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Abstract. Near-surface air temperature (Ta) is a key variable in global climatestudies. A global gridded dataset of daily maximum and minimum Ta (Tmax and Tmin) is particularly valuable and critically needed inthe scientific and policy communities but is still not available. In this paper, we developed a global dataset of daily Tmax and Tminat 1 km resolution over land across 50∘ S–79∘ N from 2003 to 2020 through the combined use of ground-station-basedTa measurements and satellite observations (i.e., digital elevation model and land surface temperature) via a state-of-the-artstatistical method named Spatially Varying Coefficient Models with Sign Preservation (SVCM-SP). The root mean square errors in our estimates rangedfrom 1.20 to 2.44 ∘C for Tmax and 1.69 to 2.39 ∘C for Tmin. We found that the accuracies were affectedprimarily by land cover types, elevation ranges, and climate backgrounds. Our dataset correctly represents a negative relationship betweenTa and elevation and a positive relationship between Ta and land surface temperature; it captured spatial and temporalpatterns of Ta realistically. This global 1 km gridded daily Tmax and Tmin dataset is the first of its kind, and weexpect it to be of great value to global studies such as the urban heat island phenomenon, hydrological modeling, and epidemic forecasting. The data havebeen published by Iowa State University at https://doi.org/10.25380/iastate.c.6005185 (Zhang and Zhou, 2022).
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Daily station-level records of air temperature, snow depth, and ground temperature in the Northern Hemisphere
Abstract Air temperature (Ta), snow depth (Sd), and soil temperature (Tg) are crucial variables for studying the above- and below-ground thermal conditions, especially in high latitudes. However,in-situobservations are frequently sparse and inconsistent across various datasets, with a significant amount of missing data. This study has assembled a comprehensive dataset ofin-situobservations of Ta, Sd, and Tg for the Northern Hemisphere (higher than 30°N latitude), spanning 1960–2021. This dataset encompasses metadata and daily data time series for 27,768, 32,417, and 659 gages for Ta, Sd, and Tg, respectively. Using the ERA5-Land reanalysis data product, we applied deep learning methodology to reconstruct the missing data that account for 54.5%, 59.3%, and 74.3% of Ta, Sd, and Tg daily time series, respectively. The obtained high temporal resolution dataset can be used to better understand physical phenomena and relevant mechanisms, such as the dynamics of land-surface-atmosphere energy exchange, snowpack, and permafrost.
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- PAR ID:
- 10515650
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Data
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2052-4463
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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