东海“桑吉”轮事故溢油污染的长期预测

“桑吉”轮事故溢出的凝析油和燃料油对漂油途经海域的海洋生态会产生长期灾害性影响。对溢油污染水体的漂移轨迹和污染程度及范围的预测可为海洋环境保护及污染评估提供重要基础信息。基于国家海洋局第一海洋研究所研发的海洋环境业务化预报系统,本研究较精确预测了“桑吉”轮事故发生后至沉船位置的漂移轨迹;继而利用拉格朗日粒子追踪法预测了撞船后60天内的油粒子漂流轨迹。结果表明,沉船点附近的溢油主要向东北方向输运,大部分油粒子进入西边界强流黑潮并在黑潮带动下迅速进入黑潮延伸体海域。基于预报系统2009-2017年历史表层海流资料,对溢油影响程度和范围进行了风险概率分析,结果显示受溢油影响最大的区域为沉船点东北方向海域。     

“桑吉”号泄漏物质扩散与漂移数值模拟预测

基于ROMS(Regional Ocean Modeling System)模式,对西北太平洋海域进行了水平分辨率高达4km的水动力环境数值模拟,该分辨率可以很好地分辨我国东海陆架环流以及中尺度涡旋等过程,此外模式考虑了8个分潮,模式结果很好地再现了黄、东海陆架环流等。基于模式结果,对"桑吉"号泄漏物质可能的扩散和迁移轨迹进行了数值模拟分析。在"桑吉"号沉船位置的表、底Ekman层内,释放拉格朗日粒子和示踪物来示踪"桑吉"号泄漏物质的可能影响范围。拉格朗日粒子和示踪物模拟结果表明:在未来3个月,"桑吉"号泄漏物质对我国黄海的影响较小,其主要随着对马暖流进入日本海和随着黑潮进入日本九州以南的太平洋海域。随着冬、春的季节转换,三个月后,北风会减弱,减弱风场的试验表明,风场减弱会减少泄漏物质向黄海的输送。5月份后黄海冷水团逐渐形成,由于斜压效应,在黄海深层水中会逐渐建立起气旋式环流,从而进一步阻碍了"桑吉"号泄漏物质向黄海的输送,该气旋式环流有利于"桑吉"号泄漏物质通过对马海峡向日本海的输送,而会抑制底层泄漏物质向我国黄海西侧的输送。     

海上无动力船舶漂移轨迹模型及其在“桑吉”油轮漂移模拟中的应用

无动力或破损的船舶在海上自由漂移对海上的各项生产活动是一个巨大的危险。通过对海上漂浮的船舶进行受力分析,考虑船舶的外形特征及漂浮状态,建立了海上船舶漂移轨迹模型,对船舶海面以上与以下侧向投影面积之比(面积比)、风拖曳系数、流拖曳系数等参数均进行了敏感性分析,并针对2018年1月6日爆燃的"桑吉"油轮漂移轨迹开展了模拟应用。结果表明该模型针对不同类型海上船舶的漂移轨迹有一定合理的模拟能力,船舶面积比是准确模拟漂移轨迹的一个重要参数,同时,该模型对风拖曳系数、流拖曳系数较为敏感。针对"桑吉"油轮的模拟,科氏力的作用不可忽视,考虑科氏力作用后漂移轨迹的模拟效果得到了很好的改善。     

近岸溢油漂移扩散预测方法研究——以胶州湾溢油事件为例

近些年,由于中国经济的高速发展,原油需求逐年增长,同时海上溢油事故发生的风险也在加剧。海上溢油会严重破坏海洋环境,危害我国经济发展,加强溢油漂移扩散预报研究能够为海上溢油应急响应提供技术支撑。因此,国家海洋信息中心研发了海上突发事件应急一体化预测预警系统。该系统基于GIS平台研发,能够在二维电子海图基础上叠加相关的海洋环境动力要素信息,模拟溢油扩散和漂移态势,同时计算剩余油量,估算溢油面积以及岸线吸附程度等。2013年11月22日,山东黄岛发生输油管道爆炸事故,造成大量原油溢入胶州湾。本文利用该系统结合高分辨率大气模型WRF的三重嵌套技术和海流模型SELFE的非结构化网格加密技术,对事故溢油漂移扩散开展了预测工作。预测结果显示,油污受胶州湾内往复流的影响极大,72 h后,胶州湾内外大部分海域都将存在油污,主要集中在黄岛沿岸海域、团岛及浮山湾附近,预测扫海面积为70.32 km2。经海事部门高分辨率卫星观测结果印证,系统模拟的溢油分布较为合理。该系统的业务化应用为相关海上溢油污染应急工作提供了一定理论依据和参考。     

各向异性背景场误差协方差构建及在“凡亚比”台风的应用

利用相临过去时段预报结果中同一时刻不同时效的模式预报场差异,计算预报误差协方差,并基于集合-变分混合同化系统将其与静态背景场误差协方差结合,从而在同化系统中构建了具有各向异性和一定流依赖特征的背景场误差协方差。单点观测理想试验显示本方案改善了静态模型化背景场误差协方差的各向同性和流依赖性问题。“凡亚比”台风的一系列同化及模拟试验表明,从台风路径、强度等方面本文方案的效果都要优于三维变分法。本文方案在不需要集合预报,计算量与三维变分法相当的情况下,给同化系统引入了各向异性、一定流依赖特征的背景误差协方差,因此本方案适于在计算资源较为紧缺情况下,对时效要求较高的预报业务中应用。     

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.     

同化技术在渤海溢油应急预报系统中的应用

溢油应急预报对溢油事故现场处理具有重要指导意义。国内外已开展大量溢油数值预报技术研究,但由于各类误差的引入(尤其风和流数值预报误差的引入)以及模型本身的不完善等各种原因导致溢油数值预报无法满足日益提高的溢油预报精度需求。随着现场观测技术和监测水平的提高,如何充分利用实时观测数据提高业务化溢油应急预报精度,并满足应急预报迅速快捷的要求,成为目前业务化溢油应急预报的首要问题。国家海洋环境预报中心于2008年实现了渤海溢油业务化预报系统的建立和业务化应用,本文针对当前渤海溢油业务化应急预报中存在的现实问题,利用已有渤海海上5个石油平台从2010年1月至2011年2月的风场观测数据,初步开展最优插值方法(optimal interpolation assimilation method,OI)同化技术在国家海洋环境预报中心渤海溢油应急预报系统风场订正的应用研究。本文采用交错订正方法,确定了OI同化技术中相关尺度因子的选取,从而实现在这5个观测站地理分布情况下,OI同化技术应用中参数的最优化,之后在理想实验和实际案例的应用中,该同化方法明显提高渤海溢油预报精度。本文为如何进一步利用同化方法迅速快捷地实现溢油应急预报精度的提高提供了一定研究基础。     

基于卫星遥感海温数据的南海SST预报误差订正

尝试利用卫星遥感高分辨率海表温度资料GHRSST (Group for High Resolution Sea Surface Temperature) 与海表温度(sea surface temperature, SST)数值预报产品之间的误差, 建立一种南海SST模式预报订正方法。首先, 利用南海的Argo浮标上层海温数据对GHRSST 海温数据进行验证, 结果表明两者之间均方根误差约为0.3℃, 相关系数为0.98, GHRSST 海温数据可用于南海业务化数值预报SST的订正。预报订正后的SST与Argo浮标海温数据相比, 24h、48h和72h的均方根误差均由0.8℃左右下降到0.5℃以内。与GHRSST 海温数据相比, 南海北部海域(110°E—121°E, 13°N—23°N)订正后的24h、48h和72h的SST预报空间误差均显著减小, 在冷空气影响南海期间或中尺度涡存在的过程中, SST预报订正效果也较为显著。因此, 该方法可考虑在南海业务化SST数值预报系统中应用。     

Using a mesoscale ensemble to predict forecast error and perform targeted observation

Using NCEP short range ensemble forecast（SREF） system,demonstrated two fundamental on-going evolutions in numerical weather prediction（NWP） are through ensemble methodology.One evolution is the shift from traditional single-value deterministic forecast to flow-dependent（not statistical） probabilistic forecast to address forecast uncertainty.Another is from a one-way observation-prediction system shifting to an interactive two-way observation-prediction system to increase predictability of a weather system.In the first part,how ensemble spread from NCEP SREF predicting ensemble-mean forecast error was evaluated over a period of about a month.The result shows that the current capability of predicting forecast error by the 21-member NCEP SREF has reached to a similar or even higher level than that of current state-of-the-art NWP models in predicting precipitation,e.g.,the spatial correlation between ensemble spread and absolute forecast error has reached 0.5 or higher at 87 h（3.5 d） lead time on average for some meteorological variables.This demonstrates that the current operational ensemble system has already had preliminary capability of predicting the forecast error with usable skill,which is a remarkable achievement as of today.Given the good spread-skill relation,the probability derived from the ensemble was also statistically reliable,which is the most important feature a useful probabilistic forecast should have.The second part of this research tested an ensemble-based interactive targeting（E-BIT） method.Unlike other mathematically-calculated objective approaches,this method is subjective or human interactive based on information from an ensemble of forecasts.A numerical simulation study was performed to eight real atmospheric cases with a 10-member,bred vector-based mesoscale ensemble using the NCEP regional spectral model（RSM,a sub-component of NCEP SREF） to prove the concept of this E-BIT method.The method seems to work most effective for basic atmospheric state variables,moderately effective for convective instabilities and least effective for precipitations.Precipitation is a complex result of many factors and,therefore,a more challenging field to be improved by targeted observation.     

无主漂油源集合预测方法研究

采用基于"拉格朗日"粒子追踪方法的油粒子数值溯源模式,引入天气预报中的"集合预报"方法,考虑气象、海洋环境背景场误差因素,研究无主溢油源的集合预测方法。对某次渤海中西部溢油溯源事件的数值模拟结果表明,溢油源集合预测分析结果与溢油源实际调查情况相符,表明无主漂油源集合预测方法科学、结论可信。     

2002～2003渤海海冰数值预报

海冰数值预报是海冰业务预报的主要方法之一。国家海洋环境预报中心建立了业务化的渤海海冰数值预报系统，并进行了多年的业务化运行。随着我国卫星遥感事业和数值天气预报技术的发展，海冰预报系统也在不断发展中。本文介绍了2002～2003冬季应用HY—1卫星海冰遥感和T213数值天气预报模式产品对该海冰数值预报系统的改进，对2002～2003冬季海冰数值预报的结果进行了分析和比较。     

财政补贴对增殖型海洋牧场经营企业绩效的影响——基于碳汇友好型与非碳汇友好型的划分

财政补贴是建设海洋牧场、增殖海洋生物资源、扩增海洋碳汇的重要调控手段。文章依据财政补贴的生态和经济目标,细分碳汇友好型补贴和非碳汇友好型补贴,并以2008—2020年现有的海参类、鱼类和贝类上市增殖型海洋牧场经营企业为数据来源,分析2种补贴对企业绩效的差异化影响。研究表明：2011年前非碳汇友好型补贴的效果优于碳汇友好型补贴,而2011年后则相反。碳汇友好型补贴对增殖型海洋牧场经营企业绩效存在差异化影响：对于海参类牧场来说,影响由大到小依次为人工鱼礁补贴、增殖放流补贴、生境修复补贴;对于鱼类牧场来说,影响由大到小依次为增殖放流补贴、生境修复补贴、人工鱼礁补贴;对于贝类牧场来说,生境修复补贴和增殖放流补贴的效果不明显。研究结果不仅从时间层面验证碳汇友好型补贴对促进海洋牧场建设的可喜成效,而且对未来优化海洋牧场财政补贴政策具有借鉴意义。     

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。     

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.     

综合应用模型正演及地震瞬时属性预测薄层展布——以平湖油气田某薄层油藏为例

应用地震储层预测技术检测薄层一直是地震技术中的难题之一,薄层展布能否检测受很多因素影响。以平湖油气田某薄层油藏为例,应用模型正演及与实际地震反射特征对比的方法研究了该薄层油藏地震响应特征,应用地震瞬时属性预测了薄层平面展布。结合沉积背景及单井相分析认为,该薄层为小型河流三角洲沉积体系,沉积相带分布清楚。预测结果与实钻结果吻合良好,为油藏经济有效开发奠定了基础。     

Bohai Sea oil spill model: a numerical case study

An operational Bohai Sea oil spill serving module (BSOSSM) that can provide users with trajectory and movement information of the released oil is developed for the purpose of informing mitigation of oil spill incidents in the Bohai Sea, China. BSOSSM is one of the serving modules that had been integrated in China digital ocean prototype system, a marine information platform for managing, displaying and disseminating all the data investigated by China 908 Program. The oil spill trajectory is calculated by an oil spill model (OSM), which serves as a component in BSOSSM. The impacts of wind, current, as well as Stokes drift on oil spill trajectory are studied by sensitivity experiments conducted using OSM. Simulation results indicate that wind forcing is the most important factor in controlling the oil trajectory at the sea surface in Bohai Sea, whereas current and Stokes drift play relatively less important roles. However, because the direction of waves generally follows that of the wind, Stokes drift does lead to an increase in oil drift and spreading velocity. Case studies of the Penglai 19-3 oil spill incident (June 2011) and Xingang oil spill (April 2005) demonstrate that OSM can generally reproduce the oil spreading, and is therefore capable of supporting the emergency response of future oil spills in the Bohai Sea through BSOSSM.     

Oil dispersal modelling: reanalysis of the Rena oil spill using open-source modelling tools

ABSTRACT

Oil spill forecast modelling is typically used immediately after a spill to predict oil dispersal and promote mobilisation of more effective response operations. The aim of this work was to map oil dispersal after the grounding of the MV Rena on Astrolabe Reef and to verify the results against observations. Model predictions were broadly consistent with observed distribution of oil contamination. However, some hot spots of oil accumulation, likely due to surf-zone and rip current circulation, were not well represented. Additionally, the model was run with 81 differing wind conditions to show that the events occurring during the grounding represented the typical likely behaviour of an oil spill on Astrolabe Reef. Oil dispersal was highly dependent on prevailing wind patterns; more accurate prediction would require better observations of local wind patterns. However, comparison of predictions with observations indicated that the GNOME model was an effective low-cost approach.     

黄海绿潮多源卫星遥感业务化监测需求分析与系统设计

在分析黄海绿潮卫星遥感监测需求基础上,设计了绿潮多源卫星遥感业务化监测系统。系统设计包括:卫星数据源选择,多源卫星影像绿潮信息提取方法、多源监测结果融合方法和绿潮信息提取总体流程,系统功能模块设计,系统研发语言和平台选择,以及监测报告、数值模拟初始场等产品的设计。为其后的系统研发中的中低分辨率信息提取、多源监测结果信息融合等关键问题提供了解决思路和方法。系统研发后可为黄海绿潮防灾减灾应急决策提供及时、准确、全面的绿潮监测信息。     

A Dynamical Statistical Model for Prediction of a Tropical Cyclone

A dynamical statistical method is applied for operational forecasting of the Bay of Bengal tropical cyclone “Nargis” of April–May 2008. The method consists of three forecast components, namely (a) analysis of Genesis Potential Parameter (GPP) and maximum potential intensity, (b) track prediction, and (c) 12 hourly intensity prediction for forecasts up to 72 hours. The results of the study showed that GPP could provide necessary predictive signal at early stages of development on the further intensification of the low pressure system into a tropical cyclone. The landfall forecast position errors by different operational numerical models (NWP) showed landfall position errors ranging from 10 km to 150 km and landfall time error ranges from 6 hours early to 6 hours delay. The dynamical statistical model is capable to provide 12 hourly nearly realistic intensity forecasts up to 60 hours of forecast.     

MASNUM海浪数值模式业务化预报与检验

介绍了MASNUM(Key Laboratory of Marine Science and Numerical Modeling)海浪数值预报系统,并利用全球和西北太平洋的Jason-1卫星数据和NDBC浮标数据中的海浪波高观测,对该预报系统进行了自2007年8月1日-2007年12月31日5个月的24,48和72 h预报结果的比较检验.模式校验结果表明,有效波高预报与观测的绝均差在0.5 m左右,从夏季到冬季,预报精度不断提高,与风场冬季预报精度较高吻合.