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。     

A stochastic operational forecasting system of the Black Sea: Technique and validation

In this article, we present the latest version of an ensemble forecasting system of the hydrodynamics of the Black Sea, based on the GHER model. The system includes the Weakly Constrained Ensembles algorithm to generate random, but physically balanced perturbations to initialize members of the ensemble. On top of initial conditions, the ensemble accounts also for uncertainty on the atmospheric forcing fields, and on some scalar parameters such as river flows or model diffusion coefficients. The forecasting system also includes the Ocean Assimilation Kit, a sequential data assimilation package implementing the SEEK and Ensemble Kalman filters. A novel aspect of the forecasting system is that not only our best estimate of the future ocean state is provided, but also the associated error estimated from the ensemble of models. The primary goal of this paper is to quantitatively show that the ensemble variability is a good estimation of the model error, regardless of the magnitude of the forecast errors themselves. In order for this estimation to be meaningful, the model itself should also be well validated. Therefore, we describe the model validation against general circulation patterns. Some particular aspects critical for the Black Sea circulation are validated as well: the mixed layer depth and the shelfopen sea exchanges. The model forecasts are also compared with observed sea surface temperature, and errors are compared to those of another operational model as well.     

Modelling surface drifting of buoys during a rapidly-moving weather front in the Gulf of Finland, Baltic Sea

The Gulf of Finland is an elongated estuary located in the north-eastern extremity of the Baltic Sea. This semi-enclosed sea-area is subject to heavy sea traffic, and is one of the main risk areas for oil accidents in the Baltic. The continuous development and validation of operational particle drift and oil-spill forecasting systems is thus seen to be essential for this sea-area.Here, the results of a three-day drift experiment in May 2003 are discussed. The field studies were performed using GPS-positioned surface floating buoys. The aim of this paper is to evaluate how well models can reproduce the drift of these buoys. Model simulations, both in forecast and hindcast modes, were carried out by three different 3D hydrodynamic models, the results of which are evaluated by comparing the calculated drifts with observations. These models were forced by HIRLAM (High Resolution Limited Area Model) and ECMWF (European Centre for Medium-Range Weather Forecasts) meteorological forecast fields.The simulated drift of the buoys showed a good agreement with observations even when, during the study period, a rapidly-changing wind situation was observed to affect the investigation area; in this situation the winds turned about 100 degrees in half an hour. In such a case it is a very complicated task to forecast the drifters' routes: there is a need to regularly update the meteorological forcing fields and to use these regularly-updated fields throughout the simulations. It is furthermore recommended that forecasts should be made using several circulation models and several meteorological forecasts, in order to get an overview of the accuracy of the forecasted drifts and related differences in between the forecasts.     

Wind-wave generation of ocean current: on model identification

It is shown how stochastic models based on inertial fluctuations, forced by Stokes drift and wind stress, give apparently accurate predictions of sea surface current. A parameter estimation procedure that gives subjectively reasonable results may therefore also be found. However, objective model identification turns out to be difficult and an estimation model capable of following large-scale model errors is necessary for reasonably accurate parameter estimates. Such a model is proposed and simulation results are presented and discussed. In this mode the inertial oscillation damping, is easily overestimated and the Stokes drift effect is seen to be smaller than the wind stress effect. The latter appears to be uncertain     

Statistical and hydrodynamic models for the operational forecasting of floods in the Venice Lagoon

The risk of flooding in Venice has increased strongly since the beginning of the century. To reduce the damage to the city and the negative impact on the activities in the lagoon, an accurate flood warning system is necessary. This system will also be fundamental during the construction and for the efficient operation of storm surge barriers covering the three existing inlets of the lagoon. In this context new operational statistical and hydrodynamic models have been developed. Forecast winds and pressure fields which constitute basic information for the warning system have been obtained through an ad hoc Limited Area Meteorological model. It has been demonstrated that, provided that this information is available on an operational basis, the implementation of a flood warning system for Venice using the models developed is feasible. The statistical model, which is based on a multiple regression technique, extends the forecasting range of the model presently in operation at the Centro Previsioni e Segnalazioni Maree del Comune di Venezia, from 3 hrs up to 24 hrs, and presents good accuracy (estimated mean absolute errors smaller than 10 cm) for short-term forecasts up to 9 hrs. The hydrodynamic model includes all the physical processes important for the simulation of water levels and currents in coastal and marine environments. The model set-up adopted covers the entire Adriatic Sea, with a grid spacing of 6 km. Special attention has been given to the positioning of the open boundary and to the correct reproduction of the main free oscillation of the Adriatic, which is responsible for the possible recurrence of flooding after the main storm has passed. The inclusion of this model in a flood warning system is mainly intended for long-term forecasts (> 24 hrs), and can typically be used to forecast up to 3–4 days ahead, with an estimated mean absolute error smaller than 20 cm.     

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.

     

影响黄渤海的温带气旋数值集合预报试验

为了改进温带气旋数值预报的精度,基于WRF(Weather Research and Forecasting)模式,利用GSI(Gridpoint Statistical Interpolation)-EnKF(Ensemble Kalman Filter)系统,设计了一套温带气旋集合预报方法,其具有的2种选择方案通过滤掉质量较差的集合成员从而将集合成员数目控制在10以内,达到了大幅降低集合预报计算量的目的。针对2020年7月一次影响黄海的温带气旋个例,开展了一系列决定性预报与集合预报的数值对比试验。分析结果如下:1)不采取任何择优方案的集合预报效果就已经明显优于决定性预报,而采取择优方案使得预报效果进一步得到提升;2)预报初始时刻择优(直接择优方案)的集合预报效果远不如短时积分3 h后才进行择优(积分择优方案)的预报效果; 3)积分择优方案优于直接择优方案的原因是,初始场集合体中的成员经过短时积分后其误差得以放大而使得择优更加准确。多个例的应用结果进一步表明,本文提出的积分择优方案温带气旋集合预报方法具有较好的业务预报应用前景。     

南海及周边海域风浪流耦合同化精细化数值预报与信息服务系统简介

面向社会需求,建立覆盖南海及周边海域的高分辨率风-浪-流耦合同化数值预报与信息服务系统。系统包含耦合同化数值预报模式、海洋动力环境数据库与可视化平台两部分。其中,耦合同化数值预报模式由中尺度大气数值预报模式、海浪数值预报模式和区域海洋环流数值模式,在C-Coupler耦合器中进行耦合,引入集合调整Kalman滤波同化模块,在耦合预报前进行大气、海浪和海流的同化后报模拟,为耦合预报模式提供更为精确的初始场。预报结果经海洋动力环境数据库和可视化平台处理后,通过二维和三维可视化展示,向用户提供直观的南海及周边海域海洋环境预报产品。     

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.     

On the effects of wave drift on the dispersion of floating pollutants

The movement of floating pollutants such as oil slicks on the surface of the sea is due to a number different factors, among which wave drift is certainly significant.In principle, it has been known since Stokes' time that a floating particle is subject to the movement caused by the orbital motion of water particles and that an average drift velocity results because the trajectories are not closed. In the past, however, this effect was often either disregarded or simply included with the surface wind induced current. In recent times the difference between the two effects has been conceptually clarified, so that the average wave drift in random one-dimensional seas has been the object of research and the results are now included in most handbooks and models for oil slick forecasting.Due to the chaotic nature of the wave field, however, the drift also causes floating substances to disperse, and this phenomenon is a much more neglected area of research. Recent work by Bovolin et al. [IAHR Congress, 1997] and Sobey and Barker [J. Coast. Res. 13 (1997)] has brought the subject to attention, and computational tools can now be made to quantify the effect and to verify when and how it should be taken into consideration in oil slick accident practise.The work presented in this paper is based on random simulation of the wave induced Eulerian velocity field in a directional sea, by making use of standard offshore wave directional models and on the ensemble averaging of floating particles trajectories in order to compute the spatial dispersion.     

Examination of targeting methods in a simplified setting

The effectiveness of 2 methods for targeting observations is examined using a T21 L3 QG model in a perfect model context. Target gridpoints are chosen using the pseudo‐inverse (the inverse composed of the first three singular vectors only) and the quasi‐inverse or backward integration (running the tangent equations with a negative time‐step). The effectiveness of a target is measured by setting the analysis error to zero in a region surrounding the target and noting the impact on the forecast error in the verification region. In a post‐time setting, when the targets are based on forecast errors that are known exactly, both methods provide targets that are significantly better than targets chosen at random within a broad region upstream of the verification region. When uncertainty is added to the verifying analysis such that the forecast error is known inexactly, the pseudo‐inverse targets still perform very well, while the backward integration targets are degraded. This degradation due to forecast uncertainty is especially significant when the targets are a function of height as well as horizontal position. When an ensemble‐forecast difference is used in place of the inexact forecast error, the backward integration targets may be improved considerably. However, this significant improvement depends on the characteristics of the initial‐time ensemble perturbation. Pseudo‐inverse targets based on ensemble forecast differences are comparable to pseudo‐inverse targets based on exact forecast errors. Targets based on the largest analysis error are also found to be considerably more effective than random targets. The collocation of the backward integration and pseudo‐inverse targets appears to be a good indicator of target skill.     

Bred-ensemble ocean forecast of loop current and rings

Ocean forecasting with a General Circulation Model (GCM) commonly begins from an initial analysis obtained by data assimilation. Instead of a single initial state, bred-ensemble forecast [BEnF; which is used for weather forecasting at the National Centers for Environmental Prediction] begins from an ensemble of initial states obtained by using the GCM to breed fast-growing modes into the analysis. Here we apply the technique to forecast the locations and strengths of the Loop Current and rings from July through September 2005. Model results are compared against satellite observations, surface drifter trajectories, and moored currents. It is found that BEnF gives closer agreements with observations than the conventional single forecast. The bred-vectors (perturbed minus unperturbed state-vectors) have growth rates ≈0.04–0.08 day⁻¹ and spatial (cyclone–anticyclone) scales ≈200–300 km suggestive of baroclinic instability mode in the Loop Current and rings. As in atmospheric applications, initializations with these growing vectors contribute to the more accurate ensemble mean forecast.     

集合成员个数对集合预报技巧影响的试验分析

利用全球中期预报模式T63L9,选取2004年6月4日至13日10d作为试验个例进行了集合预报试验,分析了不同集合成员个数对于预报结果的影响。结果表明,集合预报的技巧都明显高于单个控制预报。在集合成员较少时,随集合成员教的增加,集合预报的技巧提高明显,当集合成员数多于11个时,集合预报的效果提高缓慢。在中期预报时段内。集合成员数11为集合预报效果随集合成员教趋于饱和的临界值,如果继续增加成员数.预报效果提高较少,但计算量却大大的增加。本文只是单个试验个例的分析结果。为验证结论的普适性,还需要进行更多的试验。     

中国近海海面风场预报方法综述

海上大风是一种灾害性海洋天气现象,能够准确、及时的预报海上大风对沿海地区的防灾减灾具有十分重要的意义.目前近海风场的预报方法有经验预报、统计预报、数值模式预报和统计动力(数值产品的释用)预报等.本文主要针对这些预报方法进行汇总与分析,为预报员提供可靠的依据.     

黄、渤海冷空气海浪场的集合预报试验

利用欧洲集合天气预报系统51个预报风场驱动SWAN海浪模式,对黄、渤海2013年12月-2014年2月期间受冷空气影响的海浪场进行数值模拟试验,并利用浮标观测资料对海浪集合预报结果进行初步检验分析,结果显示:从逐时平均偏差结果可知,24h预报时效内集合平均与控制预报性能相近,48~72h预报时效内,集合平均明显优于控制预报,但均比实况偏小;集合分位值(75、90百分位值和极端值)明显优于集合平均,且预报时效越长,优势越明显,集合预报极端值与实况相当或略偏大;从逐24h平均偏差结果可知,集合分位值(75、90百分位值和极端值)比集合平均和控制预报更接近实况。总的分析表明:集合分位值(75、90百分位值和极端值)对受冷空气影响的海浪场具有较强的分辨能力,可以提高对海浪场的预报水平,且有较好的应用潜力。     

The Long-term Ecosystem Observatory: an integrated coastalobservatory

An integrated ocean observatory has been developed and operated in the coastal waters off the central coast of New Jersey, USA. One major goal for the Long-term Ecosystem Observatory (LEO) is to develop a real-time capability for rapid environmental assessment and physical/biological forecasting in coastal waters. To this end, observational data are collected from satellites, aircrafts, ships, fixed/relocatable moorings and autonomous underwater vehicles. The majority of the data are available in real-time allowing for adaptive sampling of episodic events and are assimilated into ocean forecast models. In this observationally rich environment, model forecast errors are dominated by uncertainties in the model physics or future boundary conditions rather than initial conditions. Therefore, ensemble forecasts with differing model parameterizations provide a unique opportunity for model refinement and validation. The system has been operated during three annual coastal predictive skill experiments from 1998 through 2000. To illustrate the capabilities of the system, case studies on coastal upwelling and small-scale biological slicks are discussed. This observatory is one part of the expanding network of ocean observatories that will form the basis of a national observation network     

黄海近岸海雾的初始场择优集合预报试验:个例研究

选取2014年4月发生的一次黄海近岸海雾个例,利用WRF(Weather Research and Forecasting)模式开展了集合预报试验研究。依据每个集合成员初始场中海平面气压、2 m温度、2 m水汽混合比与2 m相对湿度(relative humidity, RH)4个变量的均方根误差(root mean square error, RMSE)与RMSE集合平均值的相对大小,以剔除高于者而保留低于者的原则,设计了4种不同的初始场集合体择优方案,实施了一系列数值预报试验,比较了不同择优方案的集合预报效果。研究结果表明:(1)蒙特卡罗方法所生成的集合体中存在不少海雾预报效果较差的成员,这会降低集合预报效果,因此初始场择优十分必要;(2)以RH作为择优变量的择优方案(记为RH-RMSE方案),集合预报效果明显优于其他3种方案;(3)对比不择优集合预报,采用RH-RMSE方案的择优集合预报效果不仅节省了50%左右的计算时间,并且公正预兆评分(equitable threat score,ETS)改进率高达36%左右。本研究提出的RH-RMSE方案具有业务化应用前景。     

海洋数据同化与数据融合技术应用综述

简述了不同数据同化和数据融合方法在海洋环境监测与预测方面的应用、国内外相关业务单位的海洋分析和预报系统的现状,以及海洋数据同化将来的业务化应用的发展趋势。四维变分和集合卡尔曼滤波正在成为国际上海洋环境分析与预报的主要应用方向,海-气耦合数据同化以及海冰数据同化是目前数据同化方法研究的热点。     

南海及邻近海域大气-海浪-海洋耦合精细化数值预报系统研制

本文主要介绍了南海及邻近海域大气-海浪-海洋耦合精细化数值预报系统的研制概况。预报区域为99°E~135°E,15°S~45°N,包括渤海、黄海、东海和南海及其周边海域。为了给耦合预报模式提供较准确的预报初始场,在预报开始之前,分别进行了海浪模式和海洋模式的前24小时同化后报模拟。海浪模式和海洋模式都采用了集合调整Kalman滤波同化方法,海浪模式同化了Jason-2有效波高数据;海洋模式同化了SST数据、MADT数据和ARGO剖面数据。为了改进海洋温度和盐度的模拟,我们在海洋模式的垂向混合方案中引入波致混合和内波致混合的作用。预报系统的运行主要包括两个阶段,首先海浪模式和海洋模式进行了2014年1月至2015年10月底的同化后报模拟,强迫场源自欧洲气象中心的六小时的再分析数据产品。然后耦合预报系统将同化后报模拟的结果作为初始场进行了14个月的耦合预报。预报产品包括大气产品（气温、风速风向、气压等）、海浪产品（有效波高和波向等）、海流产品（温度、盐度和海流等）。一系列观测资料的检验比较表明该大气-海浪-海洋耦合精细化数值预报系统的预报结果较为可靠,可以为南海及周边海洋资源开发和安全保障提供数据和信息产品服务。     

Forecasting ocean wave energy: Tests of time-series models

This paper evaluates the ability of time-series models to predict the energy from ocean waves. Data sets from four Pacific Ocean sites are analyzed. The energy flux is found to exhibit nonlinear variability. The probability distribution has heavy tails, while the fractal dimension is non-integer. This argues for using nonlinear models. The primary technique used here is a time-varying parameter regression in logs. The time-varying regression is estimated using both a Kalman filter and a sliding window, with various window widths. The sliding window method is found to be preferable. A second approach is to combine neural networks with time-varying regressions, in a hybrid model. Both of these methods are tested on the flux itself. Time-varying regressions are also used to forecast the wave height and wave period separately, and combine the forecasts to predict the flux. Forecasting experiments are run at an hourly frequency over horizons of 1-4 h, and at a daily frequency over 1-3 days. All the models are found to improve relative to a random walk. In the hourly data sets, forecasting the components separately achieves the best results in three out of four cases. In daily data sets, the hybrid and regression models yield similar outcomes. Because of the intrinsic variability of the data, the forecast error is fairly high, comparable to the errors found in other forms of alternative energy, such as wind and solar.