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Using UrbaNet Data to Quantify the Nocturnal Heat Islands of US Cities

Using UrbaNet Data to Quantify the Nocturnal Heat Islands of US Cities Mark Hoekzema Bruce Hicks AWS Convergence Technologies, Inc. Metcorps 12410 Milestone Center Drive P.O. Box 1510 Germantown, MD 20876 Norris, TN 37828 mhoekzema@aws.com hicks.metcorps@gmail.com.

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Using UrbaNet Data to Quantify the Nocturnal Heat Islands of US Cities

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  1. Using UrbaNet Data to Quantify • the Nocturnal Heat Islands of US Cities • Mark Hoekzema Bruce Hicks • AWS Convergence Technologies, Inc. Metcorps • 12410 Milestone Center Drive P.O. Box 1510 • Germantown, MD 20876 Norris, TN 37828 • mhoekzema@aws.com hicks.metcorps@gmail.com

  2. Satellite IR imagery of Washington From Bauman 2001

  3. Methodology – • Identify a presumed center of an urban heat island. • Describe circles around it, at some convenient radial increment (3 km for the present analysis). • Within each annulus, average temperatures and wind speeds derived from AWS stations. • Here, we focus on Washington, DC, and New York City. • Note – • The AWS data are obtained within the urban roughness layer. They are consequently not representative of the boundary layer aloft, however they are quite indicative of the atmosphere that affects people. Hence, the analysis that follows is not compatible with many earlier analyses that use, for example, aircraft to explore the heat island effect. Here, the focus is on what influences people directly.

  4. The distribution of AWS “Weatherbug” sites around the central areas of Washington, DC, and New York City. Selected sites of the NOAA DCNet program are also shown.

  5. Jan Feb Mar Apr May Jun Washington (DOC) 2200 - 0500 Aug Sep Jul Oct Nov Dec

  6. Jan Feb Mar Apr May Jun Washington (DOC) 1100 - 1700 Aug Jul Sep Oct Dec Nov

  7. DOC 2200 - 0500

  8. Jan Feb Mar Apr Jun May New York City (TSQ) 2200 - 0500 Jul Aug Sep Oct Nov Dec

  9. Jan Feb Mar APR Jun May New York City (TSQ) 1100 - 1700 Aug Jul Sep Oct Nov Dec

  10. TSQ

  11. 2200 - 0500 1100 - 1700 U < 1 m/s U < 1 m/s 2200 - 0500 1100 - 1700 2 < U < 3 m/s 2 < U < 3 m/s WASHINGTON, DC, JANUARY, 2007

  12. 2200 - 0500 1100 – 1700 U < 1 m/s U < 1 m/s 2200 – 0500 1100 - 1700 2 < U < 3 m/s 2 < U < 3 m/s NEW YORK CITY, JANUARY 2007

  13. Conclusions: The AWS/UrbaNet data provide intriguing spatial detail on the nature of urban heat islands. The magnitude of an urban heat island effect is not necessarily directly related to the level of heat generated in the vicinity of the city center. It is postulated that larger cities with larger roughness cause deeper urban boundary layers throughout which the heat island effect is then dissipated. This is doubtlessly also influenced by the continuing convective regimes throughout the entire daily cycle. Such regimes appear to be common, but are not universal. As is predicted by all related models, high winds tend to wash out the urban heat island.

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