多普勒雷达资料同化在台风“灿都”(2010)预报中的应用研究

文章基于中尺度天气预报模式(WRF)及其三维变分同化系统(WRF-3DVAR), 采用了两部雷达径向风资料, 进行单一时间分析以初始化台风“灿都”(Chanthu), 比较研究了同化雷达径向速度(V_r)对台风“灿都”分析和预报的影响。结果表明: 同化雷达径向风的作用主要体现在台风强度和环流结构的调整, 且在同化达到一定时长后, 对改进同化后的预报分析有积极效应。同化试验改进台风的初始风场以及台风环流中心的热力和动力结构、强度和位置, 进而提高18h预报的台风结构、路径、强度。     

基于ROMS模式的南海SST与SSH四维变分同化研究

卫星遥感观测获得了大量高分辨率的海面实时信息,包括海面温度（SST）和海面高度（SSH）等,同化进入数值模式可有效提升模拟精度。本文基于ROMS模式与四维变分同化方法（4DVAR）,使用AVHRR SST和AVISO SSH数据,开展了南海区域同化实验。为检验同化的效果,分别利用HYCOM再分析资料和Argo温盐实测数据分析了同化结果的海面高度、流场及温盐剖面的精度。对比结果表明,SST和SSH的同化能够改善ROMS的模拟结果:同化后海面高度场能够更为准确地捕捉海洋的中尺度特征,与HYCOM海面高度再分析资料相比,平均绝对偏差和均方根误差分别为0.054 m和0.066 m;与HYCOM 10 m层流场相比,东向与北向流速平均绝对偏差分别为0.12 m/s和0.11 m/s,相比未同化均提升约0.01 m/s;温盐同化结果与Argo温盐实测具有较高的一致性,温度和盐度平均绝对偏差为0.45℃、0.077,均方根误差为0.91℃、0.11,单个的温盐廓线对比说明,同化结果与HYCOM再分析资料精度相当。     

粒度资料数据同化方法初探

本文从资料处理和应用等方面,对粒度数据的同化方法进行初步探讨,建立了粒度数据同化分类标准。从目前研究情况来看,可对不同分析精度粒度数据、不同空间分布范围数据和不同分析方法的数据进行数据同化。不同精度粒度数据同化方法的对比分析结果表明Spline插值效果较为理想。为了避免出现龙格现象,减小误差,将Spline插值和线性插值结合进行插值效果会更好。     

四维变分同化技术在风暴潮数值模拟中的应用

利用区域海洋模式ROMS(Regional Ocean Modelling System)及其四维变分同化模块,建立了具有资料同化能力的东中国海风暴潮数值模式,通过将海洋站水位观测资料同化到风暴潮模式中,提高了模式对风暴潮的模拟精度。四维变分同化技术能够在整个同化时间窗口保持动力协调,使模拟结果在该时间窗口内最大程度的靠近观测,同时,得到了最优预报初始场。利用该模式,对两次温带风暴潮过程进行了数值模拟,结果表明:在同化窗口内,同化对模拟精度有明显的提高;结束同化之后,得到的最优预报初始场对临近预报精度也有一定提高。     

基于潮汐表数据同化的天文潮数值预报模型及其模拟预报效果

潮汐表是利用长期潮汐观测结果经调和分析实现的主要港湾潮汐预报结果,具有较高的预报精度,而通常的天文潮数值预报目前还难以达到潮汐表的预报精度.本研究在建立常规天文潮数值预报模型的基础上,建立了基于潮汐表数据同化的天文潮数值预报模型,并分别采用这2种模型预报福建沿岸海域的天文潮.其结果表明同化模型的预报结果无论是在潮时还是在潮高均明显优于常规模型;同化模型能显著地改善所研究的沿岸海域90个水位点中至少45个水位点的潮汐预报结果,而其他水位点的预报结果也有不同程度地改善.     

Beach Wizard: Nearshore bathymetry estimation through assimilation of model computations and remote observations

A data–model assimilation method (called “Beach Wizard”) is presented with which the nearshore subtidal bathymetry can be accurately estimated based on video-derived observations of wave roller dissipation and variation of the intertidal shoreline, and/or radar-derived observations of wave celerity. Using many consecutive images, these observed properties are compared with numerical model results, and through a simple, optimal least-squares estimator approach the estimated bathymetry is adjusted gradually for each image in order to improve the fit between model output and observations. The key advantages of the technique are that it is based on multiple sources of information (i.e., different remote sensors and/or data products), depends on only a few free parameters (to which the model results are insensitive), and shows good skill. Herein, the technique is applied to a synthetic case and two sets of field data from sites at Duck, NC (USA) and Egmond (The Netherlands). The method, which may be extended with observations of other properties from other sources than the three described in this paper, can deliver coastal state information (i.e., simultaneous updates of bathymetry, waves, and currents) with high temporal and spatial resolution and can be used in conjunction with or instead of in-situ measured data.     

Cycling the Representer Method for 4D-variational data assimilation with the Navy Coastal Ocean Model

The Cycling Representer Method, which is a technique for solving 4D-variational data assimilation problems, has been demonstrated to improve the assimilation accuracy with simpler nonlinear models. In this paper, the Cycling Representer Method will be used to assimilate an array of ADCP velocity observations with the Navy Coastal Ocean Model (NCOM). Experiments are performed in a high-resolution Mississippi Bight domain for the entire month of June, 2004 and demonstrate the usefulness of this assimilation technique in a realistic application.The Representer Method is solved by minimizing a cost function containing the weighted squared errors of velocity measurements, initial conditions, boundary conditions, and model dynamics. NCOM, however, is a highly nonlinear model and in order to converge towards the global minimum of this cost function, NCOM is linearized about a background state using tangent linearization. The stability of this tangent linearized model (TLM) is a very sensitive function of the background state, the level of nonlinearity of the model, open boundary conditions, and the complexity of the bathymetry and flow field. For the Mississippi Bight domain, the TLM is stable for only about a day. Due to this short TLM stability time period, the Representer Method is cycled by splitting the time period of the assimilation problem into short intervals. The interval time period needs to be such that it is short enough for the TLM to be stable, but long enough to minimize the loss of information due to reducing the temporal correlation of the dynamics and data. For each new cycle, a background is created as a nonlinear forecast from the previous cycle’s assimilated solution. This background, along with the data that falls within this new cycle, is then used to calculate a new assimilated solution. The experiments presented in this paper demonstrate the improvement of the assimilated solution as the time window of the cycles is reduced to 1 day. The 1-day cycling, however, was only optimal for the first half of the experiment. This is because there was a strong wind event near the middle of June that significantly reduced the stability of the 1-day cycling and caused substantial errors in the assimilation. Therefore, the 12-h cycling worked best for the second half of the experiment. This paper also demonstrates that the forecast skill is improved as the assimilation system progresses through the cycles.     

一阶有限元浅水模式的变分资料同化

一阶有限元浅水方程的伴随模式应用于气象资料同化场,并利用最优化方法对具有随机扰动的初始场进行优化处理。通过极小化算法,使所定义的目标函数达到最小,从而得到最优的气象要素初始场。数值试验结果表明这种处理是很有效的     

Variational Assimilation of GPS Precipitable Water Vapor and Hourly Rainfall Observations for a Meso-β Scale Heavy Precipitation Event During the 2002 Mei-Yu Season

Recent advances in Global Positioning System (GPS) remote sensing technology allow for a direct estimation of the precipitable water vapor (PWV) from delayed signals transmitted by GPS satellites, which can be assimilated into numerical models with four-dimensional variational (4DVAR) data assimilation. A mesoscale model and its 4DVAR system are used to access the impacts of assimilating GPS-PWV and hourly rainfall observations on the short-range prediction of a heavy rainfall event on 20 June 2002. The heavy precipitation was induced by a sequence of meso-β-scale convective systems (MCS) along the mei-yu front in China.The experiments with GPS-PWV assimilation successfully simulated the evolution of the observed MCS cluster and also eliminated the erroneous rainfall systems found in the experiment without 4DVAR assimilation. Experiments with hourly rainfall assimilation performed similarly both on the prediction of MCS initiation and the elimination of erroneous systems, however the MCS dissipated much sooner than it did in observations. It is found that the assimilation-induced moisture perturbation and mesoscale low-level jet are helpful for the MCS generation and development. It is also discovered that spurious gravity waves may post serious limitations for the current 4DVAR algorithm, which would degrade the assimilation efficiency, especially for rainfall data. Sensitivity experiments with different observations, assimilation windows and observation weightings suggest that assimilating GPS-PWV can be quite effective, even with the assimilation window as short as 1 h. On the other hand, assimilating rainfall observations requires extreme cautions on the selection of observation weightings and the control of spurious gravity waves.     

面向同化应用的红外高光谱探测资料局地综合通道选择方案及在FY-3D/HIRAS中的初步应用

通道选择是红外高光谱探测资料同化的关键技术。为了最大限度提取红外高光谱探测资料观测信息,减少模式在青藏高原等常规观测稀少地区的初始场的误差,不同区域需要选取不同通道进行同化。基于信号自由度的通道选择方法提出一种面向资料同化的红外高光谱资料的局地综合通道选择方案,该方案综合考虑了局地的大气温度垂直分布特征、背景误差协方差、仪器通道的雅克比函数、权重函数和其他影响红外高光谱模拟和同化的因素。针对CMA＿GFS（原GRAPES＿GFS）全球背景误差协方差,在高原和海洋两个典型区域对FY-3D/HIRAS红外高光谱资料的温度通道进行局地综合通道选择,并通过一维变分同化评估了局地综合通道选择方案对分析场的影响。结果表明,高原和海洋两个典型区域的大气温度垂直分布特征、背景误差协方差、模式垂直分层以及各通道的雅克比函数和权重函数均有明显的差异,选出的敏感通道也明显不同,相比较在其他区域选择出的通道,在对应地区选择的通道能够显著提高红外高光谱资料的同化效果。