mapping fine structure in manhattan’s urban heat island
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Mapping Fine Structure in Manhattan’s Urban Heat Island. Dr Brian Vant-Hull NOAA-CREST, CCNY With Maryam Karimi, Mark Arend, Rouzbeh Nazari, Reza Khanbilvardi. 2013 CREST Symposium. 2013 CREST Symposium. City College of New York (Physical Aspects). Columbia Mailman school - PowerPoint PPT PresentationTRANSCRIPT
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
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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
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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.
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NationalBuilding StatisticsDatabase250 m resolution
Vegetation index
RGBComposite
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A mixture of satellite sensing of vegetation and building surveys at 250 m resolution.
To be related to temperature variations.
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Natural History Museum Lincoln Center
Temperatures are typically ~1 C warmer at street level, Dewpoints (moisture content) are variable.
TEMPS
DEWPTS
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Averages and Deviations
Standard Deviation calculated each day, and temperature differences
represented as deviations from average
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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)
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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.