改进生态建模:在耦合生态模型中整合 CNOP-P 和邻接同化技术

IF 3.1 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES
Yongzhi Liu , Minjie Xu , Xianqing Lv
{"title":"改进生态建模:在耦合生态模型中整合 CNOP-P 和邻接同化技术","authors":"Yongzhi Liu ,&nbsp;Minjie Xu ,&nbsp;Xianqing Lv","doi":"10.1016/j.ocemod.2024.102462","DOIUrl":null,"url":null,"abstract":"<div><div>Ecological modeling is an important methodology for studying the spatio-temporal evolution of marine ecosystem. Given the significant role of model parameters as a major source of uncertainty in ecological models, we propose a novel approach by combining the Conditional Nonlinear Optimal Perturbation related to Parameters (CNOP-P) method with the adjoint assimilation method to enhance predictive accuracy. CNOP-P denotes the parameter perturbation that leads to the greatest deviation of the model's development from the reference state. In comparison to other sensitivity analysis methods, this combined approach proves to be more efficient. Considering the nonlinearity of the model structure, the maximum development of the model does not consistently align with the extreme parameter values within the confidence interval. Minor parameter errors can lead to substantial model development, significantly impacting the precision of ecological models. Notably, traditional sensitivity analysis methods such as one-at-a-time (OAT) sensitivity analysis and global sensitivity analysis (GSA) methods fail to capture this characteristic. On the other hand, the GSA methods incur substantial computational costs and tends to overestimate the sensitivity of the most sensitive parameters while underestimating the sensitivity of less sensitive parameters. The combined approach of CNOP-P and adjoint assimilation enables the assimilation of satellite data and the simultaneous optimization of model parameters alongside the CNOP-P calculations. This integration substantially improves both efficiency and precision of the ecological model, thereby improving predictive skill.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"193 ","pages":"Article 102462"},"PeriodicalIF":3.1000,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improving ecological modeling: Integrating CNOP-P and adjoint assimilation in a coupled ecological model\",\"authors\":\"Yongzhi Liu ,&nbsp;Minjie Xu ,&nbsp;Xianqing Lv\",\"doi\":\"10.1016/j.ocemod.2024.102462\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Ecological modeling is an important methodology for studying the spatio-temporal evolution of marine ecosystem. Given the significant role of model parameters as a major source of uncertainty in ecological models, we propose a novel approach by combining the Conditional Nonlinear Optimal Perturbation related to Parameters (CNOP-P) method with the adjoint assimilation method to enhance predictive accuracy. CNOP-P denotes the parameter perturbation that leads to the greatest deviation of the model's development from the reference state. In comparison to other sensitivity analysis methods, this combined approach proves to be more efficient. Considering the nonlinearity of the model structure, the maximum development of the model does not consistently align with the extreme parameter values within the confidence interval. Minor parameter errors can lead to substantial model development, significantly impacting the precision of ecological models. Notably, traditional sensitivity analysis methods such as one-at-a-time (OAT) sensitivity analysis and global sensitivity analysis (GSA) methods fail to capture this characteristic. On the other hand, the GSA methods incur substantial computational costs and tends to overestimate the sensitivity of the most sensitive parameters while underestimating the sensitivity of less sensitive parameters. The combined approach of CNOP-P and adjoint assimilation enables the assimilation of satellite data and the simultaneous optimization of model parameters alongside the CNOP-P calculations. This integration substantially improves both efficiency and precision of the ecological model, thereby improving predictive skill.</div></div>\",\"PeriodicalId\":19457,\"journal\":{\"name\":\"Ocean Modelling\",\"volume\":\"193 \",\"pages\":\"Article 102462\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-11-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ocean Modelling\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1463500324001483\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"METEOROLOGY & ATMOSPHERIC SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ocean Modelling","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1463500324001483","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

生态建模是研究海洋生态系统时空演变的重要方法。鉴于模型参数是生态模型不确定性的主要来源,我们提出了一种新方法,将参数相关条件非线性最优扰动(CNOP-P)方法与邻接同化方法相结合,以提高预测精度。CNOP-P 表示导致模型发展与参考状态偏差最大的参数扰动。与其他敏感性分析方法相比,这种组合方法被证明更为有效。考虑到模型结构的非线性,模型的最大发展与置信区间内的极端参数值并不一致。微小的参数误差会导致模型的大幅发展,从而严重影响生态模型的精度。值得注意的是,传统的灵敏度分析方法,如一次性(OAT)灵敏度分析和全局灵敏度分析(GSA)方法,无法捕捉到这一特点。另一方面,全局灵敏度分析方法会产生大量计算成本,而且往往会高估最敏感参数的灵敏度,而低估较不敏感参数的灵敏度。将 CNOP-P 和邻接同化方法结合起来,可以在进行 CNOP-P 计算的同时同化卫星数据并优化模型参数。这种整合大大提高了生态模型的效率和精度,从而提高了预测能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving ecological modeling: Integrating CNOP-P and adjoint assimilation in a coupled ecological model
Ecological modeling is an important methodology for studying the spatio-temporal evolution of marine ecosystem. Given the significant role of model parameters as a major source of uncertainty in ecological models, we propose a novel approach by combining the Conditional Nonlinear Optimal Perturbation related to Parameters (CNOP-P) method with the adjoint assimilation method to enhance predictive accuracy. CNOP-P denotes the parameter perturbation that leads to the greatest deviation of the model's development from the reference state. In comparison to other sensitivity analysis methods, this combined approach proves to be more efficient. Considering the nonlinearity of the model structure, the maximum development of the model does not consistently align with the extreme parameter values within the confidence interval. Minor parameter errors can lead to substantial model development, significantly impacting the precision of ecological models. Notably, traditional sensitivity analysis methods such as one-at-a-time (OAT) sensitivity analysis and global sensitivity analysis (GSA) methods fail to capture this characteristic. On the other hand, the GSA methods incur substantial computational costs and tends to overestimate the sensitivity of the most sensitive parameters while underestimating the sensitivity of less sensitive parameters. The combined approach of CNOP-P and adjoint assimilation enables the assimilation of satellite data and the simultaneous optimization of model parameters alongside the CNOP-P calculations. This integration substantially improves both efficiency and precision of the ecological model, thereby improving predictive skill.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Ocean Modelling
Ocean Modelling 地学-海洋学
CiteScore
5.50
自引率
9.40%
发文量
86
审稿时长
19.6 weeks
期刊介绍: The main objective of Ocean Modelling is to provide rapid communication between those interested in ocean modelling, whether through direct observation, or through analytical, numerical or laboratory models, and including interactions between physical and biogeochemical or biological phenomena. Because of the intimate links between ocean and atmosphere, involvement of scientists interested in influences of either medium on the other is welcome. The journal has a wide scope and includes ocean-atmosphere interaction in various forms as well as pure ocean results. In addition to primary peer-reviewed papers, the journal provides review papers, preliminary communications, and discussions.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信