基于EEMD和ARIMA的海温预测模型研究          下载免费PDF全文

张莹  谭艳春  彭发定  廖杏杰  余昱昕 《海洋学研究》2019,37(1):9-14

类型丰富、时空分辨率高的海洋探测数据,为信号分解和机器学习算法的应用提供了可能。本文针对如何建立有效的海温预测模型这一问题,使用高时空分辨率的海表温度(SST)融合产品,引入信号处理领域的集合经验模态分解(EEMD)和机器学习领域的自回归积分滑动平均模型(ARIMA)。首先利用最适于分解自然信号的EEMD方法,将海温数据分解成多个确定频率的序列;再利用ARIMA分别对各个频率的序列进行预测,最后将各个序列的预测结果进行组合。该方法在丰富数据的支撑下,比以往直接使用海温数据所建立的预测模型精度更高,为更好地进行海温预测提供了新方法。  相似文献   

Stochastic bivariate time series models of waves in the North Sea and their application in simulation-based design     

《Applied Ocean Research》2019

In this paper, we present and evaluate three long-term wave models for application in simulation-based design of ships and marine structures. Designers and researchers often rely on historical weather data as a source for ocean area characteristics based on hindcast datasets or in-situ measurements. The limited access and size of historical datasets reduces repeatability of simulations and analyses, making it difficult to assess the sampling variability of performance and loads on marine vessels and structures. Markov, VAR and VARMA wave models, producing independent long-term time series of significant wave height (H_s) and spectral peak period (T_p), is presented as possible solutions to this problem. The models are tested and compared by addressing how the models affect interpretation of design concepts and the ability to replicate statistical and physical characteristics of the wave process. Our results show that the VAR and VARMA models perform sufficiently in describing design performance, but does not capture the physical process fully. The Markov model is found to perform worst of the tested models in the applied tests, especially for measures covering several consecutive sea states.  相似文献   

Upper Ocean Four-Dimensional Variational Data Assimilation in the Arabian Sea and Bay of Bengal     

Suneet Dwivedi  Atul Srivastava  Alok Kumar Mishra 《Marine Geodesy》2018,41(3):230-257

A regional ocean circulation model with four-dimensional variational data assimilation scheme is configured to study the ocean state of the Indian Ocean region (65°E–95°E; 5°N–20°N) covering the Arabian Sea (AS) and Bay of Bengal (BoB). The state estimation setup uses 10 km horizontal resolution and 5 m vertical resolution in the upper ocean. The in-situ temperature and salinity, satellite-derived observations of sea surface height, and blended (in-situ and satellite-derived) observations of sea surface temperature alongwith their associated uncertainties are used for data assimilation with the regionally configured ocean model. The ocean state estimation is carried out for 61 days (1 June to 31 July 2013). The assimilated fields are closer to observations compared to other global state estimates. The mixed layer depth (MLD) of the region shows deepening during the period of assimilation with AS showing higher MLD compared to the BoB. An empirical forecast equation is derived for the prediction of MLD using the air–sea forcing variables as predictors. The surface and sub-surface (50 m) heat and salt budget tendencies of the region are also investigated. It is found that at the sub-surface, only the advection and diffusion temperature and salt tendencies are important.  相似文献   

Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data     

《International Journal of Applied Earth Observation and Geoinformation》2020

Most studies have the achieved rapid and accurate determination of soil organic carbon (SOC) using laboratory spectroscopy; however, it remains difficult to map the spatial distribution of SOC. To predict and map SOC at a regional scale, we obtained fourteen hyperspectral images from the Gaofen-5 (GF-5) satellite and decomposed and reconstructed the original reflectance (OR) and the first derivative reflectance (FDR) using discrete wavelet transform (DWT) at different scales. At these different scales, as inputs, we selected the 3 optimal bands with the highest weight coefficient using principal component analysis and chose the normalized difference index (NDI), ratio index (RI) and difference index (DI) with the strongest correlation with the SOC content using a contour map method. These inputs were then used to build regional-scale SOC prediction models using random forest (RF), support vector machine (SVM) and back-propagation neural network (BPNN) algorithms. The results indicated that: 1) at a low decomposition scale, DWT can effectively eliminate the noise in satellite hyperspectral data, and the FDR combined with DWT can improve the SOC prediction accuracy significantly; 2) the method of selecting inputs using principal component analysis and a contour map can eliminate the redundancy of hyperspectral data while retaining the physical meaning of the inputs. For the model with the highest prediction accuracy, the inputs were all derived from the wavelength range of SOC variations; 3) the differences in prediction accuracy among the different prediction models are small; and 4) the SOC prediction accuracy using hyperspectral satellite data is greatly improved compared with that of previous SOC prediction studies using multispectral satellite data. This study provides a highly robust and accurate method for predicting and mapping regional SOC contents.  相似文献   

长江口外潮汐精细化模型研究与精度分析     

季凯敏  路川藤 《海洋技术学报》2019,38(5)

长江口外海上测量除受风浪影响较大外,最重要的问题是潮位控制非常困难。文中简要阐述了开展长江口外潮汐精细化模型研究的方法,介绍了利用潮汐精细化模型对长江口外航路任意点进行潮汐预报的方法,并通过实测数据进行了精度分析,提出了建议。  相似文献   

基于“灰箱”模型的海洋公共治理责任结构论证分析     

吕承文 《海洋通报》2018,(3):280-286

海洋公共治理是我国政府治理现代化体系建设的重要组成内容。作为适应新时期"海洋强国"战略实施的海洋公共治理被认作一种异于传统陆地政府治理的新型地公共治理活动,存在着自身一定的责任结构体系。收集了2012-2016年的六大类海洋责任案例(主要是海洋执法案例),以时间(t)、案例数量(q)、制度(I)、人(H)来构建"灰箱"模型,通过考察问责复发率(P)分析海洋公共责任(R)的问题及原因。研究结果表明:(1)我国海洋公共治理过程中应亟须重视"海洋政府"理念,问责事件也从样本数量上得到了体现;(2)海洋公共治理的目标应是防范于未然,确保责任事件的可预期化,推进政府治理能力现代化,应当基于法治的导向来重新塑造海洋政府的责任体系;(3)我国未来海洋公共治理的责任体系建构应通过立法的方式实现责任人和责任形式的可预期化,最终实现对海洋公共治理责任法治监督的应有之义。  相似文献   

基于无人机多源遥感数据海岛植被叶面积指数协同反演研究     

吴剑  陈鹏  傅世锋  陈庆辉  潘翔  宋志晓 《海洋技术学报》2019,38(6):1-8

无人机多源遥感数据的获取、融合以及应用是当今研究的热点和难点。文中以城洲岛为例,针对海岛特殊的地理生态环境,获取无人机多源遥感数据。结合无人机多光谱遥感数据定量分析各遥感植被指数与植被叶面积指数(Leaf Area Index, LAI)的响应关系,构建单因子遥感反演模型;基于无人机激光LiDAR点云提取海岛植被冠层高度模型(Canopy Height Model,CHM),并将其作为自变量引入到多源统计回归分析中,从而构建多源遥感数据协同反演模型,对区域尺度下海岛叶面积指数(LAI)进行估算,开展验证和精度评价。结果显示,加入植被冠层高度因子的协同反演模型的判定系数R2为0.92,绝对平均误差系数为12.29%,预测精度要优于单因子反演模型(判定次数R2为0.86,绝对平均误差系数19.95%)。研究表明,加入了植被冠层高度因子的协同反演模型能在一定程度上提高乔木植被LAI的预测精度。实践证明,无人机多源遥感技术在生态学定量研究中具有巨大的潜力和广阔的应用前景。  相似文献   

FBX三维模型自动转换为OBJ三维模型方法研究     

闻平  王冲  吴小东  桂林 《测绘与空间地理信息》2016,(1):113-115

本文研究实现了一种FBX三维模型自动转换为OBJ三维模型的方法,包括几何信息和材质信息的自动转换,所述方法包括获取数据、几何信息处理、材质信息处理、OBJ格式输出和材质库文件输出等步骤。  相似文献   

四川地形谱特征及中尺度模式水平网格分辨率选取   总被引：1，自引：0，他引：1       下载免费PDF全文

黄永杰  崔晓鹏 《Acta Meteorologica Sinica》2016,74(1):114-126

四川地形复杂多样,暴雨频发,常诱发山洪、泥石流等灾害。在利用中尺度模式对复杂地形区域的暴雨进行研究时,模式水平分辨率的选取缺乏定量依据。为了揭示四川地形的复杂特征和给中尺度模式水平网格分辨率的选取提供定量依据,利用二维离散余弦变换,对四川地形高度场和暴雨分布场进行谱分解,根据暴雨分布特征分区讨论了四川盆地地形特征,同时利用地形谱方差和数值试验定量讨论了数值模式水平分辨率的选取问题,得到主要结论有:(1)二维离散余弦变换能较好地表现出研究区域各向异性的复杂特征;(2)雅安地区和四川盆地西北部的地形谱与降水谱有较好的同相关系,盆地东北部和盆地中部的地形谱与降水谱在波长较大处出现反相关系;(3)针对某个区域的地形特征,可以通过计算模式能分辨的地形方差与总地形方差的比值来确定合适的中尺度模式水平网格分辨率。  相似文献   

辽宁地区不同降水云系雨滴谱参数及其特征量研究   总被引：1，自引：0，他引：1  

房彬  郭学良  肖辉 《大气科学》2016,40(6):1154-1164

利用位于辽宁省沈阳市和辽阳市的Parsivel（Particle Size and Velocity）激光雨滴谱仪观测到的雨滴谱资料按照积雨云降水系统、积层混合云降水系统和层状云降水系统分析雨滴谱特征量和谱参数的平均特征及随时间的演变特征。结果表明:Gamma分布拟合谱参数N₀和λ按照层状云、积层混合云和积雨云的顺序减小,谱参数μ按照层状云、积层混合云和积雨云的顺序增大;直径小于1 mm的降水粒子对数浓度的贡献最大,直径大于1 mm的降水粒子对雷达反射率的贡献最大;M-P分布的谱参数N₀与雨强I具有幂函数关系,并且随着雨强I的增大而增大,谱参数λ与雨强I具有指数函数关系,随着雨强I的增大而减小。  相似文献