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。     

福建省智能网格海洋预报业务系统实现与应用

研发了福建省智能网格海洋预报业务系统,该系统覆盖西北太平洋海域,在福建沿岸海域、台湾海峡及周边海域、远海海域空间分辨率分别达到0.5 km、5 km和10 km,与传统站点预报和大面预报相比,该系统在时空上预报精细化程度更优.福建省智能网格海洋预报业务系统已成为福建省海洋预报台的主要业务系统,至今已业务化稳定运行1a.     

海南岛近岸养殖区台风浪预报技术研究

随着海南深水网箱养殖规模的不断扩大,海浪精细化预报的需求越来越紧迫。以海南岛周边海域为目标区域,基于近岸海洋模式ADCIRC(Advancedcirculationmodel)和海浪模式SWAN(Simulating WavesNearshore),建立了海南岛近岸养殖区台风浪数值预报系统。该系统采用非结构高分辨率网格,近岸分辨率达到了100m。选取2014年第9号超强台风"威马逊"(RAMMASUN)进行针对海南岛近岸养殖区的台风浪数值模拟后报。模拟结果与实测数据较为吻合。采用全球预报系统GFS(Global Forecast System)风场和气压场数据作为驱动场对2018年7月的一次热带风暴过程进行预报,48小时、24小时预报的有效波高和实测结果比较平均相对误差分别为20.75%和17.0%。总体来说,该模型的预报精度可以满足近岸养殖区台风浪预报业务的需求。     

畸形波生成条件预报方法研究进展

近年海上事故调查结果表明很多海难事件都与畸形波袭击有关.随着我国经济进步,海上经济和军事活动越来越频繁,畸形波对上述海事活动具有严重威胁并可能造成重大人员和财产损失.现有的数值或经验海浪预报方法不能有效地预报这种灾害性海浪,亟需开发一种可靠的畸形波预报方法,这是海浪研究中面临的新课题.本文将对畸形波的实验和理论研究现状...     

Wave height forecasting in Dayyer, the Persian Gulf

Forecasting of wave parameters is necessary for many marine and coastal operations. Different forecasting methodologies have been developed using the wind and wave characteristics. In this paper, artificial neural network (ANN) as a robust data learning method is used to forecast the wave height for the next 3, 6, 12 and 24 h in the Persian Gulf. To determine the effective parameters, different models with various combinations of input parameters were considered. Parameters such as wind speed, direction and wave height of the previous 3 h, were found to be the best inputs. Furthermore, using the difference between wave and wind directions showed better performance. The results also indicated that if only the wind parameters are used as model inputs the accuracy of the forecasting increases as the time horizon increases up to 6 h. This can be due to the lower influence of previous wave heights on larger lead time forecasting and the existing lag between the wind and wave growth. It was also found that in short lead times, the forecasted wave heights primarily depend on the previous wave heights, while in larger lead times there is a greater dependence on previous wind speeds.     

Neural network for wave forecasting among multi-stations

Unlike in the open sea, the use of wind information for forecasting waves may encounter more ambiguous uncertainties in the coastal or harbor area due to the influence of complicated geometric configurations. Thus this paper attempts to forecast the waves based on learning the characteristics of observed waves, rather than the use of the wind information. This is reported in this paper by the application of the artificial neural network (ANN), in which the back-propagation algorithm is employed in the learning process for obtaining the desired results. This model evaluated the interconnection weights among multi-stations based on the previous short-term data, from which a time series of waves at a station can be generated for forecasting or data supplement based on using the neighbor stations data. Field data are used for testing the applicability of the ANN model. The results show that the ANN model performs well for both wave forecasting and data supplement when using a short-term observed wave data.     

Analysis of wave transmission behind low-crested breakwaters using neural networks

This paper aims at improving the prediction of wave transmission behind low-crested breakwaters by means of a numerical model based on Artificial Neural Networks (ANNs). The data here used are those gathered within the European research project DELOS.Firstly, the motivations that lead to employ an ANN numerical model to forecast the wave transmission behind low-crested structures are discussed. Then, the ANN model is tested and its architecture is optimized with a test targeted on assessing both the accuracy and the robustness of the method. A study is devoted to investigate the ANN model capability in reproducing some physical relationships among the involved parameters. Finally, comparisons of ANN results with those from experimental formulations based on the classic regression approach demonstrate a considerable improvement in the forecast accuracy.The ANN forecasting tool is available as a user-friendly Internet applet at: http://w3.uniroma1.it/cmar/wave_transm_kt.htm.     

中尺度涡业务化检验方法探讨

结合业务化海洋预报的实际需求,探讨了一种新的中尺度涡检验方法——匹配检验方法,并以此为基础提出了中尺度涡的检验评分方案。业务化预报检验,是开展中尺度涡业务化预报必要组成部分,本文提出的技巧评分方案,能够更客观地表述模式模拟或预报的中尺度涡的准确预报、空报和漏报,更好地掌握预报的质量和表现,为中尺度涡业务化预报提供更符合业务化实际的检验方案和方法指导。     

Forecast of wave run-up on coastal structure using offshore wave forecast data

In this paper, first we introduce the wave run-up scale which describes the degree of wave run-up based on observed sea conditions near and on a coastal structure. Then, we introduce a simple method which can be used for daily forecast of wave run-up on a coastal structure. The method derives a multiple linear regression equation between wave run-up scale and offshore wind and wave parameters using long-term photographical observation of wave run-up and offshore wave forecasting model results. The derived regression equation then can be used for forecasting the run-up scale using the offshore wave forecasting model results. To test the implementation of the method, wave run-up scales were observed at four breakwaters in the East Coast of Korea for 9 consecutive months in 2008. The data for the first 6 months were used to derive multiple linear regression equations, which were then validated using the run-up scale data for the remaining 3 months and the corresponding offshore wave forecasting model results. A comparison with an engineering formula for wave run-up is also made. It is found that this method can be used for daily forecast and warning of wave run-up on a coastal structure with reasonable accuracy.     

基于深度学习的赤潮发生预报方法研究

赤潮作为海洋灾害,对海洋渔业、生态、经济,以及人类生产、生活造成了严重影响。一直以来,赤潮受到研究者的广泛关注,但由于它的形成机制比较复杂,使得赤潮预报极具挑战性。针对赤潮预报的研究问题,本文收集了厦门海域赤潮发生前后的海洋监测数据,结合皮尔逊相关系数、散布矩阵、复相关系数方法,分析多环境因子与赤潮发生多要素的关联情况,重点采用基于深度学习的LSTM与CNN融合方法,挖掘环境因子的时序依赖,发现序列数据的局部特征,对赤潮发生进行预报。在厦门一号和厦门二号数据集中,本方法在预报未来12 h内的赤潮情况时,RMSE、MAE误差分别达到0.521 8、0.504 3。通过协同对比模型进一步确定赤潮发生的预报概率,在两个数据集上的最终预报准确率分别为67.58%和63.49%。本研究为赤潮的分析预报提供了探索经验,证明了将深度学习方法应用于赤潮预报的可行性。     

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.     

Forecasting space–time variability of wave heights in the Bay of Bengal: a genetic algorithm approach

The present work employs a genetic algorithm to carry out wave height forecasting in the Bay of Bengal. The use of empirical orthogonal function analysis allows the spatial extending of the forecast to the entire basin. The chaotic nature of the process limits the horizon of usable forecasts to 48 h in advance. Statistical evaluation of the quality of forecast leads to encouraging results. A major advantage of this method is that, once the forecast equations are derived, they can be used directly without the necessity of having a numerical wave model as an intermediate step.     

Real-time wave forecasting using genetic programming

The forecasting of ocean waves on real-time or online basis is necessary while carrying out any operational activity in the ocean. In order to obtain forecasts that are station-specific a time-series-based approach like stochastic modeling or artificial neural network was attempted by some investigators in the past. This paper presents an application of a relatively new soft computing tool called genetic programming for this purpose. Genetic programming is an extension of genetic algorithm and it is suited to explore dependency between input and output data sets. The wave rider buoy measurements available at two locations in the Gulf of Mexico are analyzed. The forecasts of significant wave heights are made over lead times of 3, 6, 12 and 24 h. The sample size belonged to a period of 15 years and it included an extensive testing period of 5 years. The forecasts made by the approach of genetic programming indicated that it can be regarded as a promising tool for future applications to ocean predictions.     

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.     

一个中国边缘海的风-浪-流预报系统

海洋预报是进行海上活动的安全保障,海洋预报系统技术已经成为现代海洋气象业务的技术支撑。海洋观测、数据同化、数值模拟和高性能计算机等技术的进步极大地推动着海洋业务化预报的发展。采用大气数值模式（WRF）、海洋数值模式（CROCO）和海浪数值模式（SWAN）的多模式高分辨率离线耦合方式,添加南京信息工程大学“海洋数值模拟与观测实验室”团队自主研发的一系列海洋模式参数化方案,包括浪致混合参数化方案、亚中尺度参数化方案、海山诱导混合参数化方案以及涡旋诱导的沿等密度面和跨等密度面混合参数化方案,并通过同化技术和最新的人工智能技术与观测资料相结合,构建一种面向中国边缘海的风浪流多参数耦合预报系统,用于海上风电功率的预报和其他海洋灾害预警。实际观测资料的验证表明,该预报系统能较准确地模拟海上风场、海流、海温、波浪、潮汐等海洋气象要素。同时实现了按需实时可视化全景展示。     

A computer model for forecasting ocean wave parameters at specific sites around the the New Zealand coast

An applications model for forecasting frequency‐directional wave spectra at any appropriately specified site is described. There are two stages to the calculations. Firstly, a spectrum is calculated based on results at nearby gridpoints from an ocean wave prediction model. This is then adjusted to make the spectrum consistent with the local wind history. Verifications of the model are made at sites off Cape Egmont and Great Barrier Island, North Island, New Zealand. These give encouraging results for the shape of the frequency spectrum, with reasonable skill evident in the energetic parts of the spectrum. The significant wave heights also agree well, with the model estimates explaining two thirds of the measured variance.     

Temporal evolution of innovation and residual statistics in the ECMWF variational data assimilation systems

The temporal evolution of innovation and residual statistics of the ECMWF 3D‐ and 4D‐Var data assimilation systems have been studied. First, the observational method is applied on an hourly basis to the innovation sequences in order to partition the perceived forecast error covariance into contributions from observation and background errors. The 4D‐Var background turns out to be ignificantly more accurate than the background in the 3D‐Var. The estimated forecast error variance associated with the 4D‐Var background trajectory increases over the assimilation window. There is also a marked broadening of the horizontal error covariance length scale over the assimilation window. Second, the standard deviation of the residuals, i.e., the fit of observations to the analysis is studied on an hourly basis over the assimilation window. This fit should, in theory, reveal the effect of model error in a strong constraint variational problem. A weakly convex curve is found for this fit implying that the perfect model assumption of 4D‐Var may be violated with as short an assimilation window as six hours. For improving the optimality of variational data assimilation systems, a sequence of retunes are needed, until the specified and diagnosed error covariances agree.     

海洋观测数据在海洋预报和海洋防灾减灾中的适用——以温州市和台州市为例

为完善海洋观测体系,提高海洋观测数据在海洋预报和海洋防灾减灾中的适用性,文章以海洋经济较发达和遭受海洋灾害较多的温州市和台州市为例,选取潮位、波浪和水温3个重要海洋观测要素,分析海洋观测数据在海洋预报和海洋防灾减灾中的适用,并提出对策建议。研究结果表明:由于观测时间较短、地理位置特殊和数据代表性不足,海洋观测站的潮位数据未能在台风风暴潮的预报和防灾减灾中有效发挥作用;由于波浪观测仪器布设位置的地形阻挡和观测站少,波浪数据的预报准确性和实际应用不足;个别观测站的水温数据不适用于大面海洋环境和赤潮的预报,且缺少对低温灾害的观测。针对海洋观测数据的实际应用与相关业务脱节的问题,未来应提高观测数据质量、紧密结合当地海洋预报和海洋防灾减灾工作需求、开展重点目标保障预报工作以及加强海洋观测宣传教育。     

一种基于小波分解和模糊推理神经网络的潮位预测方法

潮位预测严重影响沿海区域,尤其是近海浅水沿岸地区居民的生产生活和涉海活动。谐波分析是长周期潮位预测的传统方法,但无法预测非周期性气象过程发生时的水位变化。与数据处理方法相结合,人工智能的方法通过拟合输入与输出数据的历史数值关系,能够有效预测高度非线性和非平稳的流模式,因而在时间序列数据预测领域得到了广泛的应用。本文结合自适应模糊推理系统（Adaptive Neuro-Fuzzy Inference System, ANFIS）和小波分解方法,利用水位异常和风切变分量作为输入数据,实现了一种综合的多时效潮位预测方法。文中测试了多种输入变量组合和小波-ANFIS（WANFIS）模型,并与人工神经网络（Artificial Neural Network, ANN）、小波-ANN（WANN）和ANFIS模型进行了预测结果对比。通过不同指数的误差分析来看,相比ANN模型,ANFIS模型能够更准确的预测潮位变化,小波分解对ANFIS预测精度有一定的提高,且模型中水位异常和风切变分量数据的加入比单一的潮位数据输入能取得更好的预测结果。