Mapping Fine Structure in Manhattan’s Urban Heat Island

Dr Brian Vant-Hull

NOAA-CREST, CCNYWith Maryam Karimi, Mark Arend,

Rouzbeh Nazari, Reza Khanbilvardi

2013 CREST Symposium

QuickTime™ and a decompressor

are needed to see this picture.

2013 CREST Symposium

Consortium for Climate Risk in the Urban Northeast (CCRUN)

Columbia Mailman school

of Public Health

City College of New York

(Physical Aspects)

2013 CREST Symposium

2013 CREST Symposium

QuickTime™ and a decompressor

are needed to see this picture.

From Meir, Orton, Pullen, Holt, Thompson and Arend,Submitted to Weather and Forecasting.

New York’s Urban Heat Island as Mapped by NYC MetNet (Curated by Mark Arend)

With all this wonderful data, why would we need field campaigns?

• The stations are usually mounted on rooftops with various heights and albedo, and are not spaced at neighborhood scale

• Satellite thermal IR data (such as LandSat) also sees a lot of rooftop and treetop data.

QuickTime™ and a decompressor

are needed to see this picture.

QuickTime™ and a decompressor

are needed to see this picture.

NationalBuilding StatisticsDatabase250 m resolution

Vegetation index

RGBComposite

QuickTime™ and a decompressor

are needed to see this picture.

A mixture of satellite sensing of vegetation and building surveys at 250 m resolution.

To be related to temperature variations.

QuickTime™ and a decompressor

are needed to see this picture.

QuickTime™ and a decompressor

are needed to see this picture.

QuickTime™ and a decompressor

are needed to see this picture.

QuickTime™ and a decompressor

are needed to see this picture.

Natural History Museum Lincoln Center

Temperatures are typically ~1 C warmer at street level, Dewpoints (moisture content) are variable.

TEMPS

DEWPTS

QuickTime™ and a decompressor

are needed to see this picture.

QuickTime™ and a decompressor

are needed to see this picture.

QuickTime™ and a decompressor

are needed to see this picture.

Averages and Deviations

Standard Deviation calculated each day, and temperature differences

represented as deviations from average

QuickTime™ and a decompressor

are needed to see this picture.

Temperature Distributions of Two Locations Overcast versus Partly Cloudy Days

Data Reduction

Week 1 ……………………………………………………………………………….

Week 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Step 3:For each day,Manhattan-wide sample average and standard deviation calculated (‘daily avg’ & ‘daily SD’)from detrended data

Step 1: all walks divided into equal number of bins for spatial averaging

Step 4:

‘Differences’ = bin avgs - daily avg

‘Deviations’ = Differences/(daily SD)

Step 2:Bin averagesSubtracted from Central Park Trend

CP

Color Scheme for all Measurement Units

Bluer is lower: Yellow is Neutral: Redder is higher

White < -3.5 unitsBlue -2.5 to -3.5 unitsGreen -1.5 to -2.5 unitsYel-Grn -0.5 to -1.5 unitsYellow +/- 0.5 units; neutralOrange +0.5 to +1.5 unitsRed +1.5 to +2.5 unitsPurple +2.5 to +3.5 unitsBlack > + 3.5 units

June 8ClearCentral Park 26 CWind 7 mph 294 deg

June 29ClearCentral Park 34 CWind 7 mph 297 deg

Comparison of two days with similar meteorology(deviations)

QuickTime™ and a decompressor

are needed to see this picture.

CF

CF

79th Street

57th Street

Averages of normalized deviations of Cloudy days, Clear days, All Days

Color Scheme for all Measurement Units

Bluer is lower: Yellow is Neutral: Redder is higher

White < - 3.5 unitsBlue -2.5 to -3.5 unitsGreen -1.5 to -2.5 unitsYel-Grn -0.5 to -1.5 unitsYellow +/- 0.5 units; neutralOrange +0.5 to +1.5 unitsRed +1.5 to +2.5 unitsPurple +2.5 to +3.5 unitsBlack > + 3.5 units

Cloudy>70%2 days

All8 day

Clear4 days

x2

Tsd DPsd RHsd

Both days cloudy

70% CF 100% CF

Statistical Significance at Each Bin

<X1>

n1

<X2> n2

€

Tn1+n2−1 =< x1 > − < x2 >

σ12

n1+σ 22

n2

• In our case n is the number of days measured at each location.• But what are we comparing it to??• Unreasonable to compare to every other point, so compare to average point.• Need average number of measurements, average SD, set average value=0

<x> = 0 SDavg = 1.33 navg = 6

note our variable is # of standard deviations from average

Temperature DewPoint Rel Humidity

T valuesGreen,Red are significant

T valuesGreen,Red are significant

Temperature, RH, Light

10 instrument locations to be mounted

Regress Temp offsets from Central Park against environmental variables (evaporation, wind components, cloud fraction, etc)

Relate regression coefficients to surface characteristics (building height and density, vegetation, water, etc)

Apply to predict temperature offsets in different areas or to projected urbanization

How can this be used to downscale - current temperature maps? - weather predictions? - climate predictions?

Summary• This will be the most comprehensive measurement of

an urban environment at the 10-100 meter scale to date

• Indications are that localized street level cool spots do occur with higher buildings and vegetation as expected

• Future plans include multi-variable regression of temperature anomalies to building characteristics, vegetation, and albedo

• This work could be used to predict local variations in temperature with climate shifts and projected urban development.