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.     

Advection in plankton models with variable elemental ratios

As ocean biogeochemical models evolve to permit the elemental composition of plankton populations and dissolved organic matter to vary, each element is normally assigned a separate state variable, which is advected and mixed independently of the others. In a population of cells with varying elemental quotas, the proper currency of the advection operator is subpopulations of similar cells. The spatial gradient in total C, N, or P summed over the spectrum of such subpopulations is identical to that calculated for the population means, so treating the various elements as independent should generally be a valid approximation. However, errors can arise in high-order advection schemes with nonlinear corrector terms, which are not additive across the subpopulations. Some numerical examples indicate that these errors are relatively small [O(10⁻³–10⁻⁴)] but can be as high as O(10⁻²) in certain cases. As grid resolution varies, the error scales approximately to the Courant number.     

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.     

福建省及其沿海和台湾海峡频度统计与分析

本文采用空间扫描方法 ,按选区和时段做震级—频度统计 ,计算年平均地震数。通过比较与分析 ,得出三点初步结论     

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.     

Gauss投影的复变函数表示

利用复变函数理论重新讨论了高斯投影。研究表明，高斯投影的复变函数表示具有形式紧凑、公式简单、计算效率高等优点，特别是基于复变函数建立的尺度比和子午线收敛角公式能表示为闭合形式。     

反射波法地震勘探在工程勘察中的应用

对于地下复杂的地质构造，地震反射波法比折射波法能够提供更多更详细的地下信息。多次覆盖反射波法观测系统可以增加同一反射点的重复观测次数，从而起到压制干扰，提高有效波信号的作用。其次选取合适的地震参数能有效地拓宽有效波频带范围，对地震勘探分辨率的提高起到非常重要的作用。