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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...

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Detalles Bibliográficos
Autores principales: Leary, Emily, Young, Linda J., DuClos, Chris, Jordan, Melissa M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
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
Descripción
Sumario: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.