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Estimating daily air temperature across the Southeastern United States using high-resolution satellite data: A statistical modeling study

Shi, Liuhua ; Liu, Pengfei ; Kloog, Itai ; Lee, Mihye ; Kosheleva, Anna ; Schwartz, Joel

Environmental Research, April 2016, Vol.146, pp.51-58 [Peer Reviewed Journal]

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  • Title:
    Estimating daily air temperature across the Southeastern United States using high-resolution satellite data: A statistical modeling study
  • Author/Creator: Shi, Liuhua ; Liu, Pengfei ; Kloog, Itai ; Lee, Mihye ; Kosheleva, Anna ; Schwartz, Joel
  • Language: English
  • Subjects: Air Temperature ; Surface Temperature ; Modis ; Reanalysis ; Exposure Error ; Environmental Sciences ; Public Health
  • Is Part Of: Environmental Research, April 2016, Vol.146, pp.51-58
  • Description: Accurate estimates of spatio-temporal resolved near-surface air temperature ( ) are crucial for environmental epidemiological studies. However, values of are conventionally obtained from weather stations, which have limited spatial coverage. Satellite surface temperature ( ) measurements offer the possibility of local exposure estimates across large domains. The Southeastern United States has different climatic conditions, more small water bodies and wetlands, and greater humidity in contrast to other regions, which add to the challenge of modeling air temperature. In this study, we incorporated satellite to estimate high resolution (1 km×1 km) daily across the southeastern USA for 2000–2014. We calibrated – measurements using mixed linear models, land use, and separate slopes for each day. A high out-of-sample cross-validated of 0.952 indicated excellent model performance. When satellite were unavailable, linear regression on nearby monitors and spatio-temporal smoothing was used to estimate . The daily estimations were compared to the NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) model. A good agreement with an of 0.969 and a mean squared prediction error (RMSPE) of 1.376 °C was achieved. Our results demonstrate that can be reliably predicted using this -based prediction model, even in a large geographical area with topography and weather patterns varying considerably.
  • Identifier: ISSN: 0013-9351 ; E-ISSN: 1096-0953 ; DOI: 10.1016/j.envres.2015.12.006