模式参数的不确定性对日本南部黑潮大弯曲路径预报的影响

基于1.5层浅水方程模式,利用条件非线性最优参数扰动(CNOP-P)方法,研究模式参数的不确定性对黑潮大弯曲路径预报的影响。研究表明,单个模式参数误差如侧向摩擦系数误差、界面摩擦系数误差以及在不同季节具有不同约束的风应力大小误差,对黑潮大弯曲路径预报的影响较小,并且对背景流场的选取具有一定的敏感性;所有模式参数误差同时存在时对黑潮大弯曲路径预报具有一定的影响,并且预报结果在9个月左右不能被接受。因此,要提高黑潮大弯曲路径的预报技巧,模式中的参数需要给出更好的估计。     

Identification of the sensitive area for targeted observation to improve vertical thermal structure prediction in summer in the Yellow Sea

The sensitive area of targeted observations for short-term(7 d) prediction of vertical thermal structure(VTS) in summer in the Yellow Sea was investigated. We applied the Conditional Nonlinear Optimal Perturbation(CNOP)method and an adjoint-free algorithm with the Regional Ocean Modeling System(ROMS). We used vertical integration of CNOP-type temperature errors to locate the sensitive areas, where reduction of initial errors is expected to yield the greatest improvement in VTS prediction for the selected verification area. The identified sensitive areas were northeast-southwest orientated northeast to the verification area, which were possibly related to the southwestward background currents. Then, we performed a series of sensitivity experiments to evaluate the effectiveness of the identified sensitive areas. Results show that initial errors in the identified sensitive areas had the greatest negative effect on VTS prediction in the verification area compared to errors in other areas(e.g., the verification area and areas to its east and northeast). Moreover, removal of initial errors through deploying simulated observations in the identified sensitive areas led to more refined prediction than correction of initial conditions in the verification area itself. Our results suggest that implementation of targeted observation in the CNOP-based sensitive areas is an effective method to improve short-term prediction of VTS in summer in the Yellow Sea.     

Zebiak-Cane模式中条件非线性最优扰动对ENSO春季预报障碍的影响

使用Zebiak-Cane模式和条件非线性最优扰动(CNOP)方法,研究初始误差和参数误差共同作用对ENSO春季预报障碍现象的影响。选取模式中的8个El Ni?o事件,包括4次强事件和4次弱事件,每个El Ni?o事件又分别从8个不同的起始时间做1 a的预报,这样一共64个预报实验。对每个实验分别计算CNOP误差(初始误差和参数误差同时存在时的最优误差),通过分析误差增长,发现CNOP误差引起的1 a后的预报误差随着初始预报时间的不同有较大差异,并且不同强度的El Ni?o事件也会影响CNOP误差的发展,增长位相中强事件的预报误差要比弱事件的预报误差大一些;而衰减位相中恰恰相反,弱事件的预报误差要比强事件的预报误差要大一些;同时也发现高频El Ni?o事件对误差增长率的影响较大。本结论有助于提高Zebiak-Cane模式预报ENSO的技巧。     

日本南部黑潮路径发生弯曲的最优前期征兆及其发展机制

基于正压出入流模式, 利用条件非线性最优扰动(CNOP)方法研究初始异常的位置与模态对日本南部黑潮路径变异的影响。以模式模拟出的黑潮平直路径的平衡态作为参考态, 计算CNOP, 考察该扰动随时间的发展, 并与随机扰动的发展进行对比。结果表明, CNOP 能够导致黑潮弯曲路径发生, 随机扰动则不能。因此, CNOP 可以作为导致日本南部黑潮路径发生弯曲的一种最优前期征兆。通过分析CNOP 和随机扰动的发展过程, 可以得出: (1) CNOP 使黑潮发展成弯曲路径的过程是一个气旋涡向下游传播并增长的过程。(2) 气旋涡的向东传播都是非线性项的作用, 也就是涡度平流造成的。(3) CNOP和随机扰动发展过程中所产生的气旋涡均会传播到下游区域, 但是CNOP 产生的气旋涡能够增强, 最终导致弯曲路径发生, 而随机扰动产生的气旋涡则会减弱, 并不能导致弯曲路径发生。分析发现, 在CNOP 实验中, 非线性作用使气旋涡增大; 但在随机扰动实验中, 非线性作用使气旋涡减弱, 所以非线性作用对日本南部黑潮路径发生弯曲有重要影响。(4) 底摩擦效应对日本南部黑潮路径变异影响较小。本文揭示的黑潮路径发生弯曲的最优前期征兆及其非线性发展机制, 对提高黑潮路径变异的预报技巧具有重要意义。     

初始误差对双环流变异可预报性的影响

使用球坐标下1.5 层约化重力浅水模式模拟海洋风生双环流, 结果显示双环流射流存在拉伸模态和收缩模态间的年际变化。以双环流从拉伸模态向收缩模态的转变过程为背景场, 利用条件非线性最优扰动(CNOP)方法, 考察初始误差对双环流变异可预报性的影响, 得到两类初始误差: 全局CNOP型和局部CNOP(LCNOP)型, 两类初始误差对双环流变异的影响几乎相反。通过考察误差发展, 发现在射流从拉伸模态向收缩模态转变过程中, CNOP 型初始误差使射流弯曲程度变大, 并在预报时刻导致涡脱落; 而LCNOP 型初始误差则使射流弯曲程度变小。相比LCNOP, CNOP 型初始误差引起更大预报误差, 导致双环流变异的预报技巧下降更多。两类误差得到较大发展的区域可能存在正压不稳定, 使误差能够不断从背景场吸收能量进而得到快速发展。给出了两类使双环流变异预报技巧下降最大的初始误差, 在实际的数值预报中减少这两种类型的误差, 将有助于提高双环流变异的预报技巧。     

在Zebiak-Cane模式中观测MJO强迫对ENSO可预报性的影响, 第1部分:对最大预报误差的影响

With the observational wind data and the Zebiak-Cane model, the impact of Madden-Julian Oscillation (MJO) as external forcing on El Ni(n)o–Southern Oscillation (ENSO) predictability is studied. The obs...     

垂向湍流扩散系数的不确定性对深层叶绿素最大值现象模拟的影响

深层叶绿素最大值(Deep Chlorophyll Maximum, DCM)现象的数值模拟是研究海洋表层生态系统和全球碳循环的重要组成部分之一。但是由于自身的复杂性和观测的局限性,数值模式中物理参数的不确定性给模拟结果带来了一定程度的误差。其中,垂向湍流扩散系数(vertical turbulence diffusion)是模式所包含的物理参数中很难直接通过观测来确定的参数,它在模式中的来源和取值往往具有很大的不确定性。本文通过条件非线性最优(参数)扰动(Conditional nonlinear optimal perturbation related toparameter, CNOP-P)方法,研究了垂向湍流扩散系数的不确定性对模式模拟结果的影响。我们发现,垂向湍流扩散系数对 DCM 模拟产生最大影响的 CNOP 型扰动位于生产力层的上半部分。并且,去掉生产力层内湍流扩散系数的误差,模式模拟的改进程度最高达到了 80%。可见,垂向湍流扩散对生态系统的发展和保持起着极其重要的作用,改进垂向湍流扩散系数的不确定性,对 DCM 的数值模拟有着重要意义。     

Spatio-temporal evolution of perturbations in ensembles initialized by bred, Lyapunov and singular vectors

We study the evolution of finite perturbations in the Lorenz '96 model, a meteorological toy model of the atmosphere. The initial perturbations are chosen to be aligned along different dynamic vectors: bred, Lyapunov, and singular vectors. Using a particular vector determines not only the amplification rate of the perturbation but also the spatial structure of the perturbation and its stability under the evolution of the flow. The evolution of perturbations is systematically studied by means of the so-called mean-variance of logarithms diagram that provides in a very compact way the basic information to analyse the spatial structure. We discuss the corresponding advantages of using those different vectors for preparing initial perturbations to be used in ensemble prediction systems, focusing on key properties: dynamic adaptation to the flow, robustness, equivalence between members of the ensemble, etc. Among all the vectors considered here, the so-called characteristic Lyapunov vectors are possibly optimal, in the sense that they are both perfectly adapted to the flow and extremely robust.     

Weak and strong constraint data assimilation in the inverse Regional Ocean Modeling System (ROMS): Development and application for a baroclinic coastal upwelling system

We describe the development and preliminary application of the inverse Regional Ocean Modeling System (ROMS), a four dimensional variational (4DVAR) data assimilation system for high-resolution basin-wide and coastal oceanic flows. Inverse ROMS makes use of the recently developed perturbation tangent linear (TL), representer tangent linear (RP) and adjoint (AD) models to implement an indirect representer-based generalized inverse modeling system. This modeling framework is modular. The TL, RP and AD models are used as stand-alone sub-models within the Inverse Ocean Modeling (IOM) system described in [Chua, B.S., Bennett, A.F., 2001. An inverse ocean modeling system. Ocean Modell. 3, 137–165.]. The system allows the assimilation of a wide range of observation types and uses an iterative algorithm to solve nonlinear assimilation problems. The assimilation is performed either under the perfect model assumption (strong constraint) or by also allowing for errors in the model dynamics (weak constraints). For the weak constraint case the TL and RP models are modified to include additional forcing terms on the right hand side of the model equations. These terms are needed to account for errors in the model dynamics.Inverse ROMS is tested in a realistic 3D baroclinic upwelling system with complex bottom topography, characterized by strong mesoscale eddy variability. We assimilate synthetic data for upper ocean (0–450 m) temperatures and currents over a period of 10 days using both a high resolution and a spatially and temporally aliased sampling array. During the assimilation period the flow field undergoes substantial changes from the initial state. This allows the inverse solution to extract the dynamically active information from the synthetic observations and improve the trajectory of the model state beyond the assimilation window. Both the strong and weak constraint assimilation experiments show forecast skill greater than persistence and climatology during the 10–20 days after the last observation is assimilated.Further investigation in the functional form of the model error covariance and in the use of the representer tangent linear model may lead to improvement in the forecast skill.     

海洋资料同化对气候季节-年际预测技巧及初始场的影响试验

集合卡尔曼滤波(Ensemble Kalman filter, EnKF)是一种国内外广泛使用的海洋资料同化方案, 用集合成员的状态集合表征模式的背景误差协方差, 结合观测误差协方差, 计算卡尔曼增益矩阵, 有效地将观测信息添加到模式初始场中。由于季节、年际预测很大程度上受到初始场的影响, 因此资料同化可以提高模式的预测性能。本文在NUIST-CFS1.0预测系统逐日SST nudging的初始化方案上, 利用EnKF在每个月末将全场(full field)海表温度(sea surface temperature, SST)、温盐廓线(in-situ temperature and salinity profiles, T-S profiles)以及卫星观测海平面高度异常(sea level anomalies, SLA)观测资料同化到模式初始场中, 对比分析了无海洋资料同化以及加入同化后初始场的区别、加入海洋资料同化后模式提前1~24个月预测性能的差异以及对于厄尔尼诺-南方涛动(El Niño-southern oscillation, ENSO)预测技巧的影响。结果表明, 加入海洋资料同化能有效地改进初始场, 并且呈现随深度增加初始场改进越显著的特征。加入同化后, 对全球SST、次表层海水温度的平均预测技巧均有一定的提高, 也表现出随深度增加预测技巧改进越明显的特征。但加入海洋资料同化后, 模式对ENSO的预测技巧有所下降, 可能是由于模式误差的存在, 使得同化后的预测初始场从接近观测的状态又逐渐恢复到与模式动力相匹配的状态, 加剧了赤道太平洋冷舌偏西、中东部偏暖的气候平均态漂移。     

Predicting the East Australian Current

Results are presented from an ensemble prediction study (EPS) of the East Australian Current (EAC) with a specific focus on the examination of the role of dynamical instabilities and flow dependent growing errors. The region where the EAC separates from the coast, is characterized by significant mesoscale eddy variability, meandering and is dominated by nonlinear dynamics thereby representing a severe challenge for operational forecasting. Using analyses from OceanMAPS, the Australian operational ocean forecast system, we explore the structures of flow dependent forecast errors over 7 days and examine the role of dynamical instabilities. Forecast ensemble perturbations are generated using the method of bred vectors allowing the identification of those perturbations to a given initial state that grow most rapidly. We consider a 6 month period spanning the Austral summer that corresponds to the season of maximum eddy variability. We find that the bred vector (BV) structures occur in areas of instability where forecast errors are large and in particular in regions associated with the Tasman Front and EAC extension. We also find that very few BVs are required to identify these regions of large forecast error and on that basis we expect that even a small BV ensemble would prove useful for adaptive sampling and targeted observations. The results presented also suggest that it may be beneficial to supplement the static background error covariances typically used in operational ocean data assimilation systems with flow dependent background errors calculated using a relatively cheap EPS.     

几种统计模型对热带印度洋海温异常的预报

本文利用神经网络模型、多元线性回归模型和马尔科夫模型分别建立了统计预报模型,对热带印度洋海表温度异常(SSTA)和印度洋偶极子(IOD)指数进行了63 a的长时间回报实验,并详细比较了线性和非线性统计预报模型的差异。结果表明:统计模型对IOD指数的预报技巧和现有动力模式预报技巧相差不大,对偶极子指数(DMI)有效预报时效为3个月,东极子指数(EIO)为5～6个月,西极子指数(WIO)达到8～9个月。IOD事件强烈的季节锁相特性使得对秋季的DMI指数可以提前4个月做出有效预报。加入同期的ENSO指数来预报IOD指数,能有效地提高IOD预报技巧,特别是对IOD峰值的预报。复杂的神经网络模型和简单的多元线性回归模型在对SSTA和IOD指数的预报具有同等的效果。     

Doppler radar radial winds in HIRLAM. Part II: optimizing the super-observation processing

Doppler radar radial wind observations are modelled in numerical weather prediction (NWP) within observation errors which consist of instrumental, modelling and representativeness errors. The systematic and random modelling errors can be reduced through a careful design of the observation operator (Part I). The impact of the random instrumental and representativeness errors can be decreased by optimizing the processing of the so-called super-observations (spatial averages of raw measurements; Part II).
The super-observation processing is experimentally optimized in this article by determining the optimal resolution for the super-observations for different NWP model resolutions. A 1-month experiment with the HIRLAM data assimilation and forecasting system is used for radial wind data monitoring and for generating observation minus background (OmB) differences. The OmB statistics indicate that the super-observation processing reduces the standard deviation of the radial wind speed OmB difference, while the mean vector wind OmB difference tends to increase. The optimal parameter settings correspond at a measurement range of 50 km (100 km) to an averaging area of 1.7 km² (7.3 km²).
In conclusion, an accurate and computationally feasible observation operator for the Doppler radar radial wind observations is developed (Part I) and a super-observation processing system is optimized (Part II).     

Optimal design of biological sampling programs using the analysis of variance

An approach for optimizing environmental monitoring programs designed for the detection of biological or ecological change is developed. The monitoring designs use a factorial treatment structure that allows for seasonality, pre/post event status (where the event is typically an external perturbation), paired treatment-control stations, and auxiliary effects such as water depth variation. The sampling cost is assumed to be the sum of a fixed initial cost plus components for replication, number of stations visited, and number of sampling occasions. The optimization takes two forms: minimization of sampling cost for a given detectability of change (power), and maximization of power for a fixed cost. Both are solved using a Lagrange multiplier formulation. The resulting system of constrained nonlinear equations is solved using a modified Newton-Raphson method. Sensitivity analyses show that the monitoring design is relatively robust with respect to the decision variables as long as the power associated with the optimal design is modest.     

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.     

高分辨率海水表层二氧化碳分压重构——以大西洋为例

海洋是自然界中重要的碳汇,海-气二氧化碳通量通常利用大气和海水表层的二氧化碳分压(pCO₂)差进行估算。受制于时空分布不均匀的观测样本和预测数据,目前已有海水表层二氧化碳分压的重构结果在空间分辨率上仍有较大可提升空间。为在高空间分辨率下更好地拟合时空变化,基于表层大洋二氧化碳地图(SOCAT)的海水表层二氧化碳逸度(f CO₂)数据集和遥感卫星等多源数据,利用XGBoost模型建立了海水表层二氧化碳分压值与海洋物理、生物、光学等要素的非线性关系,并根据样本时空频率构建权重模型,最终重构了2000-2018年大西洋0.041 7°×0.041 7°下月度海水表层二氧化碳分压分布。预测结果的相关系数为0.966,均方根误差为8.087μatm,平均偏差为4.012μatm,与同类重构结果相比,海水表层二氧化碳分压的时空变化趋势一致性强,且在空间分辨率上具有优势。     

海浪初始谱误差演变初步分析

为了分析海浪初始场误差对于短期预报的影响,导出了海浪谱能量平衡方程的初始扰动谱线性演化方程,据此分析了数值实践中扰动谱的增长和衰减过程;在特定条件下,将初始扰动谱线性演化方程做了简化,依此考虑扰动谱持续时效问题。分析结果表明,初始谱误差是以指数形式增长或衰减的,即使在衰减情况下。持续时效也至少有1～2d的量级,初始场精度是影响海浪短期预报准确度的一个重要因素。     

印度洋偶极子预报技巧在多模式中的对比研究

本文采用北美多模式集合产品数据,分析了印度洋偶极子指数在不同模式中实际预报技巧和潜在可预报性的差异,并进一步探究其可能的原因。结果表明,印度洋偶极子的有效预报时效在不同模式中差别较大,从2个月到4个月不等。其中东极子海温异常在不同模式中预报技巧的差别较西极子海表面温度异常更明显,表明模式误差和初始误差对东极子海表面温度异常演变的影响更为显著。另外,印度洋偶极子的实际预报技巧和潜在预报技巧存在明显的线性关系,潜在预报技巧高的模式,其实际预报技巧也高。最后,本文诊断、分析了厄尔尼诺对印度洋偶极子预报技巧的影响,发现在厄尔尼诺和印度洋偶极子相关性较高的气候模式中,印度洋偶极子实际预报技巧也较高。     

Sea surface temperature predictability in the North Pacific from multi-model seasonal forecast

Sea surface temperature (SST) prediction based on the multi-model seasonal forecast with numerous ensemble members have more useful skills to estimate the possibility of climate events than individual models. Hence, we assessed SST predictability in the North Pacific (NP) from multi-model seasonal forecasts. We used 23 years of hindcast data from three seasonal forecasting systems in the Copernicus Climate Change Service to estimate the prediction skill based on temporal correlation. We evaluated the predictability of the SST from the ensemble members' width spread, and co-variability between the ensemble mean and observation. Our analysis revealed that areas with low prediction skills were related to either the large spread of ensemble members or the ensemble members not capturing the observation within their spread. The large spread of ensemble members reflected the high forecast uncertainty, as exemplified in the Kuroshio–Oyashio Extension region in July. The ensemble members not capturing the observation indicates the model bias; thus, there is room for improvements in model prediction. On the other hand, the high prediction skills of the multi-model were related to the small spread of ensemble members that captures the observation, as in the central NP in January. Such high predictability is linked to El Niño Southern Oscillation (ENSO) via teleconnection.

     

杭州湾三维悬浮泥沙输运模型初始场的伴随法反演研究

在悬沙输运的数值模拟中,初始场的准确给定至关重要。目前诸多确定初始场的方案均存在一定的缺陷,初始场的准确性有待进一步提高。本文基于一个三维悬沙输运伴随同化模型,通过孪生实验和实际实验,对模型初始场进行了伴随法反演研究。在孪生实验中,首先验证了初始场的相对重要性;其次,探讨了初始场的反演结果对优化算法、初始猜测值、卫星遥感数据数量、同化时间窗口宽度和背景流场误差的敏感性;最后,比较了伴随法和插值法重构初始场的能力。孪生实验结果表明:最速下降法对初始场的优化反演效果要优于三种共轭梯度法和有限记忆BFGS法;初始场的反演效果对初始猜测值、卫星遥感数据数量和背景流场误差不敏感,而对同化窗口宽度较为敏感;与插值法相比,伴随法是重构模型初始场更有效的手段。实际实验中,在杭州湾海域同化典型的小潮时期和大潮时期的GOCI卫星遥感资料所得表层悬沙浓度数据,优化反演了初始场。实际实验结果表明:数据同化后,得到了更符合实际的最优初始场,表明伴随法是实现初始场优化反演的有效手段。该研究对进一步改进悬沙输运模型的初始化方案具有一定的参考价值,也对其他数值模型的初始化方案具有一定的借鉴价值。