Transcript Option #1 Presentation Title, Align Bottom Date of Presentation

Simulations of the Urban
Boundary Layer in Phoenix,
Arizona
Susanne Grossman-Clarke
Arizona State University
Global Institute of Sustainability
17 January 2008
Yubao Liu
NCAR, Research Applications Laboratory
Joseph A. Zehnder
Creighton University
Introduction
• Extent of Phoenix ~ 4000 km2 & population 3.7
Million: Potentially large enough to influence
mesoscale meteorological processes.
• Investigation of Phoenix’ influence on weather
(NSF ATM-0710631 and NSF DEB-0423704 CAP LTER):
• Wind, temperature and moisture fields.
• Mesoscale circulations generated by urban–rural thermal
differences.
• Orographic circulations.
• Convective activity.
• Applications
• Weather forecasting, air quality simulations, urban heat
island, human comfort and heat related illness studies.
Introduction
Physical characteristics of cities affect momentum,
turbulent heat transport & surface energy balance.
• Urban roughness.
• Increased heat storage
and heat conductivity in
built materials.
• Anthropogenic heating
(electricity consumption
and traffic).
• Long-wave radiation
trapping due to urban
form.
• Urban vegetation.
Introduction
Urban Canopy Models (UCM)
Describe area average effect of cities on drag,
turbulence production, heating, and surface
energy balance.
State variables within the urban canopy are of
interest.
Differences in physical approach and detail.
Roughness and drag approach.
Application of UCM depends on PBL scheme.
Introduction
• MM5 modifications (bulk roughness
scheme)
– Urban land use.
– Surface energy balance.
– Turbulent transport.
– Medium Range Forecast PBL scheme.
• Testing of original and modified MM5
– Surface and upper air data from two
extended field campaigns in Phoenix.
Grossman-Clarke et al. 2005, JAM
Grossman-Clarke et al. 2007, JAMC
Urban Land Use derived from ASTER
Satellite Data
Stefanov et al. 2001, Remote Sens. Environ.
MM5 Model Description
• Based on Landsat Thematic Mapper
satellite images (visible, shortwave
infrared & vegetation index).
• Post-classification in expert system using
additional data sets.
• Derive land cover data with 30 m
resolution.
Stefanov et al. 2001, Remote Sens. Environ.
MM5 Model Description
• Convert data for use in MM5 or WRF.
• Re-projecting data to the geographic
projection parameters of 30-second
USGS data set.
• Mapping categories to 24 USGS
categories.
• Land cover class with highest fraction
of cover assigned to 30sec grid cell.
MM5 Model Description
• Additional urban land use/cover
classes:
– urban built-up (no vegetation)
– mesic residential (well-watered)
– xeric residential (droughtadapted vegetation)
MM5 Model Description
24-category USGS classification and two additional urban
classes.
MM5 Model Description
Standard land use
Improved
MM5 Model Description
Surface Energy Balance Equation
Cg
Tg
t
 Rn  H  G  E  f (Tg )
Tg
…
Ground temperature (K)
cg
…
Heat capacity of the ground (J m-2 K-1)
Rn
…
Net radiation balance (W m-2)
H
…
Sensible heat flux (W m-2)
G
…
Soil heat flux (W m-2)
E
…
Latent heat flux (W m-2)
MM5 Model Description
Latent Heat Flux
M a ku qvs (Tg )  qva 


E 
za
ln
 h ( za / L)
z0
M
…
Moisture availability factor [-]
z0
…
Roughness length [m]
Yh …
Stability function [-]
qvs …
Saturation specific humidity [-]
qva …
Specific humidity at za[-]
MM5 Model Description
• Heat storage in man-made materials; modified
heat capacity and thermal conductivity (Liu et
al. 2004).
• Sky view factor  in the in the long wave
radiation balance (Noilhan 1981):

Rlong  sky g L    Tg4


road  h / w   1
2
0.5

h/w
w
– Road width
h
– Building height
MM5 Model Description
• Anthropogenic heating Qa from traffic and
electricity consumption (Sailor & Lu 2004).
Qai ,v h   ipop h  Ft h  DVDc  EV / 3600
Qai ,e h   ipop h  Fe h  Ec / 3600
Qa,v Qa,e Anthropogenic heat from traffic and electricity
pop
Avg. population density for urban LU classes
h
Hour of day
Ft Fe
Fractional traffic profiles and electricity consumption
DVDc
Avg. daily vehicle distance traveled per person in Phoenix
EV
Energy release per vehicle per meter of travel
Ec
Daily per capita electricity consumption
MM5 Model Description
Anthropogenic Heat
-2
Qa (W m )
40
35
□
Urban built-up
30

Xeric residential
25
x
Mesic residential
20
15
10
5
0
0
2
4
6
8
10
12
14
Hour (LST)
16
18
20
22
MM5 Model Description
Temperature tendency equation at first prognostic level:
T
1
 V  T 
t
ac p

 p
 Q T0
1 Qa

 V  p '   0 gw  
 DT 
 a c p z
 t
 cp 0
'
Q
Heating rate resulting from diabatic processes
Dt
Horizontal and vertical diffusion
()
Adiabatic warming
Surface Parameters
Urban
built-up
Urban mesic
residential
Urban xeric
residential
Fraction vegetative cover
0
0.23
0.1
Moisture availability
0.005
0.12
0.02
Roughness length (m)
0.8
0.5
0.5
Heat capacity
(106 J m-3 K-1)
3.0
2.4
2.7
Thermal conductivity
(W m-1 K-1)
3.24
2.4
2.6
Sky view factor
0.85
0.85
0.85
MRF Scheme
Nonlocal-K Approach for Turbulent Diffusion within
the Mixed Layer
Turbulence diffusion equation for potential temperature
within the mixed-layer:
  
 

  K z 
   
t z 
 z

Correction to local gradient
to represent large eddy
turbulence:

w
K
b
…
…
…
…
Potential temperature (K)
Vertical velocity (ms-1)
Eddy diffusivity (m2s-1)
Empirical parameter (-)


w 
b
'
'
z
Hong and Pan 1996, Monthly Weather Review
Troen and Mahrt 1986, Boundary Layer Met.
MRF Scheme
Mixed Layer Turbulent Diffusivity Coefficients
Kzm for Momentum
K zm
z

 kws z 1  
 h
k
ws
z
h
p
u*
m
Cm
Uc
...
...
...
...
...
...
...
...
...
p
with
ws  u m1
u*2  CmU c2
von Karman constant (-)
Mixed layer velocity scale (ms-1)
Height (m)
PBL height (m)
Profile shape exponent (p=2)
Friction velocity (ms-1)
Wind profile function at top of surface layer (-)
Drag coefficient for momentum (-)
Horizontal wind speed under convective conditions(ms-1)
MRF Scheme
PBL Height for Mixed Layer
h  Ribcr
Ribcr …
va …
v …
g …
U(h) …
 va U h 
2
g v h    g 
Critical bulk Richardson number (0.5)
Virtual potential temperature at first prognostic level
Virtual potential temperature at z=h
Virtual potential temperature at ground level z=0
Wind speed at z=h
MRF Scheme
Under convective conditions w* is added to U in
surface flux calculations to consider extra eddy
mixing induced by surface-layer instability:
w''  ChU C  g  a 
with
U c2  U 2  bw*2
w*  C vg  va 
0.5
Ch ...
g ...
a ...
Uc ...
U ...
w* ...
C,b ...
vg ...
va ...
Drag coefficient for heat (-)
Potential temperature at ground (K)
Potential temperature at first prognostic level (K)
Wind speed under convective conditions (ms-1)
Mean horizontal wind speed (ms-1)
Convective velocity (ms-1)
Empirical constants
Virtual potential temperature at ground (K)
Virtual potential temperature at first prognostic level (K)
MRF Scheme
Under free-convection conditions, tendency to:
• Underestimate near-surface wind speed.
• Overestimate sensible heat fluxes.
• Overestimate PBL heights.
Because:
• w* function of height of the lowest prognostic level.
• Virtual surface temperature depends on the choice of
surface model.
• High values of w* result in overestimation u*  weak
surface winds, high surface sensible heat fluxes, high PBL
heights.
Zhang and Zheng 2004, JAM
Liu et al. 2006, JAM
MRF Scheme
Beljaars’ Approach for Convective Velocity
1/ 3
 g ' '
w*   h w v 
 

• w* directly linked to surface heat flux and PBL height. Both
related to strength of convective turbulence.
• No tuning parameter.
• Parameter b in Uc calibrated with LES (0.8 – 1.3).
Beljaars 1995, Quart. J. Roy. Meteor. Soc.
Liu et al. 2006, JAM
Comparison of Model Behavior with Field
Observations
• Meteorological and atmospheric chemistry field study in
Phoenix 10 May to 10 June of 1998 (Fast et al. 2000):
– To study the convective boundary layer.
• 915-MHz radar wind profiler near Sky Harbor Airport to give
hourly values of wind speed and wind direction.
• Radiosondes near Sky Harbor Airport on 14 days at 0800,
1000, 1200, 1400, 1700 LST.
• “Phoenix Sunrise Experiment” 10 – 30 June 2001 (Doran et al.
2003):
– To study the evolving structure of the PBL during the morning
transition.
• 915-MHz radar wind profiler near Sky Harbor Airport.
• Radiosondes near Sky Harbor Airport site on 12 days at 0000,
0200, 0500, 0800, 0900 and 1000 LST.
Design of Numerical Simulations
• Fifth Generation PSU/NCAR
Mesoscale Model (MM5).
• Initial and Boundary Conditions
from NCEP/ETA grid 212 (40 km
grid spacing).
• 10 May – 10 June 1998 & 10 –
30 June 2001.
• Nested Run of MM5: 54 km  18
km  6 km  2 km.
• 51 vertical layers.
• Original and modified MRF PBL
scheme (Liu et al. 2006) and 5
layer soil model.
• Urban surface energy balance
(Grossman-Clarke et al. 2005).
Comparison of MM5 Simulations with
Field Observations
• Original MM5.
• Original MRF scheme and surface
modifications.
• Modified MRF scheme and surface
modifications.
Comparison of MM5 Simulations with Field
Observations
8 June 1998 at Sky Harbor Airport
Correcting land use improves
daytime temperatures.
Heat storage, anthropogenic heat,
sky view factor improves nighttime
temperatures.
Results - Surface Temperature and Winds
• 10 May to 10 June 1998 simulation period for NWS
station at Sky Harbor Airport.
Results - Surface Temperature and Winds
• 10 June to 30 June 2001 simulation period for NWS
station at Sky Harbor Airport.
Composite Profiles of Potential Temperature (K)
Composite Profiles of Potential Temperature (K)
Results – Potential Temperature
Composite Profiles of Potential Temperature (K)
Composite Profiles of Potential Temperature (K)
Results – Composite PBL Heights
Composite Winds (m s-1) 10 to 31 May 1998
Conclusions
• Bulk approaches for the urban surface energy
balance enabled MM5 to consistently improve
performance for near-surface meteorological
variables.
• Modified MRF PBL scheme by Liu et al. (2006)
led to improved:
− Profiles of potential temperature.
− PBL height determination
− Wind speed in the lower PBL
• MM5 can be applied in studies investigating the
influence of urbanization on weather with
higher confidence.
Work with WRF
• Ported model physics into WRF; results are applicable to
WRF YSU scheme; UCM and LSM vegetation
parameterization for Phoenix based on gas exchange
measurements.
• Investigate the combined influence of global climate
change and urbanization on near-surface air
temperatures on human comfort and health (NSF
Coupled Human Natural Systems Proposal).
• Investigate the influence of urbanization on weather in
Phoenix (NSF ATM-0710631) – Co-PIs C.S.B. Grimmond,
King’s College London & J.A. Zehnder, Creighton
University in collaboration with F. Chen, National Center
for Atmospheric Research
WRF Urban Canopy Model
• Consideration of
more detailed
characteristics of the
urban surface
(construction
materials and urban
form) and urban
vegetation processes
possible.
za
First prognostic level.
Ta, TS
Air temperature at first prognostic level and street
canyon.
TR, TW, TG
Surface temperatures of roof, wall, ground.
H, HR, HW, HG, Ha
Sensible heat fluxes.
Work with WRF
Surface Energy Balance Tower
Part I - Evaluate the UCM simulated surface energy
fluxes with:
•
Comprehensive meteorological and energy flux data
obtained from previous urban field experiments.
•
Surface energy balance measurements in at least
two typical Phoenix neighborhoods during a one year
period beginning in summer 2008.
Part II - Apply the WRF/UCM system to the Phoenix
metro area to investigate:
•
How past and potential future land use changes
influence near surface atmospheric state variables
and characteristics of the planetary boundary layer?
•
How mesoscale circulations due to the variability in
urban and rural land use interact with the mesoscale
thermal circulations due to complex terrain?
•
If the increasing extent of the urban area affects the
development and propagation of summer
thunderstorms.
Potential Effects of Phoenix on Monsoon
Convective Activity
• Increased surface roughness suppresses thunderstorm
outflow and inhibits propagation into the region.
• Urban heat island effect.
• Surface roughness causes divergence of air flow
around the urban area and convergence zone
downwind.
• Evapotranspiration from irrigated vegetation and
anthropogenic open water surfaces increases CAPE.
• Interaction of topograhically and physiographically
forced circulations.
• Pollution aerosols.
Influence of Urbanization on Nearsurface Air Temperature
• Model performance during extreme
heat events for past and projected
future land use/cover in the Phoenix
metropolitan area.
• 12 - 17 July 2003 and 9 - 12 August
2003.
• Simulations of surface temperature
and relative humidity.
WRF – Simulations for Heat Waves
12-17 July 2003 & 9-12 August 2003
Simulated
Measured
120
Sky Harbor Airport, 8-12 August 2003
2 m air temperature (F)
2 m air temperature (F)
Sky Harbor Airport, 12-17 July 2003
115
110
105
100
95
90
85
12
12
13
13
14
14
15
15
16
16
17
Measured
120
115
110
105
100
95
90
85
9
17
9
10
10
11
11
12
12
Date August 2003
Date July 2003
Sky Harbor Airport, 12-17 July 2003
Simulated
Measured
50
Sky Harbor Airport, 9-12 August 2003
Relative Humidity (%)
Relative Humidity (%)
Simulated
40
30
20
10
0
Measured
Simulated
50
40
30
20
10
0
12
12
13
13
14
14
15
15
Date July 2003
16
16
17
17
9
9
10
10
11
Date August 2003
11
12
12
Land Use/Cover Scenarios
MM5 – Simulated 2 m Air Temperatures
14 July 2003 0500 pm
MM5 – Simulated 2 m Air Temperatures
15 July 2003 0500 am
Simulated 2 m Air Temperatures
14 July 2003