自适应网格模式在暴雨数值模拟中的应用

基于变分原理，自适应网格技术能根据数值模式的特点，在模式解梯度大的地区自动加密网格，提高模式的分辨率。将其应用于MM4模式中，采用多重网格法以加速自适应网格的生成。对1996年8月4日至5日发生在华北的特大台风暴雨过程，用自适应网格模式和均匀网格模式进行了数值模拟和动力诊断分析，以研究自适应网格模式在天气预报和模拟应用中的特点。试验表明，采用自适应网格后计算稳定，对所关心的天气系统及其降水的模拟精度均高于均匀网格模式，对形势场、风场的模拟精度也有明显改善。     

A New Approach to Data Assimilation

A significant attempt to design a timesaving and efficient four-dimensional variational data assimilation (4DVar) has been made in this paper, and a new approach to data assimilation, which is noted as 'three-dimensional variational data assimilation of mapped observation (3DVM)' is proposed, based on the new concept of mapped observation and the new idea of backward 4DVar. Like the available 4DVar, 3DVM produces an optimal initial condition (IC) that is consistent with the prediction model due to the inclusion of model constraints and best fits the observations in the assimilation window through the model solution trajectory. Different from the 4DVar, the IC derived from 3DVM is located at the end of the assimilation window rather than at the beginning conventionally. This change greatly reduces the computing cost for the new approach, which is almost the same as that of the three-dimensional variational data assimilation (3DVar). Especially, such a change is able to improve assimilation accuracy because it does not need the tangential linear and adjoint approximations to calculate the gradient of cost function. Therefore, in numerical test, the new approach produces better IC than 4DVar does for 72-h simulation of TY9914 (Dan), by assimilating the three-dimensional fields of temperature and wind retrieved from the Advanced Microwave Sounding Unit-A (AMSU-A) observations. Meanwhile, it takes only 1/7 of the computing cost that the 4DVar requires for the same initialization with the same retrieved data.     

高分五号大气痕量气体差分吸收光谱仪甲醛反演可行性分析及初步结果

大气痕量气体差分吸收光谱仪EMI（ Environmental trace gases Monitoring Instrument）是搭载在“高分五号”（GF-5）卫星上的覆盖紫外—可见光波段的光谱仪,用于测量240—710 nm波长范围内的地球后向散射和太阳辐射,旨在量化全球对流层和平流层痕量气体的分布,如臭氧、二氧化氮等。本文关注EMI载荷对大气弱痕量气体甲醛（HCHO）的探测能力,并基于实测数据初步评估EMI HCHO产品的探测能力与精度。研究结果表明EMI的辐照度波长校准精度高,但在不同行之间存在较大的不均匀性,其波长校正的精度与仪器的狭缝函数高度依赖行位置的变化。基于EMI的HCHO反演的结果表明,EMI UV2频段的标称信噪比较低,使得利用差分吸收光谱技术（DOAS）得到的HCHO斜柱浓度SCD（Slant Column Density）存在较大的随机误差和拟合残差。现阶段,评估得到的EMI HCHO斜柱浓度的不确定性为1.2×10¹⁶ molec./cm²。与国际同类载荷TROPOMI（TROPospheric Ozone Monitoring Instrument）、OMI（Ozone Monitoring Instrument）的交叉对比验证结果表明,EMI可以捕捉到中国地区夏季HCHO的空间分布特征。EMI & TROPOMI与EMI & OMI的相关系数均大于0.8。但是在华东地区EMI HCHO普遍高于OMI和TROPOMI,其原因需要进一步研究。本文的研究证明了EMI对夏季HCHO的探测潜力及不足,可为后续类似载荷的指标设计和算法研发提供参考。     

MICAPS4预报业务系统建设进展与未来发展

以精细化预报为标志的现代天气预报业务对高时空分辨率气象数据的应用提出了更高的需求,而传统的预报分析制作系统MICAPS3（Meteorological Information Comprehensive Analysis and Processing System Version 3）无法满足符合大数据特征的实时预报相关的数据应用。同时,模式预报和集合预报应用已在各专业预报领域中发挥越来越重要的作用,网格预报产品正在涵盖全部的预报业务流程,而MICAPS3无法提供便捷高效的支撑。为解决上述问题,国家气象中心启动了MICAPS4建设,一方面建立了基于大数据的气象实时预报应用技术体系,解决了气象大数据处理、存储、分析和显示效率的关键技术难题;另一方面搭建了多个满足不同复杂预报业务需求的专业化版本。MICAPS4将信息化技术与预报技术、预报业务流程紧密结合,解决了现代化预报方法的平台化集成应用与精细化预报制作的关键技术难题,基于MICAPS4基础框架的多个专业版在中央气象台和部分省级气象台进行业务化应用推广。基于CIMISS（China Integrated Meteorological Information Sharing System）-MICAPS4的海量数据存储环境,极大减轻了系统部署和本地化工作量,显著提升了数据解析及数据存储访问效率。     

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.     

广东清远早、晚稻稻田甲烷排放的观测研究

分析了2003、2004年广东省清远市郊区早、晚稻稻田甲烷（CH4）的排放通量,结果表明：广东清远早、晚稻稻田CH4排放通量的几何平均值2003年为4,38mg·m^-2·h^-1和6．09mg·m^-2·h^-1;2004年为5．17mg·m^-2·h^-1和8．3mg·m^-2·h^-1,土壤有机质含量是造成2003和2004年CH4排放差异的原因之一。水稻品种的不同,CH4排放通量也有所不同,实验表明,水稻品种“七丝尖”的排放通量比品种“金优99”高1．08mg·m^-2·h^-1,产量却只有64％。此外,与相关的测量结果进行初步比较。     

曝气充氧对城市污染河道内源铵态氮释放的控制

以城市污染河道沉积物和上覆水为研究对象,利用模拟实验方法,探讨不同曝气充氧方式(水曝气EW、底泥曝气ES)对污染河道內源铵态氮(NH4+-N)释放的影响.研究结果发现:从间隙水和沉积物中NH4+-N的削减效果来看,底泥曝气均要优于水曝气;实验结束后,底泥曝气组沉积物与间隙水中NH4+-N含量分别减少63.39%和43.33%,水曝气组分别减少了7.54%和13.98%;从沉积物-水界面NH4+-N的扩散通量变化来看,水曝气组界面通量高于对照组,其变化规律与对照组相似;底泥曝气组沉积物-水界面NH4+-N扩散通量变化过程完全不同于其它两组,在整个试验周期内(除第5 d以外),底泥曝气组的通量低于水曝气组,在第15 d最低,为13.73 mg/(m2.d),仅为水曝气组和对照组的14.68%和19.93%,表明底泥曝气组沉积物NH4+-N的释放潜力低于水曝气组沉积物.     

基于CART决策树的高分二号洞庭湖区湿地分类方法

高分卫星遥感湿地分类的关键在于解决"同物异谱、异物同谱"难题.本文将当前应用前景广泛的亚米级国产高分二号(GF-2)影像和CART决策树面向对象分类算法相结合,以湖南沅江为例进行洞庭湖区典型湿地的分类提取工作,选取包括光谱信息、几何特征、地形特征和纹理特征等多维对象特征对分类器进行训练,构建了多维特征湿地分类方法.试验...     

高分三号影像水体信息提取

国内外针对陆地水体信息提取、洪涝灾害快速响应方面具有较深入的研究,但是多采用发展较早、图像质量可靠的可见光影像及国外星载SAR影像。中国合成孔径雷达（SAR）卫星高分三号（GF-3）已获取了大量多极化、全极化SAR数据,为了将GF-3影像快速应用到环境保护、水资源管理等行业中,本研究分析了水体与其他目标具有的不同后向散射特性,将阈值分割法与马尔可夫随机场（MRF）相结合,发展了一种检测精度较高、自动化程度强的水体信息提取方法。该方法首先通过直方图统计的方法对不同成像模式、不同极化的GF-3影像进行后向散射强度分析,在阈值分割的研究基础上,比较了最大类间方差法（Otsu）和Kittler and Illingworth（KI）二值化法在水体-非水体分类中的效果。然后结合DEM和GF-3轨道参数排除因阴影现象产生的辐射失真对图像概率分布的影响,得到初始的水体信息分布图,再经过Fisher变换和马尔可夫随机场（MRF）的迭代运算,综合利用GF-3影像的多极化信息和空间上下文信息,以最大后验概率准则输出最终的水体分布图。利用了湖南省东北部不同成像模式的两景GF-3影像进行试验,在成像时间接近的光学影像中随机选择检验样点进行精度评价。实验结果表明,KI方法在GF-3水体提取应用中比Otsu方法具有更强的优势,剔除图像阴影区域后,自动化确定的阈值与目视解译阈值更加接近,通过MRF模型优化以后,实现了对水体信息的连贯提取,对图像噪声具有较强的抑制作用。本研究对水体目标的提取精度均达到了85%以上,实验结果精度优于基于光学影像的水体指数法,整个流程需要很少的人工经验参与,具有自动化程度强、检测精度高的优势。