Temp (C) and RH (%) Temp(C) and RH(%) - Confex...Most days were sunny to partially cloudy with 40%...
1
The colored ovals indicate where the field measurements were made, with colors indicating the temperature difference relative to the Central Park surface station in increments of 1 C. The red end (including violet) of the spectrum is warmer, blue colder, green neutral. As expected, tree filled Central Park is almost always the coolest location. The warmest location is 14th street, perhaps due to low buildings and more exposure to the sun. The City College and Chinatown locations have similar building heights and vegetation, but were always cooler than 14th street, perhaps because both locations are closer to large bodies of water. It should be noted that every other day in this series has a full cloud overcast, yet it doesn’t seem to affect the general pattern, challenging the assumption that since surface temperature contrasts are driven by solar radiation, decreased irradiance should decrease the contrast. Characterizing Temperature Variations Due to the Urban Heat Island for Climate Health Impacts In New York City Maryam Karimi, Dr. Vant-Hull,b. Dr. Nazari,R. Dr. Khanbilvardi, R. National Oceanic and Atmospheric Administration (NOAA- CREST), Graduate Center, CUNY New York, USA Introduction Five neighborhoods have been picked through out Manhattan for sampling. The heat temperature and relative humidity of all five areas were taken in six different days. Most days were sunny to partially cloudy with 40% to 60% clouds and few overcasts. Selected Neighborhoods and sample measurement To compare our data to weather station data, several routes were designe to include weather station locations. Of the weather station located in Manhattan, Midtown-Central Park, Midtown East- 46 Street, Lower East side- Mott Street were chosen as stops for the neighborhood routes. In comparing the neighborhoods, the average temperature, and relative humidity of all areas were calculated. The maximum and minimum temperature and relative humidity for each neighborhood are shown on the map. The color scales show temperature differences from Central Park. For Example, in comparing the neighborhoods, 14 th Street location shows the highest temperature in compare to all other locations for all the measurements taken at different dates. To see the accuracy of surface station data which are mounted on rooftops, neighborhood average temperatures and dew points at 2:00 pm from three different areas; Midtown- West, Midtown-East, and Lower East side are each compared to surface station of the same area. The data show that the surface station data are about 2 to 4 degrees off from the manual readings done by students. Acknowledgement This publication was made possible by the National Oceanic and Atmospheric Administration, Office of Education Educational Partnership Program award NA11SEC4810004. Its contents are solely the responsibility of the award recipient and do not necessarily represent the official views of the U.S. Department of Commerce, National Oceanic and Atmospheric Administration.” Student were given a map route for every trip and were Sent out simultaneously to walk designated routes. Every route was a thirty minute walk which consist of three stops; beginning, end and midpoint. Measurements were taken every 10 seconds for 30 minutes. Three of the routes had routes’ stop at the surface stations. Students were supplied with Data Logger, Relative Humidity Sensor and Air Temperature sensor. The start time for the 30 minute walks bracketed 2 pm. Sensors covered by Styrofoam cups and cardboard to provide thermal isolation and all devices were mounted at the same height. Temperature Sensor RH Sensor Data Collection and Analysis Device Data Logger- Portable data collection and analysis device (Figure 1). Air Temperature Sensor- used for low thermal mass and measure temperature range -25 to 125 C (Figure 2). Relative Humidity Sensor- used to measure the relative humidity over the range 0 to 95% (Figure 3) Weather Stations in NYC Temp (C) and RH (%) Over Time 24 26 28 30 32 34 36 38 10 15 20 25 30 35 Temp (C) and RH (%) Time Relative Humidity (%) Temperature © Temp (C) and RH (%) Over Time 24 26 28 30 32 34 36 38 40 10 15 20 25 30 35 Temp (C) and RH (%) Time Relative Humidity (%) Temperature © Temp (C) and RH (%) Over Time 20 25 30 35 40 1000 1500 2000 2500 Temp (C) and RH (%) Time Relative Humidity (%) Temperature © Average Dew Point 13 Average Dew Point 9 Average Dew Point 11 Temp(C) and RH(%) 10.0 15.0 20.0 25.0 30.0 35.0 40.0 10.0 15.0 20.0 25.0 30.0 35.0 Temp (C) and RH (%) Time RH(%) Temp (C) Temp(C) and RH(%) 10.0 15.0 20.0 25.0 30.0 35.0 40.0 10.0 15.0 20.0 25.0 30.0 35.0 Temp (C) and RH (%) Time RH(%) Temp (C) Average Dew Point 12.6 Temp (C) and RH (%) 24.0 26.0 28.0 30.0 32.0 34.0 36.0 38.0 40.0 10.0 15.0 20.0 25.0 30.0 35.0 Temp (C) and RH (%) Time RH(%) Temp (C) Average Dew Point 11.4 Temp (C) and RH (%) 24.0 26.0 28.0 30.0 32.0 34.0 36.0 38.0 40.0 10.0 15.0 20.0 25.0 30.0 35.0 Temp (C) and RH (%) Time RH(%) Temp (C) Average Dew Point 11.4 Temp (C) and RH (%) Over Time 24 26 28 30 32 34 36 38 10 15 20 25 30 35 Temp (C) and RH (%) Time Relative Humidity (%) Temperature © Temp (C) and RH (%) Over Time 24 26 28 30 32 34 36 38 40 10 15 20 25 30 35 Temp (C) and RH (%) Time Relative Humidity (%) Temperature © Temp (C) and RH (%) Over Time 20 25 30 35 40 1000 1500 2000 2500 Temp (C) and RH (%) Time Relative Humidity (%) Temperature © Average Dew Point 13 Average Dew Point 9 Average Dew Point 11 Temp(C) and RH(%) 10.0 15.0 20.0 25.0 30.0 35.0 40.0 10.0 15.0 20.0 25.0 30.0 35.0 Temp (C) and RH (%) Time RH(%) Temp (C) Temp(C) and RH(%) 10.0 15.0 20.0 25.0 30.0 35.0 40.0 10.0 15.0 20.0 25.0 30.0 35.0 Temp (C) and RH (%) Time RH(%) Temp (C) Average Dew Point 12.6 Temp (C) and RH (%) 24.0 26.0 28.0 30.0 32.0 34.0 36.0 38.0 40.0 10.0 15.0 20.0 25.0 30.0 35.0 Temp (C) and RH (%) Time RH(%) Temp (C) Average Dew Point 11.4 Temp (C) and RH (%) 24.0 26.0 28.0 30.0 32.0 34.0 36.0 38.0 40.0 10.0 15.0 20.0 25.0 30.0 35.0 Temp (C) and RH (%) Time RH(%) Temp (C) Average Dew Point 11.4 Temp (C) and RH (%) Over Time 24 26 28 30 32 34 36 38 10 15 20 25 30 35 Temp (C) and RH (%) Time Relative Humidity (%) Temperature © Temp (C) and RH (%) Over Time 20 22 24 26 28 30 32 34 36 38 0 5 10 15 20 25 30 35 Temp (C) and RH (%) Time Relative Humidity (%) Temperature © Temp (C) and RH (%) Over Time 24 26 28 30 32 34 36 38 40 10 15 20 25 30 35 Temp (C) and RH (%) Time Relative Humidity (%) Temperature © Temp (C) and RH (%) Over Time 20 22 24 26 28 30 32 34 36 38 40 0 5 10 15 20 25 30 35 Temp (C) and RH (%) Time Relative Humidity (%) Temperature © Temp (C) and RH (%) Over Time 20 25 30 35 40 1000 1500 2000 2500 Temp (C) and RH (%) Time Relative Humidity (%) Temperature © Temp (C) and RH (%) Over Time 15 20 25 30 35 40 0 500 1000 1500 2000 2500 Temp (C) and RH (%) Time Relative Humidity (%) Temperature © Average Dew Point 13 Average Dew Point 9 Average Dew Point 11 Temp(C) and RH(%) 10.0 15.0 20.0 25.0 30.0 35.0 40.0 10.0 15.0 20.0 25.0 30.0 35.0 Temp (C) and RH (%) Time RH(%) Temp (C) Temp(C) and RH(%) 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 Temp (C) and RH (%) Time RH(%) Temp (C) Average Dew Point 12.6 Temp (C) and RH (%) 24.0 26.0 28.0 30.0 32.0 34.0 36.0 38.0 40.0 10.0 15.0 20.0 25.0 30.0 35.0 Temp (C) and RH (%) Time RH(%) Temp (C) Average Dew Point 11.4 Temp (C) and RH (%) 20.0 22.0 24.0 26.0 28.0 30.0 32.0 34.0 36.0 38.0 40.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 Temp (C) and RH (%) Time RH(%) Temp (C) Average Dew Point 11.4 The uneven surface heating in the urban environment causes variations in temperature between neighborhoods. As the heat has been increasing over the years, human exposure to excessively warm weather, especially in crowded city like New York, becomes an important public health problem. Some neighborhoods have great exposure to heat stress depending on their physical characteristics and sparse vegetation. The Consortium for Climate Risk in the Urban Northeast (CCRUN) at Columbia University have a five year mission to Leverage their research to create new products of interest to climate stakeholders, including the health sector . They are attempting to package air quality, heat, and water events for short term and climatic forecasts to assess the climate impact on urban areas to neighborhood or regional scale. In collaboration with CCRUN, we are planning on downscaling of weather forecasts to predict temperature/wind on neighborhood scale based on surface observations, vertical profiles, and satellites to find Correlations between health (hospital statistics) and fine scale observed temperature to relate Weather/Climate Forecasts to Health. As a part of the study we are comparing the temperature between different neighborhoods of New York City and current weather stations. Temperature and Relative Humidity Measurements in various neighborhood of NYC, July 15, 2011 Broken Clouds July 15, 2011 Temperature Distributions and Cloud Coverage Two neighborhoods were compared on an overcast day (July 18, 2011: solid lines) and a partly cloudy day (July 19, 2011: dashed lines). 14th street (green) was generally the hottest of all the neighborhoods, while Midtown West (gray) was generally the most humid. The plots show temperature histograms in bins of 0.5 C. It is clear that the distributions are broader on days with broken clouds, almost certainly due to larger solar heating effects. The differences between average neighborhood temperatures follow the same pattern: 3.4 C difference on an overcast day compared to a 5.4 C difference on the day with broken clouds, despite the fact that the overcast day was several degrees warmer. Preliminary Results For Summer Campaign In order to find if the differences between neighborhoods are meaningful, for each day a T-test is performed between all possible pairs of neighborhoods. For data sets of this size, T-test values greater than 2 indicate the differences between the average values are statistically significant at the 5% level. Most neighborhood temperature averages are statistically distinct from each other (selected examples are show to the right), but a few neighborhoods are very similar. For most days 145th street and the Lower East Side neighborhoods are statistically indistinguishable. T-Test In heat island effect campaigns conducted with portable instrumentation in Manhattan, some preliminary results show the following: • Various neighborhoods show warm temperature biases of several degrees compared to the Central Park weather station. • These biases were consistent on several days. • Temperature differences tend to decrease on overcast days, and in the winter. •The temperature contrasts within neighborhoods decreased with cloud cover. •LandSat data classifies the surface at resolution comparable to the field measurements. Temp Comparison Over Two Areas 0 1 2 3 4 5 6 0 1.7 3.3 5 6.7 8.3 10 11.7 13.3 15 16.7 18.3 20 21.7 23.3 25 26.7 28.3 30 Time Temp 14th Street Temperature Convent Avenue Temperature 24 hour Station/Neighborhood Comparisons A B C D E F A B C D E F Street level measurements in the vicinity of two weather stations were made roughly every 2 hours at the locations shown on the map to the right. The measurements start at 2 pm (14 hours) on November 19 and continue until 12 pm (36 hours) the next day. These street measurements were generally 1 to 2 degrees warmer than the station measurements, a pattern that continued throughout the night. Comparison to Weather Stations Average Temperature Differences from Central Park July 18, 2011 -15.0 -10.0 -5.0 0.0 5.0 10.0 15.0 20.0 14 St& Lower East Neighborhood T Test Value 145 St&Mid-W 145 St& Mid-E 145 St& 14 St 145 St& Lower East Mid-W& Mid-E Mid-W& 14 St Mid-W& Lower East Mid-E& 14 St 14 St& Lower East Preliminary Results For Summer Campaign City College Central park Upper W. side Midtown East 14th street Chinatown Scale (C) -1.5-2.5 -0.5-1.5 +/- 0.5 +0.5-1.5 +1.5-2.5 +2.5-3.5 > 3.5 The greatest temperature difference during summer was seen between 14th Street and Convent Avenue area, so a winter comparison was done to see if the same pattern held during the winter. The figure on the right shows this comparison of the temperature measurements in the two neighborhoods. In contrast to the summer, there was very little difference between the two temperature averages in the winter. This may be due to the absence of transpiration effects in the winter (the convent avenue area has more trees) or to comparatively warmer river temperatures nearby. The sudden jumps in the temperature around the CCNY campus occurred in the vicinity of a metallic sided building; when these are removed the similarities between the two neighborhoods increases. Winter Comparison Surface Characterization LandSat data can classify the surface at resolution comparable to the field measurements. Landsat data is being used to find types of ground cover from building density to water to types of vegetation. We can see that the East 14 th Street hotspot corresponds to low vegetation density, the cooler spot near city college could correspond to patches of vegetation. Summer 2011 data Conclusion Paper# S45 Identify and validate Thermal Neighborhoods – do uniform physical characteristics result in a uniform temperature bias throughout the neighborhoods? Object Methodology Data Logger Selected Neighborhood and Comparison
Transcript of Temp (C) and RH (%) Temp(C) and RH(%) - Confex...Most days were sunny to partially cloudy with 40%...
The colored ovals indicate where the field measurements were made, with colors indicating the temperature difference relative to the
Central Park surface station in increments of 1 C. The red end (including violet) of the spectrum is warmer, blue colder, green
neutral. As expected, tree filled Central Park is almost always the coolest location. The warmest location is 14th street, perhaps due to
low buildings and more exposure to the sun. The City College and Chinatown locations have similar building heights and vegetation,
but were always cooler than 14th street, perhaps because both locations are closer to large bodies of water. It should be noted that
every other day in this series has a full cloud overcast, yet it doesn’t seem to affect the general pattern, challenging the assumption
that since surface temperature contrasts are driven by solar radiation, decreased irradiance should decrease the contrast.
Characterizing Temperature Variations Due to the Urban Heat Island for
Climate Health Impacts In New York City
Maryam Karimi, Dr. Vant-Hull,b. Dr. Nazari,R. Dr. Khanbilvardi, R. National Oceanic and Atmospheric Administration (NOAA- CREST), Graduate Center, CUNY
New York, USA
Introduction
Five neighborhoods have been picked through out
Manhattan for sampling. The heat temperature and
relative humidity of all five areas were taken in six
different days. Most days were sunny to partially cloudy
with 40% to 60% clouds and few overcasts.
Selected Neighborhoods
and sample measurement
To compare our data to weather station data, several routes
were designe to include weather station locations. Of the
weather station located in Manhattan, Midtown-Central
Park, Midtown East- 46 Street, Lower East side- Mott Street
were chosen as stops for the neighborhood routes.
In comparing the neighborhoods, the average temperature, and relative humidity of all areas were calculated. The maximum and
minimum temperature and relative humidity for each neighborhood are shown on the map. The color scales show temperature
differences from Central Park. For Example, in comparing the neighborhoods, 14th Street location shows the highest temperature in
compare to all other locations for all the measurements taken at different dates.
To see the accuracy of surface station data
which are mounted on rooftops,
neighborhood average temperatures and
dew points at 2:00 pm from three different
areas; Midtown- West, Midtown-East, and
Lower East side are each compared to
surface station of the same area. The data
show that the surface station data are
about 2 to 4 degrees off from the manual
readings done by students.
Acknowledgement
This publication was made possible by the National Oceanic and Atmospheric Administration, Office of Education Educational Partnership
Program award NA11SEC4810004. Its contents are solely the responsibility of the award recipient and do not necessarily represent the
official views of the U.S. Department of Commerce, National Oceanic and Atmospheric Administration.”
Student were given a map route for every trip and were Sent out simultaneously to walk designated routes. Every route was a thirty minute walk which consist of three stops; beginning, end and midpoint. Measurements were taken every 10 seconds for 30 minutes. Three of the routes had routes’ stop at the surface stations. Students were supplied with Data Logger, Relative Humidity Sensor and Air Temperature sensor. The start time for the 30 minute walks bracketed 2 pm. Sensors covered by Styrofoam cups and cardboard to provide thermal isolation and all devices were mounted at the same height.
Temperature Sensor
RH Sensor
Data Collection and
Analysis Device
Data Logger- Portable data collection
and analysis device (Figure 1).
Air Temperature Sensor- used for low
thermal mass and measure temperature
range -25 to 125 C (Figure 2).
Relative Humidity Sensor- used to
measure the relative humidity over the
range 0 to 95% (Figure 3)
Weather Stations in NYC
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The uneven surface heating in the urban environment causes variations in temperature between
neighborhoods. As the heat has been increasing over the years, human exposure to excessively
warm weather, especially in crowded city like New York, becomes an important public health
problem. Some neighborhoods have great exposure to heat stress depending on their physical
characteristics and sparse vegetation. The Consortium for Climate Risk in the Urban Northeast
(CCRUN) at Columbia University have a five year mission to Leverage their research to create
new products of interest to climate stakeholders, including the health sector . They are attempting
to package air quality, heat, and water events for short term and climatic forecasts to assess the
climate impact on urban areas to neighborhood or regional scale.
In collaboration with CCRUN, we are planning on downscaling of weather forecasts to predict
temperature/wind on neighborhood scale based on surface observations, vertical profiles, and
satellites to find Correlations between health (hospital statistics) and fine scale observed
temperature to relate Weather/Climate Forecasts to Health.
As a part of the study we are comparing the temperature between different neighborhoods of New
York City and current weather stations.
Temperature and Relative Humidity Measurements in
various neighborhood of NYC, July 15, 2011
Broken Clouds
July 15, 2011
Temperature Distributions and Cloud Coverage
Two neighborhoods were compared on an overcast day (July 18,
2011: solid lines) and a partly cloudy day (July 19, 2011: dashed
lines). 14th street (green) was generally the hottest of all the
neighborhoods, while Midtown West (gray) was generally the most
humid. The plots show temperature histograms in bins of 0.5 C. It is
clear that the distributions are broader on days with broken clouds,
almost certainly due to larger solar heating effects. The differences
between average neighborhood temperatures follow the same
pattern: 3.4 C difference on an overcast day compared to a 5.4 C
difference on the day with broken clouds, despite the fact that the
overcast day was several degrees warmer.
Preliminary Results For Summer Campaign
In order to find if the differences between neighborhoods are meaningful, for each
day a T-test is performed between all possible pairs of neighborhoods. For data sets
of this size, T-test values greater than 2 indicate the differences between the average
values are statistically significant at the 5% level. Most neighborhood temperature
averages are statistically distinct from each other (selected examples are show to the
right), but a few neighborhoods are very similar. For most days 145th street and the
Lower East Side neighborhoods are statistically indistinguishable.
T-Test
In heat island effect campaigns conducted with portable instrumentation in Manhattan, some preliminary results
show the following:
• Various neighborhoods show warm temperature biases of several degrees compared to the Central Park weather station.
• These biases were consistent on several days.
• Temperature differences tend to decrease on overcast days, and in the winter.
•The temperature contrasts within neighborhoods decreased with cloud cover.
•LandSat data classifies the surface at resolution comparable to the field measurements.
Temp Comparison Over Two Areas
0
1
2
3
4
5
6
0
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26.7
28.3 30
Time
Tem
p
14th Street Temperature
Convent Avenue Temperature
24 hour Station/Neighborhood Comparisons
A B
C D
E F
A
B
C
D E
F
Street level measurements in the vicinity of two weather
stations were made roughly every 2 hours at the locations
shown on the map to the right. The measurements start at 2 pm
(14 hours) on November 19 and continue until 12 pm (36
hours) the next day. These street measurements were generally
1 to 2 degrees warmer than the station measurements, a pattern
that continued throughout the night.
Comparison to Weather Stations
Average Temperature Differences from Central Park
July 18, 2011
-15.0
-10.0
-5.0
0.0
5.0
10.0
15.0
20.0
14 St& Lower East
Neighborhood
T T
est
Valu
e
145 St&Mid-W
145 St& Mid-E
145 St& 14 St
145 St& Lower East
Mid-W& Mid-E
Mid-W& 14 St
Mid-W& Lower East
Mid-E& 14 St
14 St& Lower East
Preliminary Results For Summer Campaign
City College
Central park
Upper W. side
Midtown East
14th street
Chinatown
Scale (C)
-1.5-2.5
-0.5-1.5
+/- 0.5
+0.5-1.5
+1.5-2.5
+2.5-3.5
> 3.5
The greatest temperature difference
during summer was seen between 14th
Street and Convent Avenue area, so a
winter comparison was done to see if the
same pattern held during the winter. The
figure on the right shows this comparison
of the temperature measurements in the
two neighborhoods. In contrast to the
summer, there was very little difference
between the two temperature averages in
the winter. This may be due to the
absence of transpiration effects in the
winter (the convent avenue area has more
trees) or to comparatively warmer river
temperatures nearby. The sudden jumps in
the temperature around the CCNY campus
occurred in the vicinity of a metallic sided
building; when these are removed the
similarities between the two
neighborhoods increases.
Winter Comparison
Surface Characterization
LandSat data can classify the surface at resolution comparable to the field measurements. Landsat data is being used to find
types of ground cover from building density to water to types of vegetation. We can see that the East 14th Street hotspot
corresponds to low vegetation density, the cooler spot near city college could correspond to patches of vegetation.
Summer 2011 data
Conclusion
Paper# S45
Identify and validate Thermal Neighborhoods –
do uniform physical characteristics result in a
uniform temperature bias throughout the
neighborhoods?
Object
Methodology
Data Logger
Selected Neighborhood and Comparison
![Product DataProduct Data AC ACII A/RH-TEMP Series · RH3 (+/- 3%) RH2 (+/- 2%) (SP) Stainless Plate [ ] Temp Sensor 100 1K 1.8K 2.2K ASI 3K AN(Type III) CP(Type II) CSI 20K 100K 1K](https://static.fdocuments.net/doc/165x107/5f4be3ec917a1140b8450232/product-dataproduct-data-ac-acii-arh-temp-rh3-3-rh2-2-sp-stainless.jpg)
![F. lollipop_offsets.m · Web viewthermal_strain(i) = thermExp*(Temp(i) - Temp(1))*1000000; % in microstrain end end F. weightedMeanLJ1_v2.m function [ Temp,RH ] = weightedMeanLJ1_v2(](https://static.fdocuments.net/doc/165x107/5aff06fc7f8b9a434e8ff7e1/f-lollipop-viewthermalstraini-thermexptempi-temp11000000-in.jpg)
![R7428A1006 Introductions-Apr6 (2) [Read-Only]file.yizimg.com/341753/2010010811232080.pdf · Innovation • Constant RH-Temp control ... Integral Parameter Setting Integral Parameter](https://static.fdocuments.net/doc/165x107/6022ab4007d20a345f2569ad/r7428a1006-introductions-apr6-2-read-onlyfile-innovation-a-constant-rh-temp.jpg)
