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A Detroit Heat-Health Warning System Refinement Investigation
Evan Oswald and Dr. Richard Rood
Department of Atmospheric, Oceanic, and Space Sciences, University of Michigan
1. INTRODUCTION
3. Higher Resolution Data Sets
5. Human Comfort Model Coupling
•
• Purpose:
Concerning Heat Events
•
Heat is the most pronounced cause of weather fatalities (NWS). Climate change and urbanization will
potentially increase the frequency and severity of Heat Events in the future. (Meehl and Tebaldi, 2004)
Determining inter-urban structure of concerned variables; such as temperature, moisture, wind, and radiation.
•
Statistical methods are currently among the most developed techniques implemented in Heat Health
Warning Systems (HHWS) (Kalkstein et al, 1996)
• Data sets:
•
Yet higher order human-comfort models similar to the heat index have been shown to correlate with
Heat Events (Golden et al., 2007)
Detroit
Purpose:
•
• Michigan Department of Environmental Quality (MDEQ): 14 locations – 7 in Wayne county; hourly temperature & wind;
measured at 10 meters; some locations available since 1993 – all locations since 2000
•
• “HOBO” Temperature-Humidity data loggers: 17 locations in Wayne country; continuous temperature-humidity data
recorded; measured at 2 meters; 22 day “trial” performed from during August 2008; full summer observations in 2009
•
•
•
Detroit currently calls Heat advisories when daytime highs index temperatures are predicted over 100F
or overnight lows over 80 at Detroit Metropolitan Airport which can be shown to outside the Urban Heat
Island (Sanderson et al., 1973)
• COOP Network: cooperative network of volunteers; 13 locations in Wayne county; daily temperature maximum and
minimum; most stations back as far as 1995; data subject to high quality standards
•
Due to the infrequency of Heat Events, socio-economic situation and typical housing conditions Detroit
may be highly susceptible to Heat Events.
• Weather Underground: While not standardized, still offers potential insight into inter-urban structure; 30 locations in and
around Detroit
Statistical methods have already been developed and implemented into HHWS
(Kalkstein and Greene, 1996)
-Statistical methods typically used (SSC or SSC2) assumes homogeneous air
mass over region (Kalkstein and Greene, 1996)
It has been shown that the body responds to several variables describing the
environment in which it is immersed (Steadman, 1978)
The model, HS79, is based on heat and moisture exchange from the human body;
taking all relevant contributions into account; rooted in human biometeorology
The commonly known “Heat Index” is a simplification of the model.
• Heat/Moisture
exchanges determined by
differences & resistances
• Resistances based on
radiative, diffusive,
convective and
conductive properties
• Allows adjusting of
clothing cover, elevation,
solar insolation
variability, wind speed,
body size, and activity
Goals
•
Define spatial variability of variables effecting heat stress and assess heat stress variability using a
human comfort model
•
Correlate model output with health statistics to improve forecasting Heat Events in Detroit, both spatially
and temporally
a
b
2. Standard Data Sets
In a) the required
daily high was 77F
and
notice
that
humidity was taken
into account. It is
apparent that daytime
heating occurs at a
different
location
than
elevated
overnight
temperatures seen in
b) where the required
temperature was 57F.
Summer 2009 looks
promising. c) is a
sum of both d) and
e), which are average
differences
in
minimum
temperatures staying
above
73F
and
average difference in
maximum
temperatures in days
reaching above 86F
d
c
Purpose:
To correlate with health statistics, analysis and validation of our high-spatial resolution
data sets.
Acquired:
• Detroit Metropolitan, Willow Run, Grosse Isle Municipal and Detroit City Airports
• January 1979 through December 2007 – hourly data
• Temperature, dewpoint, wind direction and speed, cloud cover, and air pressure
Determined:
• Temperature gradient traveling into Detroit
• Bodies of water can have high influential in comfort levels or “Apparent Temperature”
• MDEQ Allen Park site, and HOBO monitors could be validated
50 F
40 F
30 F
Preliminary Analysis
•
•
•
•
Heat Island more prominent at nighttime and built up downtown areas, with variation up to a few degrees.
Daytime heating may not focus near same location as overnight maximum location!
Shows signs of downriver Heat Island structure
High humidity levels may increase daytime HI temperature near Detroit River while also showing ability to decrease daytime highs
in temperature
• HOBO locations may be relocated for this summers measurements and COOP station data analysis forthcoming
REFERENCES
20 F
Average difference in Daily Minimum
temperatures during warm conditions
(74F), and 51 days in sample. Notice the
steep gradient between Metro and Willow
Comparison of MDEQ Allen Park and Detroit
Metropolitan Airport. Sampling and height
differences were determine and explained.
Similar methods are employed for other
locations and data sets
ACKNOWLEDGEMENTS
Required Daily Minimum
temperatures was 74F, and
maximum required was 85F.
(right) Different
looks at a 2006
Heat Event.
First with
temperature
only, then
humidity, then
wind speed. We
miss variability
we when only
consider
temperature
e
Meehl, G. and Tebaldi, C, 2004: More Intense, More Frequent, and Longer Lasting Heat Waves in the 21st Century. Science 305, 994
Kalkstein, L. et al 1996: The Philadelphia Hot Weather-Healt Watch/Warning System: Development and Application, Summer 1995. Bulletin of the
American Meteorological Society vol. 77, No. 7
Golden, J. et al 2007: A biometeorology study of climate and heat-related morbidity in Phoenix from 2001 to 2006. Int J Biometorol DOI 10.1007/ s00484-0070142-3
Sanderson, M. et al 1973: Three Aspects of the Urban Climate of Detroit-Windsor. Bulletin of Applied Meteorology vol. 12, 4
Greene, S and Kalkstein, L 1996: Quantitative analysis of summer air masses in the eastern United States and an application to human mortality. Clim Res vol:
43-53
Steadman R.G 1979: The Assessment of Sultriness Part 1: A Temperature-Humidity Index Based on Humun Physiology and Clothing Science. American
Meterological Society vol 18: 861-873
Steadman R.G 1979: The Assessment of Sultriness Part 2: Effects of Wind, Extra Radiation and Barometric Pressure on Apparent Temperature. American
Meteorological Society vol 18: 874-884
60 F
(above) Once data fed into the model the output can be
correlated to arrangement of health statistics to derive
relationships. Also, the model can be used in different locations
and thus allows for comparisons of cities. (below) Daily
maximum temperature and dewpoints were entered into the
model, with other variables held constant, thus Apparent
temperature is the output. Days must have been over 76F for the
daily maximum calculation and over 56 for the daily minimum,
and average differences between locations were added here.
Thanks to the Michigan Department of Environmental Quality, Dr. Derek Posselt for assistance, Kia Zhang for HOBO validation Shannon Brines for GIS
assistance
6. CONCLUSIONS
•
•
•
•
Preliminary results of high-spatial observing networks show evidence of spatial
variability in temperature patterns
Early analysis of model output show spatial variability due to humidity values as
well
Model output has shown differences between Heat Events due to differences in
second order variables
If indeed heat stress is well modeled by comfort model, then heat stress levels
should indeed vary throughout Detroit
7. Future Work
•
•
•
•
Couple high-spatial data sets to make 1 map for each variable
Use maps to assess comfort (model output) spatial variability
Correlate reference data model output with health statistics, and determine
thresholds & warning system
Apply techniques to Phoenix, New York City, Philadelphia, St. Louis and Chicago
*Contact: Evan Oswald • Department of Atmospheric, Oceanic, and Space Sciences • University of Michigan • Ann Arbor, MI 48109 • [email protected]