A new method for multi-point pollution source identification

对于污染源源项特征(数量,位置及排放强度)的快速且精确估计是污染事件应急响应的关键.与单点源估计相比,多点源的重建更具挑战性.本文提出了一种新的针对于多点源污染事件的两步反演算法,该算法通过结合大气化学模式与有限的浓度观测数据以实现对于未知数量的多点源的准确估计.在其计算过程中,无需任何未知量的先验信息,且可以自动识别污染源的数量,并确定每一个污染源的位置及强度.本文通过使用若干组理想试验验证了算法的适用性,试验结果表明,该算法可准确估计单点源,两点源和三点源的个数及位置,强度的估计误差可基本忽略.且该算法的估计精度和计算效率不会随着点源个数的增加而减弱.     

Application of the Hilbert–Huang Transform to the Estimation of Air-Sea Turbulent Fluxes

The Hilbert–Huang transform (HHT) is applied to analyzing the turbulent time series obtained within the atmospheric boundary layer over the ocean. A method based on the HHT is introduced to reduce the influence of non-turbulent motions on the eddy-covariance based flux by removing non-turbulent modes from the time series. The scale dependence of the flux is examined and a gap mode is identified to distinguish between turbulent modes and non-turbulent modes. To examine the effectiveness of this method it is compared with three conventional methods (block average, moving-window average, and multi-resolution decomposition). The data used are from three sonic anemometers installed on a moored buoy at about 6, 4 and 2.7 m height above the sea surface. For each method, along-wind and cross-wind momentum fluxes and sensible heat fluxes at the three heights are calculated. According to the assumption of a constant-flux layer, there should be no significant difference between the fluxes at the three heights. The results show that the fluxes calculated using HHT exhibit a smaller difference and higher correlation than the other methods. These results support the successful application of HHT to the estimation of air-sea turbulent fluxes.     

DRP-4DVar方法同化AIRS反演资料在一次江淮流域暴雨中的应用

利用经济省时的降维投影四维变分同化方法(DRP-4DVar),在2009年7月22～23日江淮流域的一次大暴雨过程中同化晴空条件下高光谱大气红外探测仪(AIRS)反演温度、湿度廓线,改进此次强降水过程的模拟。试验结果分析显示,同化AIRS反演的温度及湿度场后,基于四维变分同化系统的模式约束,能够改进湿度场、高度场、高低层散度场。从累积降水量偏差图及同化试验增量图可以看到,正降水量偏差对应于正湿度增量、负位势高度增量及低层负散度高层正散度增量,负降水量偏差则与之相反。同化试验较参照试验可更好地模拟出暴雨的天气形势、对暴雨的落区及强度有更好的反映。此外,从单次同化与连续同化的试验对比结果看出,连续同化试验结果较单次同化结果有进一步的改进,说明不断加入新的观测资料可以更好地模拟强降水过程。     

The Structure of Background-error Covariance in a Four-dimensional Variational Data Assimilation System: Single-point Experiment

A four dimensional variational data assimilation (4DVar) based on a dimension-reduced projection (DRP-4DVar) has been developed as a hybrid of the 4DVar and Ensemble Kalman filter (EnKF) concepts. Its good flow-dependent features are demonstrated in single-point experiments through comparisons with adjoint-based 4DVar and three-dimensional variational data (3DVar) assimilations using the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5). The results reveal that DRP-4DVar can reasonably generate a background error covariance matrix (simply B-matrix) during the assimilation window from an initial estimation using a number of initial condition dependent historical forecast samples. In contrast, flow-dependence in the B-matrix of MM5 4DVar is barely detectable. It is argued that use of diagonal estimation in the B-matrix of the MM5 4DVar method at the initial time leads to this failure. The experiments also show that the increments produced by DRP-4DVar are anisotropic and no longer symmetric with respect to observation location due to the effects of the weather trends captured in its B-matrix. This differs from the MM5 3DVar which does not consider the influence of heterogeneous forcing on the correlation structure of the B-matrix, a condition that is realistic for many situations. Thus, the MM5 3DVar assimilation could only present an isotropic and homogeneous structure in its increments.     

An Economical Approach to Four-dimensional Variational Data Assimilation

Four-dimensional variational data assimilation (4DVar) is one of the most promising methods to provide optimal analysis for numerical weather prediction (NWP). Five national NWP centers in the world have successfully applied 4DVar methods in their global NWPs, thanks to the increment method and adjoint technique. However, the application of 4DVar is still limited by the computer resources available at many NWP centers and research institutes. It is essential, therefore, to further reduce the computational cost of 4DVar. Here, an economical approach to implement 4DVar is proposed, using the technique of dimension-reduced projection (DRP), which is called ``DRP-4DVar." The proposed approach is based on dimension reduction using an ensemble of historical samples to define a subspace. It directly obtains an optimal solution in the reduced space by fitting observations with historical time series generated by the model to form consistent forecast states, and therefore does not require implementation of the adjoint of tangent linear approximation. To evaluate the performance of the DRP-4DVar on assimilating different types of mesoscale observations, some observing system simulation experiments are conducted using MM5 and a comparison is made between adjoint-based 4DVar and DRP-4DVar using a 6-hour assimilation window.     

GRAPES-GEPS全球集合预报系统湿奇异向量的时空尺度敏感性研究

湿奇异向量(Moist Singular Vectors,简称MSVs)是包含了湿物理切线性过程计算得到的奇异向量.研究MSVs对最优化时间间隔(optimization time interval,简称OTI)及模式水平分辨率的敏感性对提高集合预报效果至关重要.本文基于中国气象局数值预报中心自主研发的全球/区域同化和...     

一次副高控制下的江西暴雨过程模拟分析

利用WRF数值模式输出的高分辨率模式资料,对2010年7月14—15日江西出现的一次副高控制下的暴雨过程进行了分析。结果表明,此次暴雨过程同样也存在充沛的水汽输送,比湿、水汽通量散度基本都达到江西暴雨的阈值;暴雨区以北能量锋区密集,梯度达到12 K/纬距,大暴雨出现在强能量锋区南缘的高能量舌中;干冷气流侵入地面暖槽,地面扰动有利于强不稳定能量的释放,激发中尺度对流系统发展;强降水发生在切变线附近的西南气流中,并且强降水发生时中低层为一致的上升运动;低层辐合、高层辐散的抽吸作用对上升运动的维持十分有利;300 hPa层以下有正涡度柱存在,在其两侧有下沉气流,并有次级环流存在。     

Impact of Analysis-time Tuning on the Performance of the DRP-4DVar Approach

In this study we extend the dimension-reduced projection-four dimensional variational data assimilation (DRP-4DVar) approach to allow the analysis time to be tunable, so that the intervals between analysis time and observation times can be shortened. Due to the limits of the perfect-model assumption and the tangentlinear hypothesis, the analysis-time tuning is expected to have the potential to further improve analyses and forecasts. Various sensitivity experiments using the Lorenz-96 model are conducted to test the impact of analysistime tuning on the performance of the new approach under perfect and imperfect model scenarios, respectively. Comparing three DRP-4DVar schemes having the analysis time at the start, middle, and end of the assimilation window, respectively, it is found that the scheme with the analysis time in the middle of the window outperforms the others, on the whole. Moreover, the advantage of this scheme is more pronounced when a longer assimilation window is adopted or more observations are assimilated.     

冷水坑矿田层状富铅锌矿赋矿围岩——铁锰碳酸岩角砾岩物质来源及成因分析

冷水坑矿田具有浅部斑岩型矿体与深部层状矿体的复杂组合,深部层状矿体赋存于上侏罗统打鼓顶组晶屑凝灰岩所夹的(铁锰)碳酸岩角砾岩层中。从(铁锰)碳酸岩角砾岩的基本地质特征入手,论述和分析了(铁锰)碳酸岩角砾岩的主要物质成分组成、分布、产状及与矿体关系等特点,结合矿物X-射线粉晶分析、碳-氧同位素和氢-氧同位素佐证,认为(铁锰)碳酸岩角砾岩为深水湖泊相火山-沉积成因,与层状矿体形成密切相关,后期斑岩岩浆就位及其流体活动对早期火山沉积碳酸岩层叠加改造,从而形成了与斑岩具有成因联系的层状富铅锌银矿床。     

Application and Characteristic Analysis of the Moist Singular Vector in GRAPES-GEPS

The singular vector(SV) initial perturbation method can capture the fastest-growing initial perturbation in a tangent linear model(TLM). Based on the global tangent linear and adjoint model of GRAPES-GEPS(Global/Regional Assimilation and Prediction System—Global Ensemble Prediction System), some experiments were carried out to analyze the structure of the moist SVs from the perspectives of the energy norm, energy spectrum, and vertical structure. The conclusions are as follows: The evolution of ...