基于简单模式的集合卡尔曼滤波与粒子滤波的比较研究

集合卡尔曼滤波和粒子滤波是大气海洋领域两种先进的数据同化方法。理论上讲,粒子滤波克服了集合卡尔曼滤波中先验分布的高斯假定。但现有的关于两种方法的比较研究不够全面和系统,基于简单的洛伦兹63模式,重点对基于确定性集合卡尔曼滤波和均权重粒子滤波的数据同化方法开展对比分析,通过对观测误差和模式误差的不同配置,设计了四组试验着重研究两种方法相同试验条件下的同化效果。试验结果表明:与采用最优膨胀系数的集合卡尔曼滤波的同化方法相比,均权重粒子滤波的均方根误差更加依赖于观测信息的质量,但最优膨胀因子的集合卡尔曼滤波的均方根误差低于粒子滤波同化方法。      

谱模式中半隐式时间积分格式稳定性的试验和分析

本文研究了具有参考大气的谱模式半隐式时间差分格式的计算稳定性问题.通过试验与分析,找到了影响这种半隐式格式计算稳定性的关键因子.另外本文还进一步试验了第一作者过去提出的修改的半隐式时间差分格式,这种格式采用了修改半隐式项而不改变重力波性状的做法.取得了稳定的计算结果.     

半拉格朗日、半隐式欧拉方程组大气数值模式研究

介绍了一个具有较高时间积分效率的三维弹性大气数值模式。其中,控制方程为能够描述大气非静力及可压缩性的欧拉方程组,时间积分采用了高效率的“半拉格朗日、半隐式”方案。通过引用一个简化的“云物理过程”参数化方案,针对大气中深厚湿对流过程的若干问题进行了数值试验,结果表明,模式能够对该过程中的一些基本和复杂现象进行有效地模拟。试验结果还表明,动力学框架具有很好的稳定性,能够实现高效率的时间积分,它还具有较好的频散特性,保证了空间计算的精度,从而完好地描述了风暴的形态。     

预估-校正的半隐式半拉格朗日时间积分方案及其在CMA-GFS模式中的应用

CMA-GFS采用的是传统的二时间层半隐式半拉格朗日时间积分方案(SISL).拉格朗日平流速度和非线性项需要采用时间外插进行计算,在急流轴附近等梯度大值区会造成计算不稳定,甚至积分中断现象.文中通过构造预估-校正半隐式半拉格朗日时间积分方案(SISL/P-C),以减少时间外插的影响;半隐式系数由原来的0.72减小到0....     

不同模式误差方案在集合Kalman滤波土壤湿度同化中的比较试验

本文主要目的是探讨不同模式误差方案在土壤湿度同化中的性能。基于集合Kalman滤波同化方法和AVIM (Atmosphere-Vegetation Interaction Model) 陆面模式, 利用理想试验对膨胀因子方案 (Covariance Inflation, 简称CI)、 直接随机扰动方案 (Direct Random Disturbance, 简称DRD)、 误差源扰动方案 (Source Random Disturbance, 简称SRD) 等3种模式误差方案的同化效果进行了比较, 讨论了各方案在不同观测误差、 观测层数、 观测间隔情况下的同化性能。试验结果表明在观测误差估计完全准确的情况下, 3种方案都能获得较好的同化效果, 并且SRD方案相对于真值的均方根误差最小。当观测误差估计不准确时, SRD方案的同化效果仍能基本得以保持, 而CI和DRD方案则对观测误差估计更为敏感, 同化效果下降明显。当同化多层观测时, CI和DRD方案由于难以保持不同层观测之间的匹配关系, 同化结果反而变差, 而SRD方案能有效协调同化多层观测, 增加观测层后同化结果有了进一步的改善。当观测时间间隔较大时, CI和DRD方案的同化效果显著下降; 而SRD方案由于包含了一定的误差订正功能, 在观测稀疏时仍能保持较好的同化效果。     

二阶精度半隐式半拉格朗日轨迹计算和时间积分方案在GRAPES区域模式中的应用

将两时间层稳定外插方案(Stable Extrapolation Two-Time-Level Scheme,SETTLS)引入GRAPES区域模式,并将其用于上游点和非线性项的时间外插计算。对线性项采用二阶精度的非中央权重时间平均,并取等温参考大气的温度大于实际大气平均温度,以保证半隐式积分方案的稳定性。原GRAPES时间积分方案对线性项做一阶非中央权重时间平均,对参考大气的选择并无限制,而为保证稳定性,须取较大的非中央权重系数,但非中央权重系数会对低波数波动产生较强的衰减作用。理想试验结果表明,相比原GRAPES半隐式半拉格朗日(SISL)时间积分方案,新SISL时间积分方案计算稳定且对波动的衰减作用较弱。     

JFNK方法概述及其在大气全隐式非静力模式中的应用方案

首先介绍了近年来新发展的非线性方程全隐式数值求解的JFNK方法,及其在地球流体力学方面应用计算实例.可看到,无论在计算精度还是计算效率方面,全隐式数值求解远远超过常规的半隐式计算格式.其次,还讨论了JFNK方法在气象非静力模式中应用方案,并提出了用静力假定和半隐式差分格式来构造预条件处理器,变三维求解为二维求解,简化了方程组求解难度.该方案不仅可用于差分模式,也为用譜方法求解非静力模式提供可能.     

线性准地转模型中副热带感热加热强迫的定常波

通过求取定常线性准地转位涡模式的解析解,研究了感热加热强迫所激发的副热带定常波的结构特征,讨论了基本流、牛顿冷却及地面摩擦等对定常波振幅和位相的影响。结果表明,东风时定常波在垂直方向上表现为上、下层反位相的第一斜压结构,且地面系统远强于中高层;西风时定常波呈现出向上的传播特征,在高层,随着风速增大振幅随高度的升高有增大趋势。在近地层,东风时气旋（反气旋）主体位于加热西（东）侧;西风时气旋（反气旋）主体位于加热东（西）侧,近地层以上相反。此外,发现东、西风基本流的作用具有对称特征,这与潜热加热显著不同。研究结果还表明,牛顿冷却对定常波有重要影响,基本流越弱影响越显著。在静止大气中,感热加热强迫下无斯韦尔德鲁普（Sverdrup）解,考虑牛顿冷却时,感热强迫在热源范围内的近地层和中高层分别激发出气旋式和反气旋式环流,气旋中心位于加热中心略偏西的位置。在非静止大气中,牛顿冷却项使地面系统中心向上风方向移动,东风时向东移。牛顿冷却对高、低层系统均有削弱作用。地面摩擦则明显不同,它总会使低层系统减弱,高层系统增强。     

线性准地转模型中基本流和辐射冷却对感热加热强迫的副热带高压形态的影响

通过求取定常线性准地转位涡模式的数值解,研究了感热型垂直非均匀分布的冷、热源强迫所激发的副热带环流的结构特征,讨论了副热带背景风场和洋面辐射冷却对洋面副热带高压"三角形偏心"结构形成的重要作用。结果表明,基本流对感热加热强迫的副热带环流有重要影响,当基本流为常数时,感热加热强迫的气旋和反气旋主要集中在对流层中下层,且地面系统远强于中高层。当基本流为非常数时,其经向切变能改变气旋和反气旋中心的经向位置,使它移至0风速所在纬度附近;其垂直变化加强了中高层气旋和反气旋,中心位于对流层上层,与南亚高压的位置基本一致。研究结果还表明,在大洋东部洋面辐射冷却与副热带地区背景风场的共同作用下,形成了洋面副热带高压特有的"三角形偏心"结构。副热带高纬度的西风使感热强迫的洋面副热带高压东移,低纬度的东风使其西移,形成东北—西南走向的"平行四边形"结构,且中心位于大洋西部。大洋东部强洋面辐射冷却激发的洋面反气旋加强了大洋东部的副热带高压,使其中心东移至大洋东部,从而表现出东北—西南走向的"三角形偏心"结构。     

Implementation of a one-dimensional enthalpy sea-ice model in a simple pycnocline prediction model for sea-ice data assimilation studies

To further explore enthalpy-based sea-ice assimilation, a one-dimensional(1D) enthalpy sea-ice model is implemented into a simple pycnocline prediction model. The 1D enthalpy sea-ice model includes the physical processes such as brine expulsion, flushing, and salt diffusion. After being coupled with the atmosphere and ocean components, the enthalpy sea-ice model can be integrated stably and serves as an important modulator of model variability. Results from a twin experiment show that the sea-ice data assimilation in the enthalpy space can produce smaller root-mean-square errors of model variables than the traditional scheme that assimilates the observations of ice concentration, especially for slow-varying states. This study provides some insights into the improvement of sea-ice data assimilation in a coupled general circulation model.     

Comparison of nonlinear local Lyapunov vectors with bred vectors,random perturbations and ensemble transform Kalman filter strategies in a barotropic model

The breeding method has been widely used to generate ensemble perturbations in ensemble forecasting due to its simple concept and low computational cost. This method produces the fastest growing perturbation modes to catch the growing components in analysis errors. However, the bred vectors(BVs) are evolved on the same dynamical flow, which may increase the dependence of perturbations. In contrast, the nonlinear local Lyapunov vector(NLLV) scheme generates flow-dependent perturbations as in the breeding method, but regularly conducts the Gram–Schmidt reorthonormalization processes on the perturbations. The resulting NLLVs span the fast-growing perturbation subspace efficiently, and thus may grasp more components in analysis errors than the BVs.In this paper, the NLLVs are employed to generate initial ensemble perturbations in a barotropic quasi-geostrophic model.The performances of the ensemble forecasts of the NLLV method are systematically compared to those of the random perturbation(RP) technique, and the BV method, as well as its improved version—the ensemble transform Kalman filter(ETKF)method. The results demonstrate that the RP technique has the worst performance in ensemble forecasts, which indicates the importance of a flow-dependent initialization scheme. The ensemble perturbation subspaces of the NLLV and ETKF methods are preliminarily shown to catch similar components of analysis errors, which exceed that of the BVs. However, the NLLV scheme demonstrates slightly higher ensemble forecast skill than the ETKF scheme. In addition, the NLLV scheme involves a significantly simpler algorithm and less computation time than the ETKF method, and both demonstrate better ensemble forecast skill than the BV scheme.     

Ensemble data assimilation in a simple coupled climate model: The role of ocean-atmosphere interaction

A conceptual coupled ocean-atmosphere model was used to study coupled ensemble data assimilation schemes with a focus on the role of ocean-atmosphere interaction in the assimilation. The optimal scheme was the fully coupled data assimilation scheme that employs the coupled covariance matrix and assimilates observations in both the atmosphere and ocean. The assimilation of synoptic atmospheric variability that captures the temporal fluctuation of the weather noise was found to be critical for the estimation of not only the atmospheric, but also oceanic states. The synoptic atmosphere observation was especially important in the mid-latitude system, where oceanic variability is driven by weather noise. The assimilation of synoptic atmospheric variability in the coupled model improved the atmospheric variability in the analysis and the subsequent forecasts, reducing error in the surface forcing and, in turn, in the ocean state. Atmospheric observation was able to further improve the oceanic state estimation directly through the coupled covariance between the atmosphere and ocean states. Relative to the mid-latitude system, the tropical system was influenced more by ocean-atmosphere interaction and, thus, the assimilation of oceanic observation becomes more important for the estimation of the ocean and atmosphere.     

The Interannual Variability of Climate in a Coupled Ocean-Atmosphere Model

In this paper, the interannual variability simulated by the coupled ocean-atmosphere general circulation model of the Institute of Atmospheric Physics (IAP CGCM) in 40 year integrations is analyzed, and compared with that by the corresponding IAP AGCM which uses the climatic sea surface temperature as the boundary condition in 25 year integrations.The mean climatic states of January and July simulated by IAP CGCM are in good agreement with that by IAP AGCM, i.e., no serious ‘climate drift’ occurs in the CGCM simulation. A comparison of the results from AGCM and CGCM indicates that the standard deviation of the monthly averaged sea level pressure simulated by IAP CGCM is much greater than that by IAP AGCM in tropical region. In addition, both Southern Oscillation (SO) and North Atlantic Oscillation (NAO) can be found in the CGCM simulation for January, but these two oscillations do not exist in the AGCM simulation.The interannual variability of climate may be classified into two types: one is the variation of the annual mean, another is the variation of the annual amplitude. The ocean-atmosphere interaction mainly increases the first type of variability. By means of the rotated EOF, the most important patterns corresponding to the two types of interannual variability are found to have different spatial and temporal characteristics.     

基于四维灾体模型的浙江省气象灾害损失年景评价

应用浙江省气象灾情统计年鉴资料,运用四维灾体评估模型,对浙江省的气象灾害损失进行科学评估。浙江省2003—2017年灾害损失年景评价分析结果显示,2004年和2005年为气象灾害损失明显偏重年景,进入2010年以来,2013年为年景最差年份;台风灾害损失在每年浙江省气象灾害损失年景评价中具有非常重要的作用,重大台风事件易造成气象灾害偏重或明显偏重的损失年景。评估结果表明,四维灾体模型下的气象灾害评估较为科学客观。     

区域气候模式分辨率对中国夏季气温模拟影响的评估

为评估区域气候模式中分辨率对中国夏季气温模拟的影响,选用区域气候模式RegCM3,针对中国范围同一区域设置3种水平分辨率(30、60和90 km)和3种垂直分辨率(14、18和23层)就2000-2004年夏季地表气温进行模拟试验,采用模拟偏差、均方根误差、空间相关系数等多种参数,评估了气温模拟对分辨率的敏感性.结果表...     

基于GWR模型的典型区域GPM数据降尺度研究——以浙江省为例

为提高研究区域的降尺度效果,基于地理加权回归法(Geographically Weighted Regression, GWR),选取全球降水计划(Global Precipitation Measurement, GPM)3IMERGM产品,以数字高程模型(Digital Elevation Model, DEM)作为控制解释变量,将其分别与解释变量水汽通量散度、气温构建两个降尺度模型、与解释变量归一化植被指数(Normalized Difference Vegetation Index, NDVI)构建对照降尺度模型,对浙江省进行降尺度研究。利用研究区域内气象站点的实测数据,对由不同解释变量构建的3个降尺度模型的降尺度结果进行对比分析及精度验证。结果表明：构建的3个降尺度模型中,引入解释变量水汽通量散度构建的降尺度模型的综合效果优于其余两种模型,水汽通量散度较NDVI、气温更适合作为解释变量。构建的降尺度模型有效地提高了GPM数据的空间分辨率(由0.1°提升至1 km),降尺度数据维持了精度且能够更真实反映研究区域内的降水量分布情况。     

陆面过程模式CLM4.5在半干旱区退化草原站的模拟性能评估

利用国际协同强化观测期(CEOP)在中国半干旱区退化草地站——通榆站的观测资料,对一个较为完善的陆面过程模式NCAR_CLM4.5(Community Land Model 4.5)的模拟性能进行检验。模拟结果与观测资料的对比表明,CLM4.5能很好地模拟出观测站点的辐射通量、水热交换、土壤温湿的空间分布和时间变化特征。但地表吸收的辐射模拟值略低,土壤湿度偏低,地表吸收的辐射及土壤温度等日变化略大;大气强迫变量处于某些特定的形势下时,模拟存在较大误差,如8月底的模拟。此外,冬季辐射通量、水热交换以及土壤温湿的模拟均存在较大误差,说明CLM4.5模式在冬季地表物理过程的参数化方案上需要进一步改进。