Preprints
https://doi.org/10.5194/essd-2022-233
https://doi.org/10.5194/essd-2022-233
 
21 Jul 2022
21 Jul 2022
Status: this preprint is currently under review for the journal ESSD.

A global dataset of daily near-surface air temperature at 1-km resolution (2003–2020)

Tao Zhang1, Yuyu Zhou1, Kaiguang Zhao2, Zhengyuan Zhu3, Gang Chen4, Jia Hu1, and Li Wang5 Tao Zhang et al.
  • 1Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA, 50011, USA
  • 2School of Environment and Natural Resources, Ohio Agricultural Research and Development Center, The Ohio State University, Wooster, OH, 44691, USA
  • 3Department of Statistics, Iowa State University, Ames, IA, 50011, USA
  • 4Laboratory for Remote Sensing and Environmental Change (LRSEC), Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC, 28223, USA
  • 5Department of Statistics, George Mason University, Fairfax, VA, 22030, USA

Abstract. Near-surface air temperature (Ta) is a key variable in global climate studies. A global gridded dataset of daily maximum and minimum Ta (Tmax and Tmin) is particularly valuable and critically needed in the scientific and policy communities, but is still not available. In this paper, we developed a global dataset of daily Tmax and Tmin dataset at 1-km resolution from 2003 to 2020 through the combined use of station-based ground Ta measurements and satellite observations (i.e., digital elevation model, and land surface temperature) via a state-of-the-art statistical method named Spatially Varying Coefficient Models with Sign Preservation (SVCM-SP). The root mean square errors of our estimates ranged from 1.20 to 2.44 for Tmax and 1.69 to 2.39 ºC for Tmin. We found that the accuracies were affected primarily by land cover types, elevation ranges, and climate backgrounds. Our dataset correctly represents the negative and positive relationships between Ta with elevation or land surface temperature; it captured spatial and temporal patterns of Ta realistically. This global 1-km gridded daily Tmax and Tmin dataset is the first of its kind and we expect it to be of great value to global studies such as urban heat island phenomenon, hydrological modeling, and epidemic forecasting. The data are available at Iowa State University’s DataShare at https://doi.org/10.25380/iastate.c.6005185 (Zhang and Zhou, 2022).

Tao Zhang et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-233', Anonymous Referee #1, 09 Aug 2022
  • RC2: 'Comment on essd-2022-233', Anonymous Referee #2, 30 Aug 2022
  • RC3: 'Comment on essd-2022-233', Anonymous Referee #3, 01 Sep 2022

Tao Zhang et al.

Data sets

A global 1 km resolution daily near-surface air temperature dataset (2003 – 2020) Tao Zhang, Yuyu Zhou https://doi.org/10.25380/iastate.c.6005185

Tao Zhang et al.

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Short summary
We generated a global 1 km daily maximum and minimum near-surface air temperature (Tmax and Tmin) dataset (2003–2020) to fill current data gap using a novel statistical model. The average root mean squared errors ranged from 1.20 to 2.44 for Tmax and 1.69 to 2.39 ºC for Tmin. The gridded global air temperature dataset is of great use in a variety of studies such as urban heat island phenomenon, hydrological modeling, and epidemic forecasting.