divided into two patterns, the global and regional patterns. The global pattern consists of planetary front system and ITCZ and its inter-monthly variation shows the north-and-south shift of the rain belt; the regional pattern consists of the sea-land monsoon and plateau monsoon regime, in which the inter-monthly variation of rain belt     

A Regional Spectral Nested Multilevel Primitive Equation Model

By means of vertical normal modes a regional nested multilevel primitive equation model can be reduced to several sets of shallow water equations characterized by various equivalent depths. Therefore, time integration of the model in spectral form can be performed in the manner similar to those used in the spectral nested shallow water equation model case.     

A Regional Spectral Nested Shallow Water Equation Model

A method to expand meteorological elements in terms of finite double Fourier series in a limited-region and a spectral nested shallow water equation model based upon the method with conformal map projection in rectangular coordinates, have been proposed, and computational stability and efficiency of time integration have been discussed.     

Monthly Extended Predicting Experiments with Nonlinear Regional Prediction.PartⅡ:Improvement of Wave Component Prediction

Based on Chen et al. (2006), the scheme of the combination of the pentad-mean zonal height departure nonlinear prediction with the T42L9 model prediction was designed, in which the pentad zonal heights at all the 12-initial-value-input isobar levels from 50 hPa to 1000 hPa except 200, 300, 500, and 700 hPa were derived from nonlinear forecasts of the four levels by means of a good correlation between neighboring levels. Then the above pentad zonal heights at 12 isobar-levels were transformed to the spectrum coefficients of the temperature at each integration step of T42L9 model. At last, the nudging was made. On account of a variety of error accumulation, the pentad zonal components of the monthly height at isobar levels output by T42L9 model were replaced by the corresponding nonlinear results once more when integration was over. Multiple case experiments showed that such combination of two kinds of prediction made an improvement in the wave component as a result of wave-flow nonlinear interaction while reducing the systematical forecast errors. Namely the monthly-mean height anomaly correlation coefficients over the high- and mid-latitudes of the Northern Hemisphere, over the Southern Hemisphere and over the globe increased respectively from 0.249 to 0.347, from 0.286 to 0.387, and from 0.343 to 0.414 (relative changes of 31.5%, 41.0%, and 18.3%). The monthly-mean root-mean-square error (RMSE) of T42L9 model over the three areas was considerably decreased, the relative change over the globe reached 44.2%. The monthly-mean anomaly correlation coefficients of wave 4-9 over the areas were up to 0.392, 0.200, and 0.295, with the relative change of 53.8%, 94.1%, and 61.2%, and correspondingly their RMSEs were decreased respectively with the rate of 8.5%, 6.3%, and 8.1%. At the same time the monthly-mean pattern of parts of cases were presented better.     

Monthly Extended Predicting Experiments with Nonlinear Regional Prediction.PartⅠ:Prediction of Zonal Mean Flow

Systematic errors have recently been founded to be distinct in the zonal mean component forecasts, which account for a large portion of the total monthly-mean forecast errors. To overcome the difficulty of numerical model, the monthly pentad-riean nonlinear dynamic regional prediction models of the zonal mean geopotential height at 200, 300, 500, and 700 hPa based on a large number of historical data (NCEP/NCAR reanalysis data) were constituted by employing the local approximation of the phase space reconstruction theory and nonlinear spatio-temporal series prediction method. The 12-month forecast experiments of 1996 indicated that the results of the nonlinear model are better than those of the persistent, climatic prediction, and T42L9 model either over the high- and mid-latitude areas of the Northern and Southern Hemispheres or the tropical area. The root-mean-square of the monthly-mean height of T42L9 model was considerably decreased with a change of 30.4%, 26.6%, 82.6%, and 39.4%, respectively, over the high- and mid-latitudes of the Northern Hemisphere, over the high- and mid-latitudes of the Southern Hemisphere, over the tropics and over the globe, and also the corresponding anomaly correlation coefficients over the four areas were respectively increased by 0.306-0.312, 0.304-0.429, 0.739-0.746, and 0.360-0.400 (averagely a relative change of 11.0% over the globe) by nonlinear correction after integration, implying that the forecasts given by nonlinear model include more useful information than those of T42L9 model.     

A Limited Area Model for Monsoon Prediction

A six level regional primitive equation model has been formulated and tested for monsoon prediction. The model uses dynamic normal mode initialization scheme for obtaining initial balance. The physical processes included are: the large scale condensation, the Kuo type of cumulus convection, the surface friction, the sensible heat supply and evaporation over the sea. The actual smooth orography is included. The model has been integrated for 48 hrs using input of 7 July and 8 August 1979 when the domain of integration was dominated by an intense monsoon depression. In order to investigate the model simulation of formative stage of the depression, the model was also integrated using input of 4 July 1979.Furthermore, the envelope orography has been constructed and included in the model for investigating its effects on the monsoon prediction. Results of the model forecast are presented and discussed.     

Three－dimensional Global Scale Permanent－wave Solutions of the Nonlinear Quasigeostrophic Potential Vorticity Equation and Energy Dispersion

The three-dimensional nonlinear quasi-geostrophic potential vorticity equation is reduced to a linear form in the stream function in spherical coordinates for the permanent wave solutions consisting of zonal wavenumbers from 0 to n and rn vertical components with a given degree n. This equation is solved by treating the coefficient of the Coriolis parameter square in the equation as the eigenvalue both for sinusoidal and hyperbolic variations in vertical direction. It is found that these solutions can represent the observed long term flow patterns at the surface and aloft over the globe closely. In addition, the sinusoidal vertical solutions with large eigenvalue G are trapped in low latitude, and the scales of these trapped modes are longer than 10 deg. lat. even for the top layer of the ocean and hence they are much larger than that given by the equatorial β-plane solutions. Therefore such baroclinic disturb-ances in the ocean can easily interact with those in the atmosphere.Solutions of the shallow water potential vorticity equation are treated in a similar manner but with the effective depth H = RT / g taken as limited within a small range for the atmosphere.The propagation of the flow energy of the wave packet consisting of more than one degree is found to be along the great circle around the globe both for barotropic and for baroclinic flows in the atmosphere.     

Monthly Mean Temperature Prediction Based on a Multi－level Mapping Model of Neural Network BP Type

In terms of 34-year monthly mean temperature series in 1946-1979, the multi-level mapping model of neural network BP type was applied to calculate the system’s fractual dimension D0 = 2.8, leading to a three-level model of this type with i × j = 3 × 2, k = 1, and the 1980 monthly mean temperture prediction on a long-term basis were pre-pared by steadily modifying the weighting coefficient, making for the correlation coefficient of 97% with the measurements. Furthermore, the weighting parameter was modified for each month of 1980 by means of observations, therefore constructing monthly mean temperature forecasts from January to December of the year, reaching the correlation of 99.9% with the measurements. Likewise, the resulting 1981 monthly predictions on a long-range basis with 1946-1980 corresponding records yielded the correlation of 98% and the month-to month forecasts of 99.4%.     

A nonlinear time-lag differential equation model for predicting monthly precipitation

This paper investigates the nonlinear prediction of monthly rainfall time series which consists of phase space con-tinuation of one-dimensional sequence, followed by least-square determination of the coefficients for the terms of the time-lag differential equation model and then fitting of the prognostic expression is made to 1951-1980 monthly rainfall datasets from Changsha station Results show that the model is likely to describe the nonlinearity of the an-nual cycle of precipitation on a monthly basis and to provide a basis for flood prevention and drought combating for the wet season.     

有效位能和冷空气活动与台风暴雨增幅的研究

以昆明近百年月雨量时间序列为样本,进行了月雨量的可预报性试验,通过估算和１２年共１４４次的独立预报试验,我们得出：在适当的预报精度的要求下,月雨量的可预报时间尺度约为５个月,于适当嵌入的相空间维数ｍ和在一定的提前预报时间尺度Ｔ内,对月雨量的距平符号预报准确率可达６０％,对于雨季（５－１０月）则可达到６５％。     

月降水量的神经网络混合预报模型研究

以均生函数表征预报量自身周期变化，结合500hPa月平均高度场和月平均海温场预报因子，采用神经网络方法建立了一种新的短期气候预报模型。分别以广西桂北、桂中和桂南6月降水量作为预报对象进行预报试验，结果表明，这种新的预报方法比均生函数回归预报模型及高度场、海温场预报因子的回归预报模型，具有更好的物理基础和预报能力。     

利用一维时间序列重建动力系统的初步研究

通过对月平均气温一维时间序列作相空间拓展,和假定相空间状态变量随时间的演化方程含有线性和非线性二次项,再根据拓展后的资料进行拟合,利用最小二乘法求出各项系数,保留其方差贡献大的项,以重建动力系统.实际计算结果表明,重建的低阶动力系统有可能对月平均气温所表征的短期气候演化过程的非线性特性作出描写.     

东北区夏季月降水数值产品释用预报方法

采用卡尔曼滤波法对国家气候中心的T63动力延伸预报场进行误差订正, 将订正后的预报场用于基于天气系统特征定性判断与相似离度指数定量相似相结合的降水预报模型, 制作东北区51个站点的定量的月降水预报, 并对1996~1998年夏季月降水进行了预报试验, 试验效果较好.     

Preliminary Study of Reconstruction of a Dynamic System Using an One－Dimensional Time Series

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A Comparison of De-noising Methods for Differential Phase Shift and Associated Rainfall Estimation

Measured differential phase shift ΦDP is known to be a noisy unstable polarimetric radar variable, such that the quality of ΦDP data has direct impact on specific differential phase shift KDP estimation, and subsequently, the KDP-based rainfall estimation. Over the past decades, many ΦDP de-noising methods have been developed; however, the de-noising effects in these methods and their impact on KDP-based rainfall estimation lack comprehensive comparative analysis. In this study, simulated noisy ΦDP data were generated and de-noised by using several methods such as finite-impulse response(FIR), Kalman, wavelet,traditional mean, and median filters. The biases were compared between KDP from simulated and observedΦDP radial profiles after de-noising by these methods. The results suggest that the complicated FIR, Kalman,and wavelet methods have a better de-noising effect than the traditional methods. After ΦDP was de-noised,the accuracy of the KDP-based rainfall estimation increased significantly based on the analysis of three actual rainfall events. The improvement in estimation was more obvious when KDP was estimated with ΦDP de-noised by Kalman, FIR, and wavelet methods when the average rainfall was heavier than 5 mm h-1.However, the improved estimation was not significant when the precipitation intensity further increased to a rainfall rate beyond 10 mm h-1. The performance of wavelet analysis was found to be the most stable of these filters.     

一类具有非线性迁移项的拟线性抛物方程解的熄灭

在Rn有界域上讨论了具有非线性迁移项的平均曲率型方程ut-div[σ(| u|2) u]+b(u)· u=0的初边值问题。若初值u0∈Lq,q≥n,问题的解将在有限时刻熄灭,并且给出了解的L∞估计。     

汛期预测的集合预告模拟研究

本文介绍一个跨季度汛期降水预测的集合预测模拟试验，以1987年为例，用2月1日～26日每隔5天的观测资料作初值进行了6个积分，取其平均作为集合预测，共有7个预测结果，计算了7个预测与观测场的同号率，计算了6个预测的标准差，结果表明:集合预测比个别试验更为合理，这6个试验的结果在大尺度分布上是相近的，其差别也是不大的。集合预测值得继续进行试验。文中也讨论了一些由于大气运动固有的动力学特性，其集合预测与经典的数学考虑有所区别，对集合预测提出了一些新的看法，许多问题还需要继续深入探讨，以期能对该方法作出恰当的评