A 1-km hourly air-temperature model for 13 northeastern U.S. states using remotely sensed and ground-based measurements.

ABSTRACT:

Background

Accurate and precise estimates of ambient air temperatures that can capture fine-scale within-day variability are necessary for studies of air temperature and health.

Method

We developed statistical models to predict temperature at each hour in each cell of a 927-m square grid across the Northeast and Mid-Atlantic United States from 2003 to 2019, across ~4000 meteorological stations from the Integrated Mesonet, using inputs such as elevation, an inverse-distance-weighted interpolation of temperature, and satellite-based vegetation and land surface temperature. We used a rigorous spatial cross-validation scheme and spatially weighted the errors to estimate how well model predictions would generalize to new cell-days. We assess the within-county association of tempe

SUBMITTER: Carrion D 

PROVIDER: S-EPMC8403657 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature