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Identifying Heat Waves in Florida: Considerations of Missing Weather Data.

Leary E, Young LJ, DuClos C, Jordan MM - PLoS ONE (2015)

Bottom Line: 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.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).A heat wave definition that incorporates information from all monitors is advised.

View Article: PubMed Central - PubMed

Affiliation: School of Natural Resources and Environment, University of Florida, PO Box 116455, Gainesville, FL, 32611, United States of America.

ABSTRACT

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.

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Related in: MedlinePlus

National Weather Service regions and locations of FCC monitors within Florida.
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pone.0143471.g001: National Weather Service regions and locations of FCC monitors within Florida.

Mentions: Although meteorological thresholds for weather could be local or region-specific [3, 4, 8], the use of a local or monitor-specific definition of heat wave would exclude many rural and agricultural areas, important target populations for Florida’s public health services. Regional heat waves were considered using the seven National Weather Service (NWS) regions in Florida (Fig 1). In this analysis, the Keys region (KEY) was combined with the Miami region (MFL), resulting in six regions. Using NWS regions provides an inherent method for communicating extreme heat alerts through the NWS alert system, a major interest of FDOH.


Identifying Heat Waves in Florida: Considerations of Missing Weather Data.

Leary E, Young LJ, DuClos C, Jordan MM - PLoS ONE (2015)

National Weather Service regions and locations of FCC monitors within Florida.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4664249&req=5

pone.0143471.g001: National Weather Service regions and locations of FCC monitors within Florida.
Mentions: Although meteorological thresholds for weather could be local or region-specific [3, 4, 8], the use of a local or monitor-specific definition of heat wave would exclude many rural and agricultural areas, important target populations for Florida’s public health services. Regional heat waves were considered using the seven National Weather Service (NWS) regions in Florida (Fig 1). In this analysis, the Keys region (KEY) was combined with the Miami region (MFL), resulting in six regions. Using NWS regions provides an inherent method for communicating extreme heat alerts through the NWS alert system, a major interest of FDOH.

Bottom Line: 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.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).A heat wave definition that incorporates information from all monitors is advised.

View Article: PubMed Central - PubMed

Affiliation: School of Natural Resources and Environment, University of Florida, PO Box 116455, Gainesville, FL, 32611, United States of America.

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus