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A daily high-resolution (1 km) human thermal index collection over the North China Plain from 2003 to 2020
Human-perceived temperature (HPT) describes the joint effects of multiple climatic factors such as temperature and humidity. Extreme HPT events may reduce labor capacity and cause thermal discomfort and even mortality. These events are becoming more frequent and more intense under global warming, po...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10507099/ https://www.ncbi.nlm.nih.gov/pubmed/37723201 http://dx.doi.org/10.1038/s41597-023-02535-y |
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author | Li, Xiang Luo, Ming Zhao, Yongquan Zhang, Hui Ge, Erjia Huang, Ziwei Wu, Sijia Wang, Peng Wang, Xiaoyu Tang, Yu |
author_facet | Li, Xiang Luo, Ming Zhao, Yongquan Zhang, Hui Ge, Erjia Huang, Ziwei Wu, Sijia Wang, Peng Wang, Xiaoyu Tang, Yu |
author_sort | Li, Xiang |
collection | PubMed |
description | Human-perceived temperature (HPT) describes the joint effects of multiple climatic factors such as temperature and humidity. Extreme HPT events may reduce labor capacity and cause thermal discomfort and even mortality. These events are becoming more frequent and more intense under global warming, posing severe threats to human and natural systems worldwide, particularly in populated areas with intensive human activities, e.g., the North China Plain (NCP). Therefore, a fine-scale HPT dataset in both spatial and temporal dimensions is urgently needed. Here we construct a daily high-resolution (~1 km) human thermal index collection over NCP from 2003 to 2020 (HiTIC-NCP). This dataset contains 12 HPT indices and has high accuracy with averaged determination coefficient, mean absolute error, and root mean squared error of 0.987, 0.970 °C, and 1.292 °C, respectively. Moreover, it exhibits high spatiotemporal consistency with ground-level observations. The dataset provides a reference for human thermal environment and could facilitate studies such as natural hazards, regional climate change, and urban planning. |
format | Online Article Text |
id | pubmed-10507099 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-105070992023-09-20 A daily high-resolution (1 km) human thermal index collection over the North China Plain from 2003 to 2020 Li, Xiang Luo, Ming Zhao, Yongquan Zhang, Hui Ge, Erjia Huang, Ziwei Wu, Sijia Wang, Peng Wang, Xiaoyu Tang, Yu Sci Data Data Descriptor Human-perceived temperature (HPT) describes the joint effects of multiple climatic factors such as temperature and humidity. Extreme HPT events may reduce labor capacity and cause thermal discomfort and even mortality. These events are becoming more frequent and more intense under global warming, posing severe threats to human and natural systems worldwide, particularly in populated areas with intensive human activities, e.g., the North China Plain (NCP). Therefore, a fine-scale HPT dataset in both spatial and temporal dimensions is urgently needed. Here we construct a daily high-resolution (~1 km) human thermal index collection over NCP from 2003 to 2020 (HiTIC-NCP). This dataset contains 12 HPT indices and has high accuracy with averaged determination coefficient, mean absolute error, and root mean squared error of 0.987, 0.970 °C, and 1.292 °C, respectively. Moreover, it exhibits high spatiotemporal consistency with ground-level observations. The dataset provides a reference for human thermal environment and could facilitate studies such as natural hazards, regional climate change, and urban planning. Nature Publishing Group UK 2023-09-18 /pmc/articles/PMC10507099/ /pubmed/37723201 http://dx.doi.org/10.1038/s41597-023-02535-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Li, Xiang Luo, Ming Zhao, Yongquan Zhang, Hui Ge, Erjia Huang, Ziwei Wu, Sijia Wang, Peng Wang, Xiaoyu Tang, Yu A daily high-resolution (1 km) human thermal index collection over the North China Plain from 2003 to 2020 |
title | A daily high-resolution (1 km) human thermal index collection over the North China Plain from 2003 to 2020 |
title_full | A daily high-resolution (1 km) human thermal index collection over the North China Plain from 2003 to 2020 |
title_fullStr | A daily high-resolution (1 km) human thermal index collection over the North China Plain from 2003 to 2020 |
title_full_unstemmed | A daily high-resolution (1 km) human thermal index collection over the North China Plain from 2003 to 2020 |
title_short | A daily high-resolution (1 km) human thermal index collection over the North China Plain from 2003 to 2020 |
title_sort | daily high-resolution (1 km) human thermal index collection over the north china plain from 2003 to 2020 |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10507099/ https://www.ncbi.nlm.nih.gov/pubmed/37723201 http://dx.doi.org/10.1038/s41597-023-02535-y |
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