Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 4

Thornton, Michele; Shrestha, R.; Wei, Yixiong; Thornton, P. E.; Kao, Shih‐Chieh; Wilson, Bruce E.

doi:10.3334/ornldaac/1840

Cited by 87 publications

(77 citation statements)

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“…Daily

resolution air temperature data were acquired for the southeastern United States between 2000 and 2016 from a national meteorology data product (Daymet, version 4) ( Thornton et al. 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Long-Term Exposure to Low-Level NO2 and Mortality among the Elderly Population in the Southeastern United States

Qian

Rosenberg

et al. 2021

Environ Health Perspect

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Background: Mounting evidence has shown that long-term exposure to fine particulate matter [PM in aerodynamic diameter ( )] and ozone ( ) can increase mortality. However, the health effects associated with long-term exposure to nitrogen dioxide ( ) are less clear, in particular the evidence is scarce for at low levels that are below the current international guidelines. Methods: We constructed a population-based full cohort comprising all Medicare beneficiaries (aged , ) in the southeastern United States from 2000 to 2016, and we then further defined the below-guideline cohort that included only those who were always exposed to low-level , that is, with annual means below the current World Health Organization guidelines (i.e., ). We applied previously estimated spatially and temporally resolved concentrations and assigned annual means to study participants based on their ZIP code of residence. Cox proportional hazards models were used to examine the association between long-term exposure to low-level and all-cause mortality, adjusting for potential confounders. Results: About 71.1% of the Medicare beneficiaries in the southeastern United States were always exposed to low-level over the study period. We observed an association between long-term exposure to low-level and all-cause mortality, with a 1.042 (95% CI: 1.040, 1.045) in single-pollutant models and a 1.047 (95% CI: 1.045, 1.049) in multipollutant models (adjusting for and ), per increase in annual concentrations. The penalized spline indicates a linear exposure–response relationship across the entire exposure range. Medicare enrollees who were White, female, and residing in urban areas were more vulnerable to long-term exposure. Conclusion: Using a large and representative cohort, we provide epidemiological evidence that long-term exposure to , even below the national and global ambient air quality guidelines, was approximately linearly associated with a higher risk of mortality among older adults, independent of and exposure. Improving air quality by reducing emissions, therefore, may yield significant health benefits. https://doi.org/10.1289/EHP9044

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“…Daily

resolution air temperature data were acquired for the southeastern United States between 2000 and 2016 from a national meteorology data product (Daymet, version 4) ( Thornton et al. 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Long-Term Exposure to Low-Level NO2 and Mortality among the Elderly Population in the Southeastern United States

Qian

Rosenberg

et al. 2021

Environ Health Perspect

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“…We characterized temperature and rainfall variability through the sampling year using Daymet: Daily Surface Weather Data, which provides data on a 1-km grid for North America [ 21 ]. Based on the sampling date for each month, we estimated rainfall and temperature in 1–4-week time lags.…”

Section: Methodsmentioning

confidence: 99%

Habitat Segregation Patterns of Container Breeding Mosquitos: The Role of Urban Heat Islands, Vegetation Cover, and Income Disparity in Cemeteries of New Orleans

Crespo

Rogers

2021

IJERPH

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Aedes aegypti and Aedes albopictus are important pathogen-carrying vectors that broadly exhibit similar habitat suitability, but that differ at fine spatial scales in terms of competitive advantage and tolerance to urban driven environmental parameters. This study evaluated how spatial and temporal patterns drive the assemblages of these competing species in cemeteries of New Orleans, LA, applying indicators of climatic variability, vegetation, and heat that may drive habitat selection at multiple scales. We found that Ae. aegypti was well predicted by urban heat islands (UHI) at the cemetery scale and by canopy cover directly above the cemetery vase. As predicted, UHI positively correlate to Ae. aegypti, but contrary to predictions, Ae. aegypti, was more often found under the canopy of trees in high heat cemeteries. Ae. albopictus was most often found in low heat cemeteries, but this relationship was not statistically significant, and their overall abundances in the city were lower than Ae. aegypti. Culex quinquefasciatus, another important disease vector, was also an abundant mosquito species during the sampling year, but we found that it was temporally segregated from Aedes species, showing a negative association to the climatic variables of maximum and minimum temperature, and these factors positively correlated to its more direct competitor Ae. albopictus. These findings help us understand the mechanism by which these three important vectors segregate both spatially and temporally across the city. Our study found that UHI at the cemetery scale was highly predictive of Ae. aegypti and strongly correlated to income level, with low-income cemeteries having higher UHI levels. Therefore, the effect of excessive heat, and the proliferation of the highly competent mosquito vector, Ae. aegypti, may represent an unequal disease burden for low-income neighborhoods of New Orleans that should be explored further. Our study highlights the importance of considering socioeconomic aspects as indirectly shaping spatial segregation dynamics of urban mosquito species.

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“…This dataset consists of 1 Â 1 km 2 gridded estimates of daily weather and climatology from interpolated and extrapolated ground-based observations. A truncated Gaussian weighting filter is used for establishing spatial and temporal relationships from multiple instrumented sites, including those managed by Environment and Climate Change Canada (Thornton et al, 2020).…”

Section: Description Of Atmospheric Datasetsmentioning

confidence: 99%

“…This is likely due to how the Daymet algorithm estimates SWE. Daymet's calculation of SWE, whose sole purpose is to control solar radiation, is based on a very simple temperature-driven model of snow accumulation and snowmelt (Thornton et al, 2020;Thornton et al, 2021). Notwithstanding, the realism of Daymet's representation of SWE is partially supported by the clear distinction between estimates for higher elevation areas and that within the valley, and nonexistence over the water channel south of Kitimat.…”

Section: Spatio-temporal Comparisons Of Modelled Precipitationmentioning

confidence: 99%

Intercomparison of atmospheric forcing datasets and twoPBLschemes for precipitation modelling over a coastal valley in northern British Columbia, Canada

Onwukwe

Jackson

Déry

2022

Meteorological Applications

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Environmental modelling of remote areas requires dynamical downscaling of meteorological data to obtain precipitation values that could substitute for sparse in-situ observations. This study examined numerical simulations of precipitation over the Terrace-Kitimat Valley, an industrializing corridor in the Coast Mountains of northern British Columbia, Canada. Modelling uncertainty was explored for 1 year of output from the Weather Research and Forecasting model at 1-km grid spacing for three atmospheric forcing datasets and two planetary boundary layer (PBL) schemes. The observed total precipitation ranged from 1170 to 2380 mm and was often underestimated by more than 40% when using the North American Regional Reanalysis as atmospheric forcing data or the Mellor-Yamada-Nakanishi-Niino level 3 (MYNN3) parameterization as PBL scheme. Persistent low bias from model configurations using these configurations suggested that merely selecting an alternative atmospheric forcing dataset does not ameliorate systematic error occasioned by a poorly performing PBL parameterization. Hence, the choice of the PBL scheme and the meteorological dataset is important for spatial estimation of precipitation using WRF. Model output best corresponded with annual gauge measurements when simulations with the Mellor-Yamada-Janji c (MYJ) PBL scheme were forced with ERA5. The North American Mesoscale Analyses (NAM-ANL) however demonstrated better performance for monthly variation and high-intensity precipitation than ERA5. Using both datasets therefore may be valuable for calculations related to environmental change. With either NAM-ANL or ERA5 as atmospheric forcing data and MYJ as the PBL scheme, the uncertainty in annual simulated precipitation amount ranged between 38% overestimation and 21% underestimation of observational data.

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Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 4

Cited by 87 publications

References 0 publications

Long-Term Exposure to Low-Level NO2 and Mortality among the Elderly Population in the Southeastern United States

Long-Term Exposure to Low-Level NO2 and Mortality among the Elderly Population in the Southeastern United States

Habitat Segregation Patterns of Container Breeding Mosquitos: The Role of Urban Heat Islands, Vegetation Cover, and Income Disparity in Cemeteries of New Orleans

Intercomparison of atmospheric forcing datasets and twoPBLschemes for precipitation modelling over a coastal valley in northern British Columbia, Canada

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