{"title":"障碍物解析大气模型输出数据调查","authors":"Vivien Voss, K. Heinke Schlünzen, David Grawe","doi":"10.1127/metz/2024/1217","DOIUrl":null,"url":null,"abstract":"Obstacle resolving micro-scale atmospheric models (ORMs) are important to assess atmospheric processes within urban areas. However, the data generated from such models are not standardised yet and existing data standards do not fit properly to those data. Standardised model data can help to prepare FAIR data publications, which foster the process of data reuse, sharing, comparison and distribution. Therefore, an online survey was distributed among ORM users and developers in the urban meteorology and urban climate community. The survey should help to assess, which models are currently in use and to collect suggestions and requirements from the participants on how a data standard should look like. The aim of the survey was not to test the knowledge of the participants about the model they use but rather to get an overview of how they understand, use and work with ORMs. Based on 14 finished survey entries, it shows that ORMs provide and handle their data differently. The participants are aware of the need for standardisation, preferring netCDF as data format and suggesting extending existing standards to the needs of ORM data.","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Survey On Output Data From Obstacle Resolving Atmospheric Models\",\"authors\":\"Vivien Voss, K. Heinke Schlünzen, David Grawe\",\"doi\":\"10.1127/metz/2024/1217\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Obstacle resolving micro-scale atmospheric models (ORMs) are important to assess atmospheric processes within urban areas. However, the data generated from such models are not standardised yet and existing data standards do not fit properly to those data. Standardised model data can help to prepare FAIR data publications, which foster the process of data reuse, sharing, comparison and distribution. Therefore, an online survey was distributed among ORM users and developers in the urban meteorology and urban climate community. The survey should help to assess, which models are currently in use and to collect suggestions and requirements from the participants on how a data standard should look like. The aim of the survey was not to test the knowledge of the participants about the model they use but rather to get an overview of how they understand, use and work with ORMs. Based on 14 finished survey entries, it shows that ORMs provide and handle their data differently. The participants are aware of the need for standardisation, preferring netCDF as data format and suggesting extending existing standards to the needs of ORM data.\",\"PeriodicalId\":1,\"journal\":{\"name\":\"Accounts of Chemical Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":16.4000,\"publicationDate\":\"2024-07-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Accounts of Chemical Research\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1127/metz/2024/1217\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1127/metz/2024/1217","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Survey On Output Data From Obstacle Resolving Atmospheric Models
Obstacle resolving micro-scale atmospheric models (ORMs) are important to assess atmospheric processes within urban areas. However, the data generated from such models are not standardised yet and existing data standards do not fit properly to those data. Standardised model data can help to prepare FAIR data publications, which foster the process of data reuse, sharing, comparison and distribution. Therefore, an online survey was distributed among ORM users and developers in the urban meteorology and urban climate community. The survey should help to assess, which models are currently in use and to collect suggestions and requirements from the participants on how a data standard should look like. The aim of the survey was not to test the knowledge of the participants about the model they use but rather to get an overview of how they understand, use and work with ORMs. Based on 14 finished survey entries, it shows that ORMs provide and handle their data differently. The participants are aware of the need for standardisation, preferring netCDF as data format and suggesting extending existing standards to the needs of ORM data.
期刊介绍:
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.