Non-stationary extreme value models to account for trends and shifts in the extreme wave climate due to climate change     

《Applied Ocean Research》2015

The extreme values of wave climate data are of great interest in a number of different ocean engineering applications, including the design and operation of ships and offshore structures, marine energy generation, aquaculture and coastal installations. Typically, the return values of certain met-ocean parameters such as significant wave height are of particular importance. There exist many methods for estimating such return values, including the initial distribution approach, the block maxima approach and the peaks-over threshold approach. In a climate change perspective, projections of such return values to a future climate are of great importance for risk management and adaptation purposes. However, many approaches to extreme value modelling assume stationary conditions and it is not straightforward how to include non-stationarity of the extremes due to for example climate change. In this paper, various non-stationary GEV-models for significant wave height are developed that account for trends and shifts in the extreme wave climate due to climate change. These models are fitted to block maxima in a particular set of wave data obtained for a historical control period and two future projections for a future period corresponding to different emission scenarios. These models are used to investigate whether there are trends in the data within each period that influence the extreme value analysis and need to be taken into account. Moreover, it will be investigated whether there are significant inter-period shifts or trends in the extreme wave climate from the historical period to the future periods. The results from this study suggest that the intra-period trends are not statistically significant and that it might be reasonable to ignore these in extreme value analyses within each period. However, when it comes to comparing the different data sets, i.e. the historical period and the future projections, statistical significant inter-period changes are detected. Hence, the accumulated effect of a climatic trend may not be negligible over longer time periods. Interestingly enough, such statistically significant shifts are not detected if stationary extreme value models are fitted to each period separately. Therefore, the non-stationary extreme value models with inter-period shifts in the parameters are proposed as an alternative for extreme value modelling in a climate change perspective, in situations where historical data and future projections are available.  相似文献   

Environmental contours using copulas     

《Applied Ocean Research》2015

A procedure is proposed for constructing environmental contours using copula theory. Copulas are functions that define the multivariate probability distribution of a random vector or a set of random variables, and, thus, also determine their dependence structure. Constructing environmental contours requires knowledge of the joint probability distribution of the environmental variables. In many practical applications, the available statistical data is used to estimate the marginal distributions and the linear correlation matrix, and then the Nataf distribution model is employed to obtain the multivariate probability distribution. It turns out that such an approach implies a particular model of dependence structure defined by a Gaussian copula, which might not always be the appropriate one. In this work, some classes of bivariate copulas are considered for modeling the dependence structure of the environmental variables. We examine measures of association, rank-based methods for estimation of copulas, goodness of fit tests for copulas, and copula selection criteria, and apply them to metocean data from hindcasts of tropical storms and extra-tropical events in the Gulf of Mexico. A formulation is proposed for expressing the variates that define the environmental contours as functions of copulas. It is then applied for computing environmental contours of significant wave height, peak spectral period and wind velocity using the estimated copula models.  相似文献   

基于ETCCDI指数2017年中国极端温度和降水特征分析   总被引：1，自引：0，他引：1  

尹红  孙颖 《气候变化研究进展》2019,15(4):363-373

利用中国1961—2017年2419站均一化逐日气候数据,计算了气候变化检测和指数联合专家组定义的26个极端气候指数,分析2017年中国极端温度和降水特征。结果表明：2017年中国区域平均的所有极端高温指数均高于1961—1990年30年平均,所有极端低温指数均低于1961—1990年30年平均。中国区域平均的多个极端温度指数达到或者接近历史极值,其中年最小日最高气温(TXn)和年最小日最低气温(TNn)均达到历史最高值,冷夜(TN10p)、冷昼(TX10p)和持续冷日日数(CSDI)达到历史最低值。年最大日最高气温(TXx)、年最大日最低气温(TNx)、暖夜(TN90p)、霜冻(FD)、冰冻(ID)、热夜(TR)、生长期长度(GSL)排在1961年以来的第2或第3位,其余极端温度指数全部排在了1961年以来前10位。2017年中国区域平均的10个极端降水指数中,有7个指数值处于1961—2017年1个标准差范围内,指示2017年的极端降水接近正常年。  相似文献   

基于学习曲线的中国未来制氢成本趋势研究     

王彦哲  欧训民  周胜 《气候变化研究进展》2022,18(3):283-293

氢能是中国能源系统低碳转型和实现2060年前碳中和目标的重要技术选择之一。根据原料来源可以将氢分为绿氢、蓝氢和灰氢,其制备成本和碳排放强度存在较大差异。文中以中国氢能生产现状为基础,建立基于学习曲线的平准化制氢成本（LCOH）模型,测算不同制氢技术从2020年到2060年的成本变化趋势。结果表明：现阶段灰氢成本最低,绿氢成本最高;到2030年绿氢成本将下降至20~25元/kg;2050年后,绿氢将成为成本最低的制氢方式（含碳排放成本),而且PEM（质子交换膜）电解水制氢的成本将低于AE（碱性）电解水制氢,光伏+PEM电解水制氢成本将下降至12元/kg。电解槽和电力成本下降是未来绿氢成本下降的主要驱动因素。敏感性分析表明,运营维护成本和关键技术学习率是影响绿氢成本下降速度的重要参数。  相似文献   

基于降水极端预报指数的福建台风极端降水预报研究     

官晓军  潘宁  黄待静  王琦  李玲 《气象学报》2021,79(3):414-427

应用1961—2017年中国气象局热带气旋最佳路径数据集、国家地面气象观测站日降水观测资料和2015年8月—2017年12月欧洲中期天气预报中心（ECMWF）集合预报系统降水极端预报指数（EFI）数据,根据百分位法定义台风影响期间福建省各站点的台风极端降水阈值,采用最小阈值法剔除台风极端降水时EFI箱线图中的异常值,保留最小值作为台风极端降水EFI阈值,建立基于EFI阈值的台风极端降水预报方法。用该方法分别对2015年8月—2017年12月和2018年登陆或影响福建台风进行台风极端降水回报和预报试验,采用TS、空报率、漏报率对回报和预报结果进行检验。结果表明:福建台风极端降水阈值由沿海向西北内陆逐渐减小,其中中北部沿海地区的阈值最大;台风日降水量与日降水EFI预报、台风极端降水时的日降水量与日降水EFI预报均存在明显的正相关,日降水EFI预报的箱线图差异指数（I_bd）也表明EFI可以较好地区分台风极端降水和非极端降水;预报试验20时（北京时）起报时效12—36、36—60、60—84和84—108 h的台风极端降水预报TS分别达到0.26、0.22、0.20和0.19,总体上略优于回报试验;台风极端降水越显著,台风极端降水预报效果越好,回报和预报都存在的不足是空报率高,主要出现在台风极端降水不明显的个例中。降水EFI预报对台风极端降水预报具有较好的指示意义,基于降水EFI阈值的台风极端降水预报产品可在业务中作为台风极端降水预报的参考。   相似文献   

CMIP6全球气候模式对中国极端降水模拟能力的评估及其与CMIP5的比较   总被引：8，自引：0，他引：8  

王予  李惠心  王会军  孙博  陈活泼 《气象学报》2021,79(3):369-386

对CMIP6全球气候模式在中国地区极端降水的模拟能力进行了综合评估。基于CN05.1观测数据集和32个CMIP6全球气候模式的降水数据,采用8个常用极端降水指数对极端降水进行了定量描述。研究结果表明,在极端降水的气候平均态方面,CMIP6多模式集合对1961—2005年中国地区区域平均的8个极端降水指数模拟的平均相对误差为29.94%,相较CMIP5降低了2.95个百分点。极端降水的气候变率方面,CMIP6多模式集合对区域平均的8个极端降水指数模拟的平均相对误差为10.10%,相较CMIP5降低5.45个百分点。此外,利用TS评分进行模式间比较,CMIP6的平均分（0.78）高于CMIP5（0.75）,且模拟能力排名前五的模式中CMIP6占4个。对比14个同源模式的TS评分可以发现,CMIP6（0.91）相对于CMIP5（0.68）的模拟能力显著提高。进一步研究发现,CMIP6相对于CMIP5对不同区域极端降水模拟能力的改进有所区别:CMIP6对干旱区平均的气候态和变率方面改进明显,而对于湿润区的改进主要表现在对极端降水空间相关模拟能力的提高。综上,在中国地区,CMIP6相较于CMIP5对极端降水的模拟能力总体上有提升。   相似文献   

Analysis of lower-boundary climate factors contributing to the summer heatwave frequency over eastern Europe using a machine-learning model          下载免费PDF全文

Ruizhi Zhang  Xiaojing Jia  Qifeng Qian 《大气和海洋科学快报》2022,15(5):100256

A machine-learning (ML) model, the light gradient boosting machine (LightGBM), was constructed to simulate the variation in the summer (June–July–August) heatwave frequency (HWF) over eastern Europe (HWF_EUR) and to analyze the contributions of various lower-boundary climate factors to the HWF_EUR variation. The examined lower-boundary climate factors were those that may contribute to the HWF_EUR variation—namely, the sea surface temperature, soil moisture, snow-cover extent, and sea-ice concentration from the simultaneous summer, preceding spring, and winter. These selected climate factors were significantly correlated to the summer HWF_EUR variation and were used to construct the ML model. Both the hindcast simulation of HWF_EUR for the period 1981–2020 and its real-time simulation for the period 2011–2020, which used the constructed ML model, were investigated. To evaluate the contributions of the climate factors, various model experiments using different combinations of the climate factors were examined and compared. The results indicated that the LightGBM model had comparatively good performance in simulating the HWF_EUR variation. The sea surface temperature made more contributions to the ML model simulation than the other climate factors. Further examination showed that the best ML simulation was that which used the climate factors in the preceding winter, suggesting that the lower-boundary conditions in the preceding winter may be critical in forecasting the summer HWF_EUR variation.摘要本文使用LightGBM机器学习模型模拟了欧洲东部夏季热浪频率的变化, 并分析了多个底边界层气候因子的贡献. 所选取的气候因子包括前期冬季, 前期春季以及同期夏季的下垫面海温, 土壤湿度, 积雪以及海冰. 分析结果说明LightGBM模型能够较好的模拟出欧洲东部夏季热浪频率的变化, 其中海温因子对模拟的贡献最大. 进一步的分析研究显示, 使用前期冬季的气候因子进行的模拟可以获得最佳模拟结果, 意味着前期冬季的下垫面气候因子可能对夏季欧洲东部热浪频率变化的预报能起到关键作用.  相似文献   

基于多源数据的机器学习在地震预测中的研究进展     

朱俊清  孙珂 《中国地震》2022,38(4):691-707

近年来,机器学习的快速发展在计算机视觉、自然语言处理和数据挖掘等领域取得前所未有的成绩。地震研究学科众多,包括地震学、大地测量学、地球化学、地球电磁学和地质学等,研究产生的多源、复杂、海量数据高度符合机器学习对于训练数据的要求,因此许多学者将机器学习方法引入到地震预测中。本文基于机器学习背景、地震预测应用流程和评价方法等方面,回顾了近年来基于机器学习方法,利用不同学科数据进行地震预测的应用概况和主要进展,并对机器学习在地震预测中的应用进行总结和讨论。  相似文献   

石英Ti/Ge-P:基于机器学习的矿床类型判别新图解SCIEI北大核心CSCD     

王瑀  邱昆峰  侯照亮  于皓丞 《岩石学报》2022,38(1):281-290

石英的微量元素记录了石英生长的物理化学条件。通过微量元素对石英原岩进行分类的研究历史已久,经典工作是在以微量元素为坐标轴的图解上绘制各类型石英的分布范围,以区分石英类型。经典图解包括Rusk(2012)提出用于区分三种矿床类型石英的Al-Ti二元图解,和Schr9n et al.(1988)提出的用于判别不同岩浆岩类型石英的Ti-Al-Ge三元图解。越来越多的研究表明,上述图解不能满足对更多石英类型进行分类的需求,同时也出现与部分已知产状类型的石英微量元素判别相矛盾的情况。随着石英原位微区测试方法的成熟,高精度石英微量元素数据逐渐丰富为系统开展机器学习提供了大数据基础,为石英微量元素研究提供了新的角度和可能性。本研究运用机器学习分类方法对石英微量元素进行精确数学分析,提出Ti/Ge-P图解为石英成因研究提出新的地球化学指标。本文同时测试了六种经典机器学习分类算法,提高Ti/Ge-P图解在石英成因分类研究上的精度。此Ti/Ge-P图解适用于多种矿床研究,包括但不局限于斑岩型矿床、矽卡岩型矿床、浅成低温热液型矿床、卡林型矿床以及造山型矿床中的石英。这项工作是大数据技术与机器学习技术在地球化学研究中的积极探索。  相似文献   

Spatial variability of soil water erosion: Comparing empirical and intelligent techniques     

《地学前缘(英文版)》2023,14(1):101456

Soil water erosion (SWE) is an important global hazard that affects food availability through soil degradation, a reduction in crop yield, and agricultural land abandonment. A map of soil erosion susceptibility is a first and vital step in land management and soil conservation. Several machine learning (ML) algorithms optimized using the Grey Wolf Optimizer (GWO) metaheuristic algorithm can be used to accurately map SWE susceptibility. These optimized algorithms include Convolutional Neural Networks (CNN and CNN-GWO), Support Vector Machine (SVM and SVM-GWO), and Group Method of Data Handling (GMDH and GMDH-GWO). Results obtained using these algorithms can be compared with the well-known Revised Universal Soil Loss Equation (RUSLE) empirical model and Extreme Gradient Boosting (XGBoost) ML tree-based models. We apply these methods together with the frequency ratio (FR) model and the Information Gain Ratio (IGR) to determine the relationship between historical SWE data and controlling geo-environmental factors at 116 sites in the Noor-Rood watershed in northern Iran. Fourteen SWE geo-environmental factors are classified in topographical, hydro-climatic, land cover, and geological groups. We next divided the SWE sites into two datasets, one for model training (70% of the samples = 81 locations) and the other for model validation (30% of the samples = 35 locations). Finally the model-generated maps were evaluated using the Area under the Receiver Operating Characteristic (AU-ROC) curve. Our results show that elevation and rainfall erosivity have the greatest influence on SWE, while soil texture and hydrology are less important. The CNN-GWO model (AU-ROC = 0.85) outperformed other models, specifically, and in order, SVR-GWO = GMDH-GWO (AUC = 0.82), CNN = GMDH (AUC = 0.81), SVR = XGBoost (AUC = 0.80), and RULSE. Based on the RUSLE model, soil loss in the Noor-Rood watershed ranges from 0 to 2644 t ha^–1yr^?1.  相似文献