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Identifying Heat Waves in Florida: Considerations of Missing Weather Data
BACKGROUND: Using current climate models, regional-scale changes for Florida over the next 100 years are predicted to include warming over terrestrial areas and very likely increases in the number of high temperature extremes. No uniform definition of a heat wave exists. Most past research on heat w...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4664249/ https://www.ncbi.nlm.nih.gov/pubmed/26619198 http://dx.doi.org/10.1371/journal.pone.0143471 |
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author | Leary, Emily Young, Linda J. DuClos, Chris Jordan, Melissa M. |
author_facet | Leary, Emily Young, Linda J. DuClos, Chris Jordan, Melissa M. |
author_sort | Leary, Emily |
collection | PubMed |
description | BACKGROUND: Using current climate models, regional-scale changes for Florida over the next 100 years are predicted to include warming over terrestrial areas and very likely increases in the number of high temperature extremes. No uniform definition of a heat wave exists. Most past research on heat waves has focused on evaluating the aftermath of known heat waves, with minimal consideration of missing exposure information. OBJECTIVES: To identify and discuss methods of handling and imputing missing weather data and how those methods can affect identified periods of extreme heat in Florida. METHODS: In addition to ignoring missing data, temporal, spatial, and spatio-temporal models are described and utilized to impute missing historical weather data from 1973 to 2012 from 43 Florida weather monitors. Calculated thresholds are used to define periods of extreme heat across Florida. RESULTS: Modeling of missing data and imputing missing values can affect the identified periods of extreme heat, through the missing data itself or through the computed thresholds. The differences observed are related to the amount of missingness during June, July, and August, the warmest months of the warm season (April through September). CONCLUSIONS: Missing data considerations are important when defining periods of extreme heat. Spatio-temporal methods are recommended for data imputation. A heat wave definition that incorporates information from all monitors is advised. |
format | Online Article Text |
id | pubmed-4664249 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-46642492015-12-10 Identifying Heat Waves in Florida: Considerations of Missing Weather Data Leary, Emily Young, Linda J. DuClos, Chris Jordan, Melissa M. PLoS One Research Article BACKGROUND: Using current climate models, regional-scale changes for Florida over the next 100 years are predicted to include warming over terrestrial areas and very likely increases in the number of high temperature extremes. No uniform definition of a heat wave exists. Most past research on heat waves has focused on evaluating the aftermath of known heat waves, with minimal consideration of missing exposure information. OBJECTIVES: To identify and discuss methods of handling and imputing missing weather data and how those methods can affect identified periods of extreme heat in Florida. METHODS: In addition to ignoring missing data, temporal, spatial, and spatio-temporal models are described and utilized to impute missing historical weather data from 1973 to 2012 from 43 Florida weather monitors. Calculated thresholds are used to define periods of extreme heat across Florida. RESULTS: Modeling of missing data and imputing missing values can affect the identified periods of extreme heat, through the missing data itself or through the computed thresholds. The differences observed are related to the amount of missingness during June, July, and August, the warmest months of the warm season (April through September). CONCLUSIONS: Missing data considerations are important when defining periods of extreme heat. Spatio-temporal methods are recommended for data imputation. A heat wave definition that incorporates information from all monitors is advised. Public Library of Science 2015-11-30 /pmc/articles/PMC4664249/ /pubmed/26619198 http://dx.doi.org/10.1371/journal.pone.0143471 Text en © 2015 Leary et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Leary, Emily Young, Linda J. DuClos, Chris Jordan, Melissa M. Identifying Heat Waves in Florida: Considerations of Missing Weather Data |
title | Identifying Heat Waves in Florida: Considerations of Missing Weather Data |
title_full | Identifying Heat Waves in Florida: Considerations of Missing Weather Data |
title_fullStr | Identifying Heat Waves in Florida: Considerations of Missing Weather Data |
title_full_unstemmed | Identifying Heat Waves in Florida: Considerations of Missing Weather Data |
title_short | Identifying Heat Waves in Florida: Considerations of Missing Weather Data |
title_sort | identifying heat waves in florida: considerations of missing weather data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4664249/ https://www.ncbi.nlm.nih.gov/pubmed/26619198 http://dx.doi.org/10.1371/journal.pone.0143471 |
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