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Creating a topoclimatic daily air temperature dataset for the conterminous United States using homogenized station data and remotely sensed land skin temperature

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JW Oyler, A Ballantyne, K Jencso, M Sweet, and S Running, Creating a landscape-scale daily air temperature dataset for the conterminous United States using homogenized station data and remotely sensed land skin temperature.: International Journal of Climatology.

Summary

Gridded topoclimatic datasets are increasingly used to drive many ecological and hydrological models and assess climate change impacts. The use of such datasets is ubiquitous, but their inherent limitations are largely unknown or overlooked particularly in regard to spatial uncertainty and climate trends. To address these limitations, we present a statistical framework for producing a 30-arcsec (∼800-m) resolution gridded dataset of daily minimum and maximum temperature and related uncertainty from 1948 to 2012 for the conterminous United States. Like other datasets, we use weather station data and elevation-based predictors of temperature, but also implement a unique spatio-temporal interpolation that incorporates remotely sensed [...]

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  • National and Regional Climate Adaptation Science Centers
  • North Central CASC

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citationTypeJournal
journalInternational Journal of Climatology
parts
typeDOI
valuehttps://doi.org/10.1002/joc.4127
typeVolume
value35
typeIssue
value9

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