Aaron Hardin & Jennifer vanos, Texas Tech University, Lubbock, TX, USA

Presentation Title: Assessment of localized urban climates and associations with air pollution and synoptic weather patterns

Slides

 

Summary

The synergistic biophysical systems within urban areas can result in substantive negative societal and health consequences due to the hazards of extreme temperatures and ambient air pollution. In order for cities to manage the growing risks and vulnerability of such exposures, progress in understanding the spatial and temporal variations in urban heat island (UHI) development is critical.

This current study aimed to address the multidimensional issues of these hazards on health, science and society. The formation of the UHI was studied under various synoptic-based air mass types to determine differential spatial and temporal development and intensities of the surface UHI.

 

Key Lessons Learned

  • Heat is only one of many factors that should be considered forUHI studies; air pollution, among others, should also be included.
     
  • The largest UHI occurs when a dry weather type is present both during the day and at night.
     
  • Dry tropical (DT) synoptic weather has the hottest UHI in urban areas, with moist tropical plus (MT+) being the hottest over the entire area.

 

Policy/Practice Implications of Research

  • Once a specific area of a city has been found to have the greatest warming caused by the UHI, mitigation strategies should be developed to help address the urban risks and hazards associated with the area (e.g., cooling center locations, targeted heat warnings, emergency responders, etc.)

 

Knowledge Gaps and Needs

  • Urban climate research requires a standardized procedure for determining nighttime and daytime UHI as well as a standard methodology for calculating the UHI.  Local Climate Zones could standardize surfaces that are often delineated as ‘urban ‘or ‘rural’.
     
  • Neighborhood-scale 'UrbaNet' weather station data collected by NOAA and Earth Networks has the potential to consider other meteorological variables (e.g., wind and humidity) in further analyses.
     
  • UrbaNet data, combined with air pollution sensors, can support the creation of more localized forecasts by operational meteorologists.

 

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