Cyclone induced storm surge and flood forecasting in the northern Bay of Bengal

A cyclone induced storm surge and flood forecasting system that has been developed for the northern Bay of Bengal is presented. The developed system includes a cyclone forecasting model that uses statistical models for forecasting of the cyclone track and maximum wind speed, and an analytical cyclone model for generation of cyclone wind and pressure fields. A data assimilation system has been developed that allows updating of the cyclone parameters based on air pressure and wind speed observations from surface meteorological stations. The forecasted air pressure and wind fields are used as input in a 2D hydrodynamic model for forecasting storm surge levels and associated flooding. An efficient uncertainty prediction procedure based on Harr's point estimation method has been implemented as part of the forecasting system for prediction of the uncertainties of the forecasted storm surge levels and inundation areas caused by the uncertainties in the cyclone track and wind speed forecasts. The developed system is applied on a severe cyclone that hit Bangladesh in April 1991. The simulated storm surge and associated flooding are highly sensitive to the cyclone data. The cyclone data assimilation system provides a more accurate cyclone track when the cyclone approaches the coastline, which results in a significant improvement of the storm surge and flood predictions. Application of the uncertainty prediction procedure shows that the large uncertainties of the cyclone track and intensity forecasts result in large uncertainties of the forecasted storm surge levels and flood extend. The forecasting system shows very good forecasting capabilities up to 24 h before the actual landfall.     

Kalman滤波风暴潮数值预报四维同化模式研究进展

本文首先介绍了Kalman滤波在风暴潮数值预报中的应用，特别介绍了近年来国际上发展的一些在实际中可行的次优化Kalman滤波算法。并通过一个稳态Kalman滤波风暴潮数值预报模式的实例表明，使用资料同化可以明显改进风暴潮后报结果；资料同化能够提供更为合理的预报初始场，对风暴潮的短期预报有较明显的改进。一旦没有资料同化到模式中去，预报结果很快接近确定性模式。     

Improvement of short-termforecasting in the northwest Pacific through assimilating Argo data into initial fields

The impact of assimilating Argo data into an initial field on the short-term forecasting accuracy of temper- ature and salinity is quantitatively estimated by using a forecasting system of the western North Pacific, on the base of the Princeton ocean model with a generalized coordinate system （POMgcs）. This system uses a sequential multigrid three-dimensional variational （3DVAR） analysis scheme to assimilate observation da- ta. Two numerical experiments were conducted with and without Argo temperature and salinity profile data besides conventional temperature and salinity profile data and sea surface height anomaly （SSHa） and sea surface temperature （SST） in the process of assimilating data into the initial fields. The forecast errors are estimated by using independent temperature and salinity profiles during the forecasting period, including the vertical distributions of the horizontally averaged root mean square errors （H-RMSEs） and the horizontal distributions of the vertically averaged mean errors （MEs） and the temporal variation of spatially averaged root mean square errors （S-RMSEs）. Comparison between the two experiments shows that the assimila- tion of Argo data significantly improves the forecast accuracy, with 24% reduction of H-RMSE maximum for the temperature, and the salinity forecasts are improved more obviously, averagely dropping of 50% for H-RMSEs in depth shallower than 300 m. Such improvement is caused by relatively uniform sampling of both temperature and salinity from the Argo drifters in time and space.     

风暴潮数值模拟中风应力拖曳系数的伴随法反演研究

借助伴随同化方法,利用实测水位资料,对空间分布的风应力拖曳系数做了反演研究.假定风应力拖曳系数具有空间分布特征,即在模拟海区中均匀选取一些独立点,利用这些独立点的风应力拖曳系数线性插值得到全场的风应力拖曳系数.同化实验结果表明,采用空间分布的风应力拖曳系数得到的模拟结果,明显优于将风应力拖曳系数取为常数和依照经验公式计...     

平均海平面上升对东中国海台风风暴潮影响的数值模拟

本文采用ECOMSED模式模拟了影响东中国海的3次台风过程,经与实测资料对比验证了模型的可靠性。在此基础上设计了敏感性试验以考察海平面上升对风暴潮造成的影响。结果表明,海平面上升对风暴潮的影响在空间分布上不是一致的,且因具体台风过程而异。整体而言,海平面上升对风暴潮造成的影响有限。海平面上升0．5m,大部分站位风暴增水极值基本不变,即使海平面上升5m大部分站位的风暴增水极值相对改变量都小于10％。     

The representer method, the ensemble Kalman filter and the ensemble Kalman smoother: A comparison study using a nonlinear reduced gravity ocean model

This paper compares contending advanced data assimilation algorithms using the same dynamical model and measurements. Assimilation experiments use the ensemble Kalman filter (EnKF), the ensemble Kalman smoother (EnKS) and the representer method involving a nonlinear model and synthetic measurements of a mesoscale eddy. Twin model experiments provide the “truth” and assimilated state. The difference between truth and assimilation state is a mispositioning of an eddy in the initial state affected by a temporal shift. The systems are constructed to represent the dynamics, error covariances and data density as similarly as possible, though because of the differing assumptions in the system derivations subtle differences do occur. The results reflect some of these differences in the tangent linear assumption made in the representer adjoint and the temporal covariance of the EnKF, which does not correct initial condition errors. These differences are assessed through the accuracy of each method as a function of measurement density. Results indicate that these methods are comparably accurate for sufficiently dense measurement networks; and each is able to correct the position of a purposefully misplaced mesoscale eddy. As measurement density is decreased, the EnKS and the representer method retain accuracy longer than the EnKF. While the representer method is more accurate than the sequential methods within the time period covered by the observations (particularly during the first part of the assimilation time), the representer method is less accurate during later times and during the forecast time period for sparse networks as the tangent linear assumption becomes less accurate. Furthermore, the representer method proves to be significantly more costly (2–4 times) than the EnKS and EnKF even with only a few outer iterations of the iterated indirect representer method.     

The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems: Part III - Observation impact and observation sensitivity in the California Current System

The critical role played by observations during ocean data assimilation was explored when the Regional Ocean Modeling System (ROMS) 4-dimensional variational (4D-Var) data assimilation system was applied sequentially to the California Current circulation. The adjoint of the 4D-Var gain matrix was used to quantify the impact of individual observations and observation platforms on different aspects of the 4D-Var circulation estimates during both analysis and subsequent forecast cycles. In this study we focus on the alongshore and cross-shore transport of the California Current System associated with wind-induced coastal upwelling along the central California coast. The majority of the observations available during any given analysis cycle are from satellite platforms in the form of SST and SSH, and on average these data exert the largest controlling influence on the analysis increments and forecast skill of coastal transport. However, subsurface in situ observations from Argo floats, CTDs, XBTs and tagged marine mammals often have a considerable impact on analyses and forecasts of coastal transport, even though these observations represent a relatively small fraction of the available data at any particular time.During 4D-Var the observations are used to correct for uncertainties in the model control variables, namely the initial conditions, surface forcing, and open boundary conditions. It is found that correcting for uncertainties in both the initial conditions and surface forcing has the largest impact on the analysis increments in alongshore transport, while the cross-shore transport is controlled mainly by the surface forcing. The memory of the circulation associated with the control variable increments was also explored in relation to 7 day forecasts of the coastal circulation. Despite the importance of correcting for surface forcing uncertainties during analysis cycles, the coastal transport during forecast cycles initialized from the analyses has less memory of the surface forcing corrections, and is controlled primarily by the analysis initial conditions.Using the adjoint of the entire 4D-Var system we have also explored the sensitivity of the coastal transport to changes in the observations and the observation array. A single integration of the adjoint of 4D-Var can be used to predict the change that occurs when observations from different platforms are omitted from the 4D-Var analysis. Thus observing system experiments can be performed for each data assimilation cycle at a fraction of the computational cost that would be required to repeat the 4D-Var analyses when observations are withheld. This is the third part of a three part series describing the ROMS 4D-Var systems.     

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。     

Modeling of coupled tide–wave–surge process in the Yellow Sea

A coupled wave–tide–surge model has been developed in this study in order to investigate the effect of the interactions among tides, storm surges, and wind waves. The coupled model is based on the synchronous dynamic coupling of a third-generation wave model, WAM cycle 4, and the two-dimensional tide–surge model. The surface stress, which is generated by interactions between wind and wave, is calculated by using the WAM model directly based on an analytical approximation of the results using the quasi-linear theory of wave generation. The changes in bottom friction are created by the interactions between waves and currents and calculated by using simplified bottom boundary layer model. In consequence, the combined wave–current-induced bottom velocity and effective bottom drag coefficient were increased in the shallow waters during the strong storm conditions.     

基于海气耦合模型的渤海两种类型风暴潮数值模拟与对比分析

渤海一年四季都易受到由温带风暴和热带气旋所致风暴潮的影响。为了缓解风暴潮灾害对海岸地区人员生命财产的影响,十分有必要了解大型风暴潮的发生过程和机制。目前大部分研究主要局限于单一的温带风暴潮或台风风暴潮。本文利用所构建的海气耦合数值模型研究了发生于渤海的两种类型的风暴潮,对发生在渤海的2次典型强风暴潮过程进行了模拟。由WRF模型模拟得到的风场强度和最低海平面气压与实测数据吻合较好,由ROMS模型模拟得到的风暴潮期间水位变化过程与潮位站观测结果也吻合较好。对两种类型风暴潮期间的风场结钩、海面风应力、海洋表面平均流场以及水位分布进行了分析对比,并将耦合模型结果与非耦合模型结果进行了对比。研究表明,渤海两种类型风暴潮期间的风场结钩、海面风应力、海洋表面平均流场以及水位分布等均存在巨大差异。渤海风暴潮的强度主要由海洋表面的驱动力所决定,但同时也受海岸地形地貌的影响。     

风暴潮三维数值计算模式的研究及在渤海湾的应用

以实验室二维温带风暴潮数值模型为基础,综合考虑海洋潮波动力与风应力联合作用,建立温带风暴潮三维数值计算模型.模型从推导三维风暴潮基本控制方程出发,并应用交替方向隐格式(ADI)方法对方程进行离散求解.对于浅水动边界,模型采取局部深槽、缩小水域的活动边界处理方法.利用拟三维数值计算方法,并提出了非平面水深等分模式和平面等水深分布模式,应用这两种计算模式分别对渤海湾2009年5月8～10日发生的风暴潮过程进行了数值模拟.将风暴潮位计算结果和增水位计算结果与塘沽验潮站的实际观测数值进行对比验证,结果显示受风应力与潮波联合作用的风暴潮位和增水位与实测数据吻合良好;通过比较得到了平面等水深分布模式的计算成果要比非平面水深等分模式的计算成果更接近观测资料的结论,为风暴潮预报提供了理论依据.     

Numerical simulation of a low-lying barrier island's morphological response to Hurricane Katrina

Tropical cyclones that enter or form in the Gulf of Mexico generate storm surge and large waves that impact low-lying coastlines along the Gulf Coast. The Chandeleur Islands, located 161 km east of New Orleans, Louisiana, have endured numerous hurricanes that have passed nearby. Hurricane Katrina (landfall near Waveland MS, 29 Aug 2005) caused dramatic changes to the island elevation and shape. In this paper the predictability of hurricane-induced barrier island erosion and accretion is evaluated using a coupled hydrodynamic and morphodynamic model known as XBeach. Pre- and post-storm island topography was surveyed with an airborne lidar system. Numerical simulations utilized realistic surge and wave conditions determined from larger-scale hydrodynamic models. Simulations included model sensitivity tests with varying grid size and temporal resolutions. Model-predicted bathymetry/topography and post-storm survey data both showed similar patterns of island erosion, such as increased dissection by channels. However, the model under predicted the magnitude of erosion. Potential causes for under prediction include (1) errors in the initial conditions (the initial bathymetry/topography was measured three years prior to Katrina), (2) errors in the forcing conditions (a result of our omission of storms prior to Katrina and/or errors in Katrina storm conditions), and/or (3) physical processes that were omitted from the model (e.g., inclusion of sediment variations and bio-physical processes).     

Support vector regression methodology for storm surge predictions

To avoid property loss and reduce risk caused by typhoon surges, accurate prediction of surge deviation is an important task. Many conventional numerical methods and experimental methods for typhoon surge forecasting have been investigated, but it is still a complex ocean engineering problem. In this paper, support vector regression (SVR), an emerging artificial intelligence tool in forecasting storm surges is applied. The original data of Longdong station at Taiwan ‘invaded directly by the Aere typhoon’ are considered to verify the present model. Comparisons with the numerical methods and neural network indicate that storm surges and surge deviations can be efficiently predicted using SVR.     

台风风暴潮异模式集合数值预报技术研究及应用

台风风暴潮数值预报的准确性在很大程度上取决于台风路径预报和强度预报的精度以及风暴潮预报模型的计算精度。目前,国际上24/48 h台风路径预报平均误差分别约为120/210 km左右[1],对于走向异常的台风误差更大;更有,根据单一的台风路径和单族的风暴潮数值预报模式并不能保证获得可靠的风暴潮预报结果。考虑多重网格法原理具有在疏密不同的网格层上进行迭代以达到平滑不同频率的误差分量,使得计算快速收敛,精度提高的特性。在前期研究基础上基于业务化高分辨率(结构网格/有限差分算法)和精细化(非结构网格/有限元算法)台风风暴潮集合数值预报模型构建多模型台风风暴潮集合数值预报系统。采用"非同族"模型进行集合预报很大程度上降低了误差相似遗传的可能性。应用该方法对典型台风风暴潮过程进行了试应用,试报结果表明:该方法对风暴潮增、减水预报效果高于单一集合预报,具有一定的应用前景。     

0216号台风风暴潮分析

本文着重分析了0216号台风引起的福建全省沿海风暴潮几个显著特点及其成因，初步探讨灾情分布的原因，为今后该类型台风风暴潮预报积累经验及防灾减灾提供基础资料依据。     

东海风暴潮与天文潮的非线性相互作用

中国东海的风暴潮具有明显的周期性波动。凤暴潮除了决定于风应力和长波效应外,还受到天文潮与风暴潮相互作用的影响。本文利用一个二维数值模式对天文潮与风暴潮相互作用的水位进行了模拟。我们选取了8114号台风加以计算。计算结果与实测资料基本相符,由此说明水位曲线中的潮周期波动主要是由于天文潮与风暴潮之间的非线性相互作用所致。数值实验还表明,如果考虑到天文潮与风暴潮的相互作用可以显著改善水位的预报精度。     

一个高分辨率的长江口台风风暴潮数值预报模式及其应用

利用河口海岸海洋模式(ECOM-Si)建立了一个适用于长江口区风暴潮的数值预报模式.该模式采用对岸线有较好拟合能力的自然正交水平坐标系统和能分辨较复杂海底地形的垂直σ坐标系统.模式考虑了长江口径流量对风暴潮的影响,部分地考虑了天文潮和风暴潮非线性相互作用对风暴增水的影响.风暴潮预报的大气强迫场用模型气压场和模型风场.利用所建立的模式对长江口区台风风暴潮进行了8个个例模拟,模拟增水与实测增水的峰值相比较,平均绝对误差不足10cm.利用本研究建立的模式,就气象因子对风暴潮位的敏感性进行了数值试验.试验结果表明,台风中心气压降低(升高)20hPa可导致约100cm的风暴潮位升高(或降低).台风最大风速半径误差对台风增水的变化影响也较显著.试验还表明,长江径流量增加1倍(减半),可以造成风暴潮的平均增加25cm(减小13cm).天文潮位相变化对风暴增水的影响数值试验表明,当台风暴潮与天文潮在不同位相相互作用,可使风暴潮位最大增加达70cm或减小90cm.     

0608号超强台风"桑美"的主要特点和风暴潮影响特征分析

0608号超强台风"桑美"正面袭击了温州,给温州造成了严重的人员伤亡和经济损失。本文利用相关气象和海洋资料分析了台风"桑美"的主要特点及其风暴潮影响特征。     

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.     

WRF模式同化系统在“碧利斯”台风暴雨数值模拟中的应用

利用美国高分辨率中尺度模式WRF(weacherResearch and Forccast)式和、WRF三维同化系统(WRF 3DVAR),以2006年"碧利斯"台风低压引发的暴雨天气过程为例,通过控制试验和同化试验的对比分析,探讨了高空和地面实况资料同化对台风低压"碧利斯"暴雨过程分析和预报的影响.初步的结果显示,同化高空和地面实况资料后对模式的初始场有明显的改进、对暴雨过程的降水落区和强度有不同程度的正反馈,更接近实况的降水.