Dataset Information

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 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.

SUBMITTER: Carrion D

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

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Carrión Daniel D Arfer Kodi B KB Rush Johnathan J Dorman Michael M Rowland Sebastian T ST Kioumourtzoglou Marianthi-Anna MA Kloog Itai I Just Allan C AC

Environmental research 20210612

<h4>Background</h4>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.<h4>Method</h4>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-weighte ...[more]

PMID: 34129866

Similar Datasets

Project description:Crop phenology is very important in regular crop monitoring. Generally, phenology is monitored through field observation surveys or satellite data. The relationships between ground observations and remotely sensed derived phenological data can enable near-real-time monitoring over large areas, which has never been attempted on hazelnuts. In this study, we extracted phenological metrics derived from MODIS Enhanced Vegetation Index (EVI) in hazelnut production regions and compared them with the spring ground phenological data (BBCH scale) from orchards located in the same area of Turkey over the period from 2019 to 2022. We observed a specific temporal dynamic between remote sensing phenometrics and ground observations. The metrics Greenup, Upturning Date, and Threshold 20% metrics corresponded to the early of EVI growth and were synchronous with the female flowering of hazelnut and ending before bud break. The metrics Threshold 50% and Start of season were associated with the steepest portion of the EVI curve, i.e., canopy greening and thickening, and occurred between ovaries enlargement and leaves unfolding. The metrics Peak of Season, Stabilization Date, and Maturity corresponded to the end of spring vegetative growth. The main outcomes are that (i) female flowering occurred before 20% of vegetation development (BBCH 64P occurred about one month before Threshold 20%), (ii) phenometrics from satellite remote sensing (i.e., Upturning Date and Threshold 20%) well-reflected leaf emergence (rs = 0.30 and rs = 0.32, respectively; p < 0.05) and unfolding (rs = 0.35 and rs = 0.39, respectively; p < 0.05), and (iii) cluster appearance temporally aligned with the peak of the EVI curve (Stabilization Date and BBCH 71P differed by around 4 days). Our method is transferable to operational phenology monitoring, and future applications will consider the senescence season and the effect of environmental variability on the comprehension of vegetation dynamics.

Project description:Remotely sensed data - available at medium to high resolution across global spatial and temporal scales - are a valuable resource for ecologists. In particular, products from NASA's MODerate-resolution Imaging Spectroradiometer (MODIS), providing twice-daily global coverage, have been widely used for ecological applications. We present MODISTools, an R package designed to improve the accessing, downloading, and processing of remotely sensed MODIS data. MODISTools automates the process of data downloading and processing from any number of locations, time periods, and MODIS products. This automation reduces the risk of human error, and the researcher effort required compared to manual per-location downloads. The package will be particularly useful for ecological studies that include multiple sites, such as meta-analyses, observation networks, and globally distributed experiments. We give examples of the simple, reproducible workflow that MODISTools provides and of the checks that are carried out in the process. The end product is in a format that is amenable to statistical modeling. We analyzed the relationship between species richness across multiple higher taxa observed at 526 sites in temperate forests and vegetation indices, measures of aboveground net primary productivity. We downloaded MODIS derived vegetation index time series for each location where the species richness had been sampled, and summarized the data into three measures: maximum time-series value, temporal mean, and temporal variability. On average, species richness covaried positively with our vegetation index measures. Different higher taxa show different positive relationships with vegetation indices. Models had high R (2) values, suggesting higher taxon identity and a gradient of vegetation index together explain most of the variation in species richness in our data. MODISTools can be used on Windows, Mac, and Linux platforms, and is available from CRAN and GitHub (https://github.com/seantuck12/MODISTools).

Dataset Information

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

Background

Method

Results

Conclusion

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets