{"title":"Correction to “Remote Sensing Data Assimilation to Improve the Seasonal Snow Cover Simulations Over the Heihe River Basin, Northwest China”","authors":"","doi":"10.1002/joc.8771","DOIUrl":null,"url":null,"abstract":"<p>\n <span>Deng, G.</span>, <span>Liu, X.</span>, <span>Shen, Q.</span>, <span>Zhang, T.</span>, <span>Chen, Q.</span> and <span>Tang, Z.</span> (<span>2024</span>), <span>Remote Sensing Data Assimilation to Improve the Seasonal Snow Cover Simulations Over the Heihe River Basin, Northwest China</span>. <i>Int J Climatol</i>, <span>44</span>: <span>5621</span>–<span>5640</span>. https://doi.org/10.1002/joc.8656\n </p><p>In the original article, the utilization of the MuSA v2.0 snow data assimilation tool as a foundational framework was not properly attributed. With permission and authorization from the MuSA development team, we would like to correct this oversight by acknowledging the tool and its original publication: “<i>Alonso- González, E., K. Aalstad, M. W. Baba, J. Revuelto, J. I. López-Moreno, J. Fiddes, R. Essery, and S. Gascoin. 2022. “The Multiple Snow Data Assimilation System (MuSA v1.0).” Geoscientific Model Development 15, no. 24: 9127–9155</i>. https://doi.org/10.5194/gmd-15-9127-2022”. We sincerely apologize for the oversight and any inconvenience caused, and express our gratitude to Dr. Esteban Alonso-González and his team for their invaluable support throughout the study.</p><p>We sincerely apologize once again for this oversight and any inconvenience it may have caused.</p>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"45 6","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/joc.8771","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Climatology","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/joc.8771","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
Abstract
Deng, G., Liu, X., Shen, Q., Zhang, T., Chen, Q. and Tang, Z. (2024), Remote Sensing Data Assimilation to Improve the Seasonal Snow Cover Simulations Over the Heihe River Basin, Northwest China. Int J Climatol, 44: 5621–5640. https://doi.org/10.1002/joc.8656
In the original article, the utilization of the MuSA v2.0 snow data assimilation tool as a foundational framework was not properly attributed. With permission and authorization from the MuSA development team, we would like to correct this oversight by acknowledging the tool and its original publication: “Alonso- González, E., K. Aalstad, M. W. Baba, J. Revuelto, J. I. López-Moreno, J. Fiddes, R. Essery, and S. Gascoin. 2022. “The Multiple Snow Data Assimilation System (MuSA v1.0).” Geoscientific Model Development 15, no. 24: 9127–9155. https://doi.org/10.5194/gmd-15-9127-2022”. We sincerely apologize for the oversight and any inconvenience caused, and express our gratitude to Dr. Esteban Alonso-González and his team for their invaluable support throughout the study.
We sincerely apologize once again for this oversight and any inconvenience it may have caused.
邓国光,刘晓,沈强,张涛,陈强,唐忠(2024),黑河流域季节性积雪模拟的遥感数据同化改进。中国生物医学工程学报,44(4):559 - 564。https://doi.org/10.1002/joc.8656在最初的文章中,没有适当地归功于MuSA v2.0积雪数据同化工具作为基础框架的使用。在获得MuSA开发团队的许可和授权后,我们希望通过承认该工具及其原始出版物来纠正这一疏忽:“Alonso- González, E., K. Aalstad, M. W. Baba, J. Revuelto, J. I. López-Moreno, J. Fiddes, R. Essery和S. Gascoin. 2022。多雪资料同化系统(MuSA v1.0)。地球科学模型开发第15期24: 9127 - 9155。https://doi.org/10.5194/gmd - 15 - 9127 - 2022”。我们对疏忽和造成的任何不便深表歉意,并感谢Esteban博士Alonso-González和他的团队在整个研究过程中给予的宝贵支持。对于这一疏忽以及由此造成的任何不便,我们再次深表歉意。
期刊介绍:
The International Journal of Climatology aims to span the well established but rapidly growing field of climatology, through the publication of research papers, short communications, major reviews of progress and reviews of new books and reports in the area of climate science. The Journal’s main role is to stimulate and report research in climatology, from the expansive fields of the atmospheric, biophysical, engineering and social sciences. Coverage includes: Climate system science; Local to global scale climate observations and modelling; Seasonal to interannual climate prediction; Climatic variability and climate change; Synoptic, dynamic and urban climatology, hydroclimatology, human bioclimatology, ecoclimatology, dendroclimatology, palaeoclimatology, marine climatology and atmosphere-ocean interactions; Application of climatological knowledge to environmental assessment and management and economic production; Climate and society interactions