{"title":"瑞士伯尔尼夜间气温高分辨率数据集(2007-2022 年)","authors":"Moritz Burger, Moritz Gubler, Stefan Brönnimann","doi":"10.1002/gdj3.237","DOIUrl":null,"url":null,"abstract":"<p>To prepare for a hotter future, information on intra-urban temperature distributions is crucial for cities worldwide. In recent years, different methods to compute high-resolution temperature datasets have been developed. Such datasets commonly originate from downscaling techniques, which are applied to enhance the spatial resolution of existing data. In this study, we present an approach based on a fine-scaled low-cost urban temperature measurement network and a formerly developed land use regression approach. The dataset covers mean nocturnal temperatures of 16 summers (2007–2022) of a medium-sized urban area with adapted land cover data for each year. It has a high spatial (50 m) and temporal (daily) resolution and performs well in validation (RMSEs of 0.70 and 0.69 K and mean biases of +0.41 and −0.19 K for two validation years). The dataset can be used to examine very detailed statistics in space and time, such as first heatwave per year, cumulative heat risks or inter-annual variability. Here, we evaluate the dataset with two application cases regarding urban planning and heat risk assessment, which are of high interest for both researchers and practitioners. Due to potential biases of the low-cost measurement devices during daytime, the dataset is currently limited to night-time temperatures. With minor adaptions, the presented approach is transferable to cities worldwide in order to set a basis for researchers, city administrations and private stakeholders to address their heat mitigation and adaptation strategies.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.237","citationCount":"0","resultStr":"{\"title\":\"High-resolution dataset of nocturnal air temperatures in Bern, Switzerland (2007–2022)\",\"authors\":\"Moritz Burger, Moritz Gubler, Stefan Brönnimann\",\"doi\":\"10.1002/gdj3.237\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>To prepare for a hotter future, information on intra-urban temperature distributions is crucial for cities worldwide. In recent years, different methods to compute high-resolution temperature datasets have been developed. Such datasets commonly originate from downscaling techniques, which are applied to enhance the spatial resolution of existing data. In this study, we present an approach based on a fine-scaled low-cost urban temperature measurement network and a formerly developed land use regression approach. The dataset covers mean nocturnal temperatures of 16 summers (2007–2022) of a medium-sized urban area with adapted land cover data for each year. It has a high spatial (50 m) and temporal (daily) resolution and performs well in validation (RMSEs of 0.70 and 0.69 K and mean biases of +0.41 and −0.19 K for two validation years). The dataset can be used to examine very detailed statistics in space and time, such as first heatwave per year, cumulative heat risks or inter-annual variability. Here, we evaluate the dataset with two application cases regarding urban planning and heat risk assessment, which are of high interest for both researchers and practitioners. Due to potential biases of the low-cost measurement devices during daytime, the dataset is currently limited to night-time temperatures. With minor adaptions, the presented approach is transferable to cities worldwide in order to set a basis for researchers, city administrations and private stakeholders to address their heat mitigation and adaptation strategies.</p>\",\"PeriodicalId\":54351,\"journal\":{\"name\":\"Geoscience Data Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2024-01-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.237\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Geoscience Data Journal\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/gdj3.237\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GEOSCIENCES, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geoscience Data Journal","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/gdj3.237","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
High-resolution dataset of nocturnal air temperatures in Bern, Switzerland (2007–2022)
To prepare for a hotter future, information on intra-urban temperature distributions is crucial for cities worldwide. In recent years, different methods to compute high-resolution temperature datasets have been developed. Such datasets commonly originate from downscaling techniques, which are applied to enhance the spatial resolution of existing data. In this study, we present an approach based on a fine-scaled low-cost urban temperature measurement network and a formerly developed land use regression approach. The dataset covers mean nocturnal temperatures of 16 summers (2007–2022) of a medium-sized urban area with adapted land cover data for each year. It has a high spatial (50 m) and temporal (daily) resolution and performs well in validation (RMSEs of 0.70 and 0.69 K and mean biases of +0.41 and −0.19 K for two validation years). The dataset can be used to examine very detailed statistics in space and time, such as first heatwave per year, cumulative heat risks or inter-annual variability. Here, we evaluate the dataset with two application cases regarding urban planning and heat risk assessment, which are of high interest for both researchers and practitioners. Due to potential biases of the low-cost measurement devices during daytime, the dataset is currently limited to night-time temperatures. With minor adaptions, the presented approach is transferable to cities worldwide in order to set a basis for researchers, city administrations and private stakeholders to address their heat mitigation and adaptation strategies.
Geoscience Data JournalGEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
5.90
自引率
9.40%
发文量
35
审稿时长
4 weeks
期刊介绍:
Geoscience Data Journal provides an Open Access platform where scientific data can be formally published, in a way that includes scientific peer-review. Thus the dataset creator attains full credit for their efforts, while also improving the scientific record, providing version control for the community and allowing major datasets to be fully described, cited and discovered.
An online-only journal, GDJ publishes short data papers cross-linked to – and citing – datasets that have been deposited in approved data centres and awarded DOIs. The journal will also accept articles on data services, and articles which support and inform data publishing best practices.
Data is at the heart of science and scientific endeavour. The curation of data and the science associated with it is as important as ever in our understanding of the changing earth system and thereby enabling us to make future predictions. Geoscience Data Journal is working with recognised Data Centres across the globe to develop the future strategy for data publication, the recognition of the value of data and the communication and exploitation of data to the wider science and stakeholder communities.