Operational Evaluation of the Quantitative Precipitation Estimation by a CINRAD-SA Dual Polarization Radar System

In this paper, the quantitative precipitation estimation based on hydrometeor classification (HCA-QPE) algorithm was proposed for the first operational S band dual-polarization radar promoted by the CINRAD/SA radar of China. The HCA-QPE algorithm, the localized Colorado State University-Hydrometeor Identification of Rainfall (CSU-HIDRO) and Joint Polarization Experiment (JPOLE) algorithms, and the dynamic Z-R relationships based on variational correction QPE (DRVC-QPE) algorithm were evaluated with the rainfall events from March 1 to October 30, 2017 in the Guangdong Province. The results indicated that even though the HCA-QPE algorithm did not use the observed rainfall data for correction, its estimation accuracy was better than that of the DRVC-QPE algorithm when the rainfall rate was greater than 5mm h^(-1); and the stronger the rainfall intensity, the greater the QPE improvement. And the HCA-QPE algorithm worked better than the localized CSU-HIDRO and JPOLE algorithms. This study preliminarily verified the improved accuracy of QPE by a dual-polarization radar system modified from CINRAD-SA radar.     

基于多属性决策的区域大气复合污染多目标优化控制方法研究

大气复合污染是我国目前面临的主要大气环境问题。科学减排对大气复合污染形成起主要作用的一次污染物和二次污染的前体物,是治理大气复合污染的有效手段。本文提出了一种新的区域大气复合污染多目标优化模型,并结合多目标进化算法和基于决策者偏好的多属性决策技术,寻找治理大气污染的最优控制策略。与传统的单目标优化控制技术相比,本文提出的方法在保证环境达标的前提下,同时兼顾治理成本和行业可持续性发展这两种在一定程度上互斥的目标,并融入了决策者从当地实际情况出发对目标重要性的权衡,更加符合管理实践的需求,可为科学治理区域大气复合污染提供借鉴。     

两种土壤温度算法的对比分析

为了定量理解黄土高原土壤的物理特性和过程, 为进一步提高陆面模式对该地区地表能量平衡模拟能力奠定基础, 本文利用2005年黄土高原陆面过程试验中7月22～26日期间裸土地表观测站土壤温度观测资料, 采用热传导（结合数学拟合法)、热传导－对流两种方法分别计算了该地区土壤热扩散率。本文还利用热传导－对流方法计算0.05～0.1 m浅薄土壤层的热扩散率垂直梯度与水通量密度之和, 其值介于0.80×10-6～2.43×10-6m/s之间。在此基础之上, 以0.05 m深度的土壤层为上边界, 分别利用上述两种方法模拟0.10 m深度的土壤层温度, 结果表明: 由于忽略土壤的垂直不均匀性和水分的垂直运动而只考虑热传导过程, 热传导方法不仅高估了土壤温度振幅, 而且高估了位相的延迟。而热传导－对流方法对温度振幅和位相的模拟值与实际观测值吻合较好, 白天 (北京时间08:00～20:00) 的温度模拟值相对测量值的平均误差、 标准差和归一化标准差分别为0.19 K、0.18 K和0.08%。     

Identification of Convective and Stratiform Clouds Based on the Improved DBSCAN Clustering Algorithm

A convective and stratiform cloud classification method for weather radar is proposed based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm. To identify convective and stratiform clouds in different developmental phases, two-dimensional (2D) and three-dimensional (3D) models are proposed by applying reflectivity factors at 0.5° and at 0.5°, 1.5°, and 2.4° elevation angles, respectively. According to the thresholds of the algorithm, which include echo intensity, the echo top height of 35 dBZ (ET), density threshold, and ε neighborhood, cloud clusters can be marked into four types: deep-convective cloud (DCC), shallow-convective cloud (SCC), hybrid convective-stratiform cloud (HCS), and stratiform cloud (SFC) types. Each cloud cluster type is further identified as a core area and boundary area, which can provide more abundant cloud structure information. The algorithm is verified using the volume scan data observed with new-generation S-band weather radars in Nanjing, Xuzhou, and Qingdao. The results show that cloud clusters can be intuitively identified as core and boundary points, which change in area continuously during the process of convective evolution, by the improved DBSCAN algorithm. Therefore, the occurrence and disappearance of convective weather can be estimated in advance by observing the changes of the classification. Because density thresholds are different and multiple elevations are utilized in the 3D model, the identified echo types and areas are dissimilar between the 2D and 3D models. The 3D model identifies larger convective and stratiform clouds than the 2D model. However, the developing convective clouds of small areas at lower heights cannot be identified with the 3D model because they are covered by thick stratiform clouds. In addition, the 3D model can avoid the influence of the melting layer and better suggest convective clouds in the developmental stage.     

基于遗传算法的最优证券投资组合模型

为了研究能使风险损失率最小、收益最大的最优证券投资组合问题，首先提出了多目标规划模型，并采用模糊优选法将多目标模型单目标化。随后尝试采用遗传算法来对其求解，给出了模型的遗传算法编码规则和算法步骤。最后通过实例论证了模型的合理性及遗传算法的有效性。     

机载雷达定量测雨中衰减效应的订正研究

星载测雨雷达的应用,必须满足一些主要条件:高的空间分辨率、宽阔的覆盖范围、大的动态降雨测量范围以及精确的点测量等。由于技术条件的限制,满足这些条件的雷达工作在被雨衰减的频率段,因此必须修正由于衰减所导致的误差。对于单波束单频雷达,kZS（衰减系数-雷达反射因子-海表反射率）算法在一个较大的雨强范围内对衰减效应的订正效果较好。本文将kZS应用到在西太平洋进行的国际大型科学考察实验TOGA/COARE中1993年1月和2月间取得机载雷达（和星载雷达频率相近）实测数据,进行雨空间分布的研究,取得了和模拟研究预期相吻合的结果。     

优化K means算法在中国近海气候区划中的应用

利用我国近海多年的风、浪及温湿资料,采用DB指标和最大距离法相结合的优化K-means聚类算法,对冬季我国近海进行气候区划,避免了传统K-means算法中确定聚类数目和初始聚类中心的主观性。结果表明,冬季我国近海可被划分为3个区域:1区主要包括28°N以北的海域以及我国东南沿海的一条狭长区域,2区主要包括台湾周边海域、台湾海峡、巴士海峡以及自越南东南部起由西南部向东北部的南海大部分海域,3区则主要包括台湾岛西南部海域、北部湾、广东沿海以及南海东南部。根据给出的区划指标,分析各分区的气候特征及其对海上舰船航行和出海人员的影响,判定1区为基本适宜区,2区为不适宜区,3区为适宜区。     

山东半岛风电爬坡事件的识别与天气分析研究

根据山东省24个风电场2013年全年每15 min的风电输出功率实测数据以及相应的WRF(Weather Research and Forecasting)气象预测数据和FNL(Final Operational Global Analysis)再分析资料,分析了区域大规模风电爬坡现象与大尺度天气系统演变的联系。结果显示,大尺度天气系统,特别是阻塞高压系统,是诱发山东地区大规模风电爬坡的重要因素;爬坡事件的预测应当考虑天气演变的因素。进一步结合旋转门(The Swinging Door,TSD)算法重新讨论了爬坡事件的定义与识别,并在此基础上分析了爬坡事件的特征及其可预报性;并指出风电功率上报、考核制度应当重点考虑爬坡时间段的预测水平,以提高电网运行的稳定性。     

遗传算法求解最大预报误差的可行性研究

利用条件非线性最优扰动（conditional nonlinear optimal perturbation,CNOP）可以实现最大预报误差的上界估计。CNOP通常由基于梯度信息的约束优化算法进行求解,且其中的梯度信息由伴随模式提供。然而当非线性模式中含不连续＂开关＂时,传统伴随方法不能为优化过程提供正确的梯度方向,从而导致优化失败。为此,采用自适应变异和混合交叉的遗传算法,联赛选择机制和小生境技术的约束处理方法来求解最大预报误差上界。为检验新方法的有效性,以修改的Lorenz模型作为预报模式,对3个初始态分别用新方法和传统伴随方法进行比较,数值试验结果显示新方法求解出的最大预报误差的上界更加精确。     

Nelder-Mead单纯形算法改进及在气象上的应用

Nelder-Mead Simplex (NMS)算法是一种查找多元函数局地最小值的无微分算法,在现代科学计算中得到广泛应用,该文提出了一种对NMS算法的改进方法.改进后,大大简化了其计算过程,提高了该算法的收敛速度.利用改进后的算法对陆面过程参数进行了拟合计算,结果表明:改进的NMS算法对非线性公式具有非常高的拟合精度,可将其应用于气象学上非线性问题计算或非线性方程组求解.