国家气象中心数值预报业务的进展

在最近的１５年中，国家气象中心的数值预报业务高速度发展，预报模式从北半球模式发展为全球谱模式，并配套建立了资料同化系统和用于降水预报的有限区预报模式，暴雨和台风预报模式正在研制中。目前数值预报时效已延至７天，Ｔ６３９３丙上时的预报水平已优于的数值预报产品的应用技术在不断改进，最高（低）气温下两天ＭＯＳ预报精度已接近预报员制作的综合预报结果。     

Uptake of macroelements by the helophytePhalaris arundinacea L.

Helophytes are often incorporated into biological wastewater treatment plants. In favourable situations, they can take up large amounts of nutrients. One helophyte, the reed canarygrass (Phalaris arundinacea L.), is fast growing when supplied with sufficient light and nutrients. Experiments were carried out under natural climatic conditions in small plastic tanks filled with sand with regular additions of a balanced and concentrated nutrient solution. In the growing season (May–October), plant production reached 10.5 kg m^–2 of dry biomass, of which 66% was in the aerial parts. Maximum nutrient uptake capacity was reached just after flowering and before senescence (beginning of October): 49% N, 34% P, 52% K and 34% Mg of the input was fixed in the aerial parts, which are easily harvestable. The corresponding values for the below ground parts were 12%, 10%, 11% and 11% respectively. Excretion of K and Mg has been observed when nutrients are translocated to the storage organs.     

数值模式初值的敏感性程度对四维同化的影响

用著名的Ｌｏｒｅｎｚ系统作了共轭变分同化的数值试验。发现随着模式对初值敏感性程度的增加，用这种方法得到和模式相协调的初始场愈来愈困难，直到某些情况下的完全失败。这表明四维同化和可预报期限是联系在一起的。另一方面，随着方程不精确程度的增加，变分同化的效果愈来愈差，直到所做的预报无任何意义可言。如果在做变分同化的同时对模式参数也进行反演，就可使得基于Ｌｏｒｅｎｚ系统所做的预报效果大大提高。     

Impact of Doppler Radar Wind in Simulating the Intensity and Propagation of Rainbands Associated with Mesoscale Convective Complexes Using MM5-3DVAR System

Pre-monsoon rainfall around Kolkata (northeastern part of India) is mostly of convective origin as 80% of the seasonal rainfall is produced by Mesoscale Convective Systems (MCS). Accurate prediction of the intensity and structure of these convective cloud clusters becomes challenging, mostly because the convective clouds within these clusters are short lived and the inaccuracy in the models initial state to represent the mesoscale details of the true atmospheric state. Besides the role in observing the internal structure of the precipitating systems, Doppler Weather Radar (DWR) provides an important data source for mesoscale and microscale weather analysis and forecasting. An attempt has been made to initialize the storm-scale numerical model using retrieved wind fields from single Doppler radar. In the present study, Doppler wind velocities from the Kolkata Doppler weather radar are assimilated into a mesoscale model, MM5 model using the three-dimensional variational data assimilation (3DVAR) system for the prediction of intense convective events that occurred during 0600 UTC on 5 May and 0000 UTC on 7 May, 2005. In order to evaluate the impact of the DWR wind data in simulating these severe storms, three experiments were carried out. The results show that assimilation of Doppler radar wind data has a positive impact on the prediction of intensity, organization and propagation of rain bands associated with these mesoscale convective systems. The assimilation system has to be modified further to incorporate the radar reflectivity data so that simulation of the microphysical and thermodynamic structure of these convective storms can be improved.     

A One-dimensional Ensemble Forecast and Assimilation System for Fog Prediction

A probabilistic fog forecast system was designed based on two high resolution numerical 1-D models called COBEL and PAFOG. The 1-D models are coupled to several 3-D numerical weather prediction models and thus are able to consider the effects of advection. To deal with the large uncertainty inherent to fog forecasts, a whole ensemble of 1-D runs is computed using the two different numerical models and a set of different initial conditions in combination with distinct boundary conditions. Initial conditions are obtained from variational data assimilation, which optimally combines observations with a first guess taken from operational 3-D models. The design of the ensemble scheme computes members that should fairly well represent the uncertainty of the current meteorological regime. Verification for an entire fog season reveals the importance of advection in complex terrain. The skill of 1-D fog forecasts is significantly improved if advection is considered. Thus the probabilistic forecast system has the potential to support the forecaster and therefore to provide more accurate fog forecasts.     

基于进化方向遗传算法的四维变分资料同化方法

数值天气预报模式初始时刻要素场的变分同化问题是一个非线性最优化问题。利用进化方向遗传算法（EDGA）求解该最优化问题，并对理想初始场作数值模拟，结果表明模拟的效果较好。     

有限区域变分四维同化系统的设计和试验

使用共轭码技术，构造了中尺度模式的伴随模式，研究了模式侧边界条件。基于伴随模式技术的原理，设计了伴随同化和Derber同化方法两套方案；下降算法采用共轭梯度法，使用准真实资料，进行子同化数值试验和预报试验。结果表明，两套同化方案都取得了一定的同化效果，Derber同化方法比伴随同化更令人满意。证明了变分同化是提高预报准确率的一种非常有效的手段。     

用变分法修正黑河试验数据的初步研究

为了对黑河试验观测资料进行订正,利用一个一维边界层垂直扩散模式和以拟牛顿极小化算法为基础的最优化方法建立了一个变分同化系统,其中,目标函数相对于控制变量的梯度用扰动法求取.并利用模式资料和黑河试验若干时刻的观测资料进行了一些数值试验.试验证明,将初始场变量与模式误差系数同时作为控制变量加以调整,能够有效调整初始场观测资料.并且,用变分同化方法能够较线性插值(时间、空间)方法更有效、合理的修正黑河试验观测资料场的种种缺陷.     

基于GIS与空间统计分析的可持续发展度量方法研究——以缅甸Myingyan District为例

利用空间信息系统所提供的强大空间数据处理和分析能力，并将之与统计分析软件包的统计分析功能进行有效的集成，建立了基于空间统计分析的可持续发展定量分析评价模型（ＳＢＳＡ）。从在缅甸中部Myingyan县的应用实例来看，该模型能够提示影响Myingyan县可持续发展能力的主导因子，通过对这些因子和可持续发展综合指数的定量化、空间化的分析，为政府部门规划区域综合发展方案，制定发展政策提供很好的决策支持。     

谱模式T63L9正规模初值化方案及试验

推导了全球谱模式T63L9的正规模,对求得的垂直和水平正规模做了分析,与其他文献进行了比较.在此基础上,为其资料四维同化系统研制了一套合适的绝热非线性正规模初值化方案,并进行了一系列试验.分析表明:方案的研制是成功的,它有效地消除了模式早期积分中虚假的高频振荡,明显改进了短期预报的效果;初值化不仅对随后的一次预报有明显的改进,而且通过同化循环,提高了整个资料同化和预报的质量.