Articles | Volume 14, issue 12
https://doi.org/10.5194/essd-14-5637-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-5637-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
21 Dec 2022
Data description paper |
| 21 Dec 2022
| 21 Dec 2022
A global dataset of daily maximum and minimum near-surface air temperature at 1 km resolution over land (2003–2020)
Tao Zhang
Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA 50011, USA
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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- Commonly collected thermal performance data can inform species distributions in a data-limited invader N. Claunch et al. 10.1038/s41598-023-43128-4
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- Thermal comfort and retail sales: A big data analysis of extreme temperature's impact on brick-and-mortar stores J. Yoo et al. 10.1016/j.jretconser.2023.103699
- Comparing ML Methods for Downscaling Near-Surface Air Temperature over the Eastern Mediterranean A. Blizer et al. 10.3390/rs16081314
- Impact of early heat anomalies on urban tree cooling efficiency: Evidence from spring heatwave events in India H. Wei et al. 10.1016/j.jag.2023.103334
- Modelling Europe-wide fine resolution daily ambient temperature for 2003–2020 using machine learning A. Bussalleu et al. 10.1016/j.scitotenv.2024.172454
- Contrasting Trends and Drivers of Global Surface and Canopy Urban Heat Islands H. Du et al. 10.1029/2023GL104661
- A systematic review of studies involving canopy layer urban heat island: Monitoring and associated factors Y. Li et al. 10.1016/j.ecolind.2023.111424
- SOC content of global Mollisols at a 30 m spatial resolution from 1984 to 2021 generated by the novel ML-CNN prediction model X. Meng et al. 10.1016/j.rse.2023.113911
- Assessment of daytime and nighttime surface urban heat islands across local climate zones – A case study in Florianópolis, Brazil B. Rech et al. 10.1016/j.uclim.2024.101954
- Ground-air temperature tracking from a geothermal climate-change observatory in South India V. Akkiraju et al. 10.1016/j.tecto.2023.230154
- Residential segregation and outdoor urban moist heat stress disparities in the United States T. Chakraborty et al. 10.1016/j.oneear.2023.05.016
- Thermal, water, and land cover factors led to contrasting urban and rural vegetation resilience to extreme hot months Y. Wang et al. 10.1093/pnasnexus/pgae147
- Multi-city assessments of human exposure to extreme heat during heat waves in the United States J. Hu et al. 10.1016/j.rse.2023.113700
Latest update: 29 Jun 2024
Short summary
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...
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