Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA 50011, USA
Kaiguang Zhao
School of Environment and Natural Resources, Ohio Agricultural Research and Development Center, The Ohio State University, Wooster, OH 44691, USA
Zhengyuan Zhu
Department of Statistics, Iowa State University, Ames, IA 50011, USA
Gang Chen
Laboratory for Remote Sensing and Environmental Change (LRSEC), Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
Jia Hu
Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA 50011, USA
Li Wang
Department of Statistics, George Mason University, Fairfax, VA 22030, USA
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9,992
2,504
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12,684
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Cumulative views and downloads
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Total article views: 9,279 (including HTML, PDF, and XML)
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7,323
1,805
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9,279
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PDF: 1,805
XML: 151
Total: 9,279
Supplement: 483
BibTeX: 145
EndNote: 201
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2,669
699
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20
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Total: 3,405
Supplement: 270
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Total article views: 12,684 (including HTML, PDF, and XML)
Thereof 12,268 with geography defined
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Total article views: 9,279 (including HTML, PDF, and XML)
Thereof 9,029 with geography defined
and 250 with unknown origin.
Total article views: 3,405 (including HTML, PDF, and XML)
Thereof 3,239 with geography defined
and 166 with unknown origin.
We generated a global 1 km daily maximum and minimum near-surface air temperature (Tmax and Tmin) dataset (2003–2020) using a novel statistical model. The average root mean square errors ranged from 1.20 to 2.44 °C for Tmax and 1.69 to 2.39 °C for Tmin. The gridded global air temperature dataset is of great use in a variety of studies such as the urban heat island phenomenon, hydrological modeling, and epidemic forecasting.
We generated a global 1 km daily maximum and minimum near-surface air temperature (Tmax and...
