Correcting nonstationary sea surface temperature bias in NCEP CFSv2 using Ensemble-based Neural Networks

IF 1.9 4区 地球科学 Q2 ENGINEERING, OCEAN Journal of Atmospheric and Oceanic Technology Pub Date : 2023-05-17 DOI:10.1175/jtech-d-22-0066.1
Ziying Yang, Jiping Liu, Chaoyuan Yang, Yongyun Hu
{"title":"Correcting nonstationary sea surface temperature bias in NCEP CFSv2 using Ensemble-based Neural Networks","authors":"Ziying Yang, Jiping Liu, Chaoyuan Yang, Yongyun Hu","doi":"10.1175/jtech-d-22-0066.1","DOIUrl":null,"url":null,"abstract":"\nSea surface temperature (SST) forecast products from the NCEP Climate Forecast System (CFSv2) that are widely used in climate research and prediction have nonstationary bias. In this study, we develop single (ANN1) and three hidden layers (ANN3) neural networks and examine their ability to correct the SST bias in the NCEP CFSv2 extended seasonal forecast starting from July in the extratropical Northern Hemisphere. Our results show that the ensemble-based ANN1 and ANN3 can reduce the uncertainty associated with parameters assigned initially and dependence on random sampling. Overall, ANN1 reduces RMSE of the CFSv2 forecasted SST substantially by 0.35°C (0.34°C) for the testing (training) data and ANN3 further reduces RMSE relatively by 0.49°C (0.47°C). Both the ensemble-based ANN1 and ANN3 can significantly reduce the spatial and temporal varying bias of the CFSv2 forecasted SST in the Pacific and Atlantic Oceans, and ANN3 shows better agreement with\nthe observation than that of ANN1 in some subregions.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Atmospheric and Oceanic Technology","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1175/jtech-d-22-0066.1","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, OCEAN","Score":null,"Total":0}
引用次数: 0

Abstract

Sea surface temperature (SST) forecast products from the NCEP Climate Forecast System (CFSv2) that are widely used in climate research and prediction have nonstationary bias. In this study, we develop single (ANN1) and three hidden layers (ANN3) neural networks and examine their ability to correct the SST bias in the NCEP CFSv2 extended seasonal forecast starting from July in the extratropical Northern Hemisphere. Our results show that the ensemble-based ANN1 and ANN3 can reduce the uncertainty associated with parameters assigned initially and dependence on random sampling. Overall, ANN1 reduces RMSE of the CFSv2 forecasted SST substantially by 0.35°C (0.34°C) for the testing (training) data and ANN3 further reduces RMSE relatively by 0.49°C (0.47°C). Both the ensemble-based ANN1 and ANN3 can significantly reduce the spatial and temporal varying bias of the CFSv2 forecasted SST in the Pacific and Atlantic Oceans, and ANN3 shows better agreement with the observation than that of ANN1 in some subregions.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于集成的神经网络校正NCEP CFSv2非平稳海面温度偏差
NCEP气候预报系统(CFSv2)中广泛应用于气候研究和预测的海面温度预报产品具有非平稳偏差。在这项研究中,我们开发了单层(ANN1)和三隐层(ANN3)神经网络,并检验了它们在北半球温带7月开始的NCEP CFSv2扩展季节预报中校正SST偏差的能力。我们的结果表明,基于系综的ANN1和ANN3可以降低与初始分配的参数相关的不确定性和对随机采样的依赖性。总体而言,对于测试(训练)数据,ANN1将CFSv2预测SST的均方根误差大幅降低0.35°C(0.34°C),ANN3将均方根误差相对降低0.49°C(0.47°C)。基于系综的ANN1和ANN3都可以显著降低太平洋和大西洋CFSv2预测海温的空间和时间变化偏差,在某些亚区域,ANN3与ANN1的观测结果更为一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
4.50
自引率
9.10%
发文量
135
审稿时长
3 months
期刊介绍: The Journal of Atmospheric and Oceanic Technology (JTECH) publishes research describing instrumentation and methods used in atmospheric and oceanic research, including remote sensing instruments; measurements, validation, and data analysis techniques from satellites, aircraft, balloons, and surface-based platforms; in situ instruments, measurements, and methods for data acquisition, analysis, and interpretation and assimilation in numerical models; and information systems and algorithms.
期刊最新文献
Synergistic retrievals of ice in high clouds from elastic backscatter lidar, Ku-band radar and submillimeter wave radiometer observations A Versatile Calibration Method for Rotary-Wing UAS as Wind Measurement Systems A Case of Idiopathic Intracranial Hypertension/Pseudotumor Cerebri Syndrome Cured by Myomectomy. Optimum Estimation of Coastal Currents Using Moving Vehicles Evaluation and Intercomparison of Small Uncrewed Aircraft Systems Used for Atmospheric Research
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1