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Mapping Fine Structure in Manhattan’s Urban Heat Island

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

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Mapping Fine Structure in Manhattan’s Urban Heat Island

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  1. 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

  2. 2013 CREST Symposium

  3. City College of New York (Physical Aspects) Columbia Mailman school of Public Health Consortium for Climate Risk in the Urban Northeast (CCRUN) 2013 CREST Symposium

  4. New York’s Urban Heat Island as Mapped by NYC MetNet (Curated by Mark Arend) From Meir, Orton, Pullen, Holt, Thompson and Arend, Submitted to Weather and Forecasting. 2013 CREST Symposium

  5. 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.

  6. National Building Statistics Database 250 m resolution Vegetation index

  7. RGB Composite A mixture of satellite sensing of vegetation and building surveys at 250 m resolution. To be related to temperature variations.

  8. Temperatures are typically ~1 C warmer at street level, Dewpoints (moisture content) are variable. Natural History Museum Lincoln Center T E M P S D E W P T S

  9. Averages and Deviations Standard Deviation calculated each day, and temperature differences represented as deviations from average

  10. Temperature Distributions of Two Locations Overcast versus Partly Cloudy Days

  11. Data Reduction Step 2: Bin averages Subtracted from Central Park Trend CP Step 1: all walks divided into equal number of bins for spatial averaging Week 1 ………………………………………………………………………………. Week 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Step 4: ‘Differences’ = bin avgs - daily avg ‘Deviations’ = Differences/(daily SD) Step 3: For each day, Manhattan-wide sample average and standard deviation calculated (‘daily avg’ & ‘daily SD’) from detrended data

  12. Color Scheme for all Measurement Units White < -3.5 units Blue -2.5 to -3.5 units Green -1.5 to -2.5 units Yel-Grn -0.5 to -1.5 units Yellow +/- 0.5 units; neutral Orange +0.5 to +1.5 units Red +1.5 to +2.5 units Purple +2.5 to +3.5 units Black > + 3.5 units Bluer is lower: Yellow is Neutral: Redder is higher

  13. June 29 Clear Central Park 34 C Wind 7 mph 297 deg June 8 Clear Central Park 26 C Wind 7 mph 294 deg Comparison of two days with similar meteorology (deviations)

  14. 79th Street CF 57th Street CF

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

  16. Color Scheme for all Measurement Units White < - 3.5 units Blue -2.5 to -3.5 units Green -1.5 to -2.5 units Yel-Grn -0.5 to -1.5 units Yellow +/- 0.5 units; neutral Orange +0.5 to +1.5 units Red +1.5 to +2.5 units Purple +2.5 to +3.5 units Black > + 3.5 units Bluer is lower: Yellow is Neutral: Redder is higher

  17. Clear 4 days Cloudy >70% 2 days All 8 day x2

  18. RHsd Tsd DPsd

  19. Both days cloudy 100% CF 70% CF

  20. Statistical Significance at Each Bin <X1>  n1 <X2>  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

  21. T values Green, Red are significant T values Green, Red are significant Rel Humidity Temperature DewPoint

  22. Temperature, RH, Light 10 instrument locations to be mounted

  23. Regress Temp offsets from Central Park against environmental variables (evaporation, wind components, cloud fraction, etc) How can this be used to downscale - current temperature maps? - weather predictions? - climate predictions? 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

  24. 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.

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