A 1-km hourly air-temperature model for 13 northeastern U.S. states using remotely sensed and ground-based measurements.
Air temperature
MODIS
NLDAS-2
Remote sensing
Social vulnerability
XGBoost
Journal
Environmental research
ISSN: 1096-0953
Titre abrégé: Environ Res
Pays: Netherlands
ID NLM: 0147621
Informations de publication
Date de publication:
09 2021
09 2021
Historique:
received:
15
12
2020
revised:
01
06
2021
accepted:
01
06
2021
pubmed:
16
6
2021
medline:
7
9
2021
entrez:
15
6
2021
Statut:
ppublish
Résumé
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. 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 temperature and social vulnerability in a heat wave as an example application. We found that a model based on the XGBoost machine-learning algorithm was fast and accurate, obtaining weighted root mean square errors (RMSEs) around 1.6 K, compared to standard deviations around 11.0 K. We found similar accuracy when validating our model on an external dataset from Weather Underground. Assessing predictions from the North American Land Data Assimilation System-2 (NLDAS-2), another hourly model, in the same way, we found it was much less accurate, with RMSEs around 2.5 K. This is likely due to the NLDAS-2 model's coarser spatial resolution, and the dynamic variability of temperature within its grid cells. Finally, we demonstrated the health relevance of our model by showing that our temperature estimates were associated with social vulnerability across the region during a heat wave, whereas the NLDAS-2 showed a much weaker association. Our high spatiotemporal resolution air temperature model provides a strong contribution for future health studies in this region.
Sections du résumé
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 temperature and social vulnerability in a heat wave as an example application.
RESULTS
We found that a model based on the XGBoost machine-learning algorithm was fast and accurate, obtaining weighted root mean square errors (RMSEs) around 1.6 K, compared to standard deviations around 11.0 K. We found similar accuracy when validating our model on an external dataset from Weather Underground. Assessing predictions from the North American Land Data Assimilation System-2 (NLDAS-2), another hourly model, in the same way, we found it was much less accurate, with RMSEs around 2.5 K. This is likely due to the NLDAS-2 model's coarser spatial resolution, and the dynamic variability of temperature within its grid cells. Finally, we demonstrated the health relevance of our model by showing that our temperature estimates were associated with social vulnerability across the region during a heat wave, whereas the NLDAS-2 showed a much weaker association.
CONCLUSION
Our high spatiotemporal resolution air temperature model provides a strong contribution for future health studies in this region.
Identifiants
pubmed: 34129866
pii: S0013-9351(21)00771-4
doi: 10.1016/j.envres.2021.111477
pmc: PMC8403657
mid: NIHMS1716185
pii:
doi:
Substances chimiques
Air Pollutants
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
111477Subventions
Organisme : NIEHS NIH HHS
ID : L60 ES032218
Pays : United States
Organisme : NICHD NIH HHS
ID : T32 HD049311
Pays : United States
Organisme : NIEHS NIH HHS
ID : R01 ES028805
Pays : United States
Organisme : NIEHS NIH HHS
ID : R00 ES023450
Pays : United States
Organisme : NIEHS NIH HHS
ID : P30 ES009089
Pays : United States
Organisme : NIEHS NIH HHS
ID : P30 ES023515
Pays : United States
Organisme : NIEHS NIH HHS
ID : R01 ES030616
Pays : United States
Informations de copyright
Copyright © 2021 Elsevier Inc. All rights reserved.
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