中国西部绿化对东亚季风气候影响的数值模拟

中国西部大开发战略中的生态环境建设将在西部地区引起显著的地表覆盖变化。根据最新的全球地表特征数据库资料和21世纪初中国西部生态环境三大重点建设工程的具体规划，得出两种植被，即现实植被和虚拟植被。并利用RIEMS—TEA模式，通过一次敏感性试验，发现西部地区绿化明显影响东亚的季风系统和中国东部季风区气候。模拟试验显示，中国西部绿化会明显增强东亚夏季风，这将会加强中国东部由南向北的水汽输送，并有利于输送邻近海洋的水汽到大陆，使得中国大陆东部季风区整体出现降温、增湿和降水增加。而且，温度、湿度、气压和风速受影响的程度在垂直方向上都已超出了边界层之外。     

Variational assimilation in combination with the regularization method for sea level pressure retrieval from QuikSCAT scatterometer data I: Theoretical frame construction

A new method of constructing a sea level pressure field from satellite microwave scatterometer measurements is presented. It is based on variational assimilation in combination with a regularization method using geostrophic vorticity to construct a sea level pressure field from scatterometer data that are given in this paper, which offers a new idea for the application of scatterometer measurements. Firstly, the geostrophic vorticity from the scatterometer data is computed to construct the observation field, and the vorticity field in an area and the sea level pressure on the borders are assimilated. Secondly, the gradient of sea level pressure (semi-norm) is used as the stable functional to educe the adjoint system, the adjoint boundary condition and the gradient of the cost functional in which a weight parameter is introduced for the harmony of the system and the Tikhonov regularization techniques in inverse problem are used to overcome the ill-posedness of the assimilation. Finally, the iteration method of the sea level pressure field is developed.     

南海海面风与海面温度耦合模态分析

为了研究南海海面风与海面温度的相互关系,利用相关分析、经验正交函数分解、奇异值分解等方法对49a的COADS资料的南海海域的海表面温度异常(SSTA)和海表面风异常(SSWA)进行分析。SSTA的EOF1解释了总方差的50.8%,该模态与整个南海SSTA同步变化,时间系数主周期为2~3a,该系数滞后5个月,与Nino3.4指数的相关系数达到0.423;SSWA的EOF1解释了总方差的25.1%,与整个南海SSWA同向变化,时间系数的主要周期为4~8a,但与Nino3.4指数的同步相关系数只有0.04,SSTA和SSWA的SVD分析结果第一模态的方差贡献为86.7%,空间分布很好地体现了SSTA和SSWA之间的正反馈机制,左右奇异向量时间系数达到0.5,且时间系数的主要周期都为4~8a,证实了南海海域海-气耦合的主模态为ENSO模态。     

Variational assimilation in combination with a regularization method for sea level pressure retrieval from QuikSCAT scatterometer data II: simulation experiment and actual case study

The sea level pressure field can be computed from sea surface winds retrieved from satellite microwave scatterometer measurements, based on variational assimilation in combination with a regularization method given in part I of this paper. First, the validity of the new method is proved with a simulation experiment. Then, a new processing procedure for the sea level pressure retrieval is built by combining the geostrophic wind, which is computed from the scatterometer 10-meter wind using the University of Washington planetary boundary layer model using this method. Finally, the feasibility of the method is proved using an actual case study.     

Remote sensing of atmospheric duct parameters using simulated annealing

Simulated annealing is one of the robust optimization schemes. Simulated annealing mimics the annealing process of the slow cooling of a heated metal to reach a stable minimum energy state. In this paper, we adopt simulated annealing to study the problem of the remote sensing of atmospheric duct parameters for two different geometries of propagation measurement. One is from a single emitter to an array of radio receivers (vertical measurements), and the other is from the radar clutter returns (horizontal measurements). Basic principles of simulated annealing and its applications to refractivity estimation are introduced. The performance of this method is validated using numerical experiments and field measurements collected at the East China Sea. The retrieved results demonstrate the feasibility of simulated annealing for near real-time atmospheric refractivity estimation. For comparison, the retrievals of the genetic algorithm are also presented. The comparisons indicate that the convergence speed of simulated annealing is faster than that of the genetic algorithm, while the anti-noise ability of the genetic algorithm is better than that of simulated annealing.