Using UrbaNet Data to Quantify
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Using UrbaNet Data to Quantify the Nocturnal Heat Islands of US Cities Mark Hoekzema Bruce Hicks AWS Convergence Technologies, Inc. Metcorps 12410 Milestone Center DriveP.O. Box 1510 Germantown, MD 20876Norris, TN 37828 [email protected] [email protected]

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

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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 DriveP.O. Box 1510

  • Germantown, MD 20876Norris, TN 37828

  • [email protected]@gmail.com


Using urbanet data to quantify the nocturnal heat islands of us cities

Satellite IR imagery of Washington

From Bauman 2001


Using urbanet data to quantify the nocturnal heat islands of us cities

  • 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.


Using urbanet data to quantify the nocturnal heat islands of us cities

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.


Using urbanet data to quantify the nocturnal heat islands of us cities

Jan

Feb

Mar

Apr

May

Jun

Washington (DOC) 2200 - 0500

Aug

Sep

Jul

Oct

Nov

Dec


Using urbanet data to quantify the nocturnal heat islands of us cities

Jan

Feb

Mar

Apr

May

Jun

Washington (DOC) 1100 - 1700

Aug

Jul

Sep

Oct

Dec

Nov


Using urbanet data to quantify the nocturnal heat islands of us cities

DOC 2200 - 0500


Using urbanet data to quantify the nocturnal heat islands of us cities

Jan

Feb

Mar

Apr

Jun

May

New York City (TSQ) 2200 - 0500

Jul

Aug

Sep

Oct

Nov

Dec


Using urbanet data to quantify the nocturnal heat islands of us cities

Jan

Feb

Mar

APR

Jun

May

New York City (TSQ) 1100 - 1700

Aug

Jul

Sep

Oct

Nov

Dec


Using urbanet data to quantify the nocturnal heat islands of us cities

TSQ


Using urbanet data to quantify the nocturnal heat islands of us cities

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


Using urbanet data to quantify the nocturnal heat islands of us cities

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


Using urbanet data to quantify the nocturnal heat islands of us cities

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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