观测范围调整法:在模型评估中考虑观测不确定性的新方法

IF 2.5 4区 环境科学与生态学 Q3 ENVIRONMENTAL SCIENCES
J P Evans and H M Imran
{"title":"观测范围调整法:在模型评估中考虑观测不确定性的新方法","authors":"J P Evans and H M Imran","doi":"10.1088/2515-7620/ad5ad8","DOIUrl":null,"url":null,"abstract":"Model evaluations are performed by comparing a modelled quantity with an observation of that quantity and any deviation from this observed quantity is considered an error. We know that all observing systems have uncertainties, and multiple observational products for the same quantity can provide equally plausible ‘truths’. Thus, model errors depend on the choice of observation used in the evaluation exercise. We propose a method that considers models to be indistinguishable from observations when they lie within the range of observations, and hence are not assigned any error. Errors are assigned when models are outside the observational range. Errors calculated in this way can be used within traditional statistics to calculate the Observation Range Adjusted (ORA) version of that statistic. The ORA statistics highlight the measurable errors of models, provide more robust model performance rankings, and identify areas of the model where further model development is likely to lead to consistent model improvements.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The observation range adjusted method: a novel approach to accounting for observation uncertainty in model evaluation\",\"authors\":\"J P Evans and H M Imran\",\"doi\":\"10.1088/2515-7620/ad5ad8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Model evaluations are performed by comparing a modelled quantity with an observation of that quantity and any deviation from this observed quantity is considered an error. We know that all observing systems have uncertainties, and multiple observational products for the same quantity can provide equally plausible ‘truths’. Thus, model errors depend on the choice of observation used in the evaluation exercise. We propose a method that considers models to be indistinguishable from observations when they lie within the range of observations, and hence are not assigned any error. Errors are assigned when models are outside the observational range. Errors calculated in this way can be used within traditional statistics to calculate the Observation Range Adjusted (ORA) version of that statistic. The ORA statistics highlight the measurable errors of models, provide more robust model performance rankings, and identify areas of the model where further model development is likely to lead to consistent model improvements.\",\"PeriodicalId\":48496,\"journal\":{\"name\":\"Environmental Research Communications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2024-07-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Environmental Research Communications\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1088/2515-7620/ad5ad8\",\"RegionNum\":4,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Research Communications","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1088/2515-7620/ad5ad8","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

模型评估是通过比较模型量和观测量来进行的,与观测量的任何偏差都被视为误差。我们知道,所有观测系统都有不确定性,对同一数量的多个观测产品可以提供同样可信的 "真相"。因此,模型误差取决于评估工作中对观测数据的选择。我们提出的方法是,当模型在观测范围内时,认为模型与观测结果没有区别,因此不分配任何误差。当模型超出观测范围时,就会产生误差。以这种方法计算出的误差可用于传统统计中,计算出该统计的观测范围调整(ORA)版本。观测范围调整统计量突出了模型的可测量误差,提供了更可靠的模型性能排名,并确定了进一步模型开发可能导致模型持续改进的模型区域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The observation range adjusted method: a novel approach to accounting for observation uncertainty in model evaluation
Model evaluations are performed by comparing a modelled quantity with an observation of that quantity and any deviation from this observed quantity is considered an error. We know that all observing systems have uncertainties, and multiple observational products for the same quantity can provide equally plausible ‘truths’. Thus, model errors depend on the choice of observation used in the evaluation exercise. We propose a method that considers models to be indistinguishable from observations when they lie within the range of observations, and hence are not assigned any error. Errors are assigned when models are outside the observational range. Errors calculated in this way can be used within traditional statistics to calculate the Observation Range Adjusted (ORA) version of that statistic. The ORA statistics highlight the measurable errors of models, provide more robust model performance rankings, and identify areas of the model where further model development is likely to lead to consistent model improvements.
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
Environmental Research Communications
Environmental Research Communications ENVIRONMENTAL SCIENCES-
CiteScore
3.50
自引率
0.00%
发文量
136
×
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