uMM5 simulations of urban-reforestation uMM5 simulations of urban-reforestation effects on Houston UHIs for effects on Houston UHIs for
ozone-SIP emission-reduction creditsozone-SIP emission-reduction credits
R. BornsteinR. Bornstein, H. Taha, R. Balmori, H. Taha, R. Balmori
San Jose State UniversitySan Jose State University
San Jose, CASan Jose, [email protected]
presented atpresented at
GMU Dispersion ConferenceGMU Dispersion Conference
Fairfax, VA , July 2005Fairfax, VA , July 2005
AcknowledgementsAcknowledgements
D. Hitchock & P. Smith, D. Hitchock & P. Smith, State of TexasState of Texas D. Byun, D. Byun, U. of HoustonU. of Houston J. Ching & S. Dupont, US EPAJ. Ching & S. Dupont, US EPA Steve Stetson, SWS Inc.Steve Stetson, SWS Inc. S. Burian, U. of UtahS. Burian, U. of Utah D. Nowak, US Forest ServiceD. Nowak, US Forest Service NSFNSF
OUTLINEOUTLINE uMM5uMM5
FORMULATIONFORMULATION CLUSTERCLUSTER
CURRENT APPLICATIONCURRENT APPLICATION SYNOPTIC FORCING SYNOPTIC FORCING MESOSCALE INFLUENCESMESOSCALE INFLUENCES UHI IMPACTSUHI IMPACTS
CONCLUSIONCONCLUSION WHAT WE FOUNDWHAT WE FOUND FUTURE EFFORTSFUTURE EFFORTS
Urbanization TechniquesUrbanization Techniques
UrbanizeUrbanize surface, SBL, & PBL eqs. for surface, SBL, & PBL eqs. for momentum, thermo, & TKE momentum, thermo, & TKE
Allows prediction Allows prediction withinwithin UCL UCL From From veg-veg-canopycanopy model (Yamada 1982) model (Yamada 1982) Veg param Veg param replacedreplaced with urban (GIS/RS) with urban (GIS/RS)
data data Brown and Williams, 1998Brown and Williams, 1998 **Masson, 2000Masson, 2000 Sievers, 2001Sievers, 2001 **Martilli et al., 2001 (in TVM)Martilli et al., 2001 (in TVM) **DupontDupont et al., 2003 (in MM5) et al., 2003 (in MM5)
T int
Q wall
Ts roof
Drainage outside the system
Sensible heat flux
Latent heat flux
Net radiation
Storage heat flux
Anthropogenic heat flux
Precipitation
Roughness approach
Root zone layer
Infiltration
Diffusion
Deep soil layer
Drainage
Drainage network
natural soil
roof
water
Paved surface
bare soil
Surface layer
Drag-Force approach
Rn pav Hsens pav LEpav
Gs pav Ts pav
From EPA uMM5:
Mason + Martilli (by Dupont)
Within Gayno-
Seaman
PBL/TKE scheme
Advanced urbanization
scheme from Masson (2000)
____________
_________
3 new termsin each progequation
New GIS/RS inputs for uMM5 as f (x, y, z)
land use (38 categories) roughness elements anthropogenic heat as f (t) vegetation and building heights paved-surface fractions drag-force coefficients for buildings & vegetation building height-to-width, wall-plan, & impervious-
area ratios building frontal, plan, & and rooftop area densities wall and roof: ε, cρ, α, etc. vegetation: canopies, root zones, stomatal resistances
Performance for 18 hour prediction using 12 to 96 cpus
0
2
4
6
8
10
12
14
16
18
20
22
24
12 18 24 30 36 42 48 54 60 66 72 78 84 90 96
CPU
t (h
)
MM5x
uMM5x
Performance for 18 hour prediction using 12 to 96 cpus
0
2
4
6
8
10
12
14
12 24 36 48 60 72 84 96
CPU
P (
%)
MM5x
uMM5x
Performance for 18 hour prediction
0
20
40
60
80
100
120
140
160
180
200
0 10 20 30 40 50 60 70 80 90 100
CPU
t (h
)
MM5x
uMM5x
uMM5 performance by CPUuMM5 performance by CPU
With 1 CPU: MM5 is With 1 CPU: MM5 is 10x10x faster than uMM5 faster than uMM5
With 96 CPU: MM5 is onlyWith 96 CPU: MM5 is only3x3x faster than uMM5 faster than uMM5 (< 12 CPU not shown)(< 12 CPU not shown)
With With 9696 CPU: MM5 is still gaining, but CPU: MM5 is still gaining, but MM5 has ceased to gain at MM5 has ceased to gain at 4848 CPU & CPU & then it starts to loosethen it starts to loose
Performance by physicsPerformance by physics
Performance by category
0
5
10
15
20
25
30
35
40
45
50
Sound Solve Convection SBL Radiation PBL Domain Microphysics Other
Category
P(%
)
MM5
uMM5
Performance (real time) by category
0
2
4
6
8
10
12
14
16
18
Sound Solve Convection SBL Radiation PBL Domain Microphysics Other
Category
t (h
)
MM5
uMM5
sound waves & PBLsound waves & PBL schemes schemes take most CPU in bothtake most CPU in both
urban/PBL scheme in urban/PBL scheme in uMM5 takes almost uMM5 takes almost 50%50% of all timeof all time
WWmax VS. NO. OF CPU: VS. NO. OF CPU:DIFFERENCES AT 16 & 17 HR COULD BE DUE TO DIFFERENCES AT 16 & 17 HR COULD BE DUE TO
CHANGES IN INTEGRATION TIME-STEPCHANGES IN INTEGRATION TIME-STEP
Urbanization day& nite on same line stability effects not important
Martilli/EPFL resultsMartilli/EPFL results
Non-urban:Non-urban:
urbanurban
MESO-MET ATM-MODEL MUST CAPTURES MESO-MET ATM-MODEL MUST CAPTURES ALL BC FORCING ALL BC FORCING IN CORRECT ORDERIN CORRECT ORDER
OO33 EPISODES OCCUR ON A GIVEN DAY: EPISODES OCCUR ON A GIVEN DAY: NOTNOT B/C TOPO, EMISSIONS, OR SFC MESO- B/C TOPO, EMISSIONS, OR SFC MESO-
FORCING (EXCEPT FOR FOG) CHANGESFORCING (EXCEPT FOR FOG) CHANGES BUT BUT DUE TO CHANGES IN UPPER-LEVEL SYNOPTIC DUE TO CHANGES IN UPPER-LEVEL SYNOPTIC
WX PATTERNS, WHICH WX PATTERNS, WHICH COME FROM AN EXTERNAL MODEL & WHICH COME FROM AN EXTERNAL MODEL & WHICH ALTERALTER MESO SFC-FORCING (i.e., TOPO, LAND/SEA, MESO SFC-FORCING (i.e., TOPO, LAND/SEA,
URBAN)URBAN) VIA MESO-T AND THUS V VIA MESO-T AND THUS V MUST THUS EVALUATE ABOVE FACTORS:MUST THUS EVALUATE ABOVE FACTORS:
UPPER LEVELUPPER LEVEL SYN Wx Patterns: p & then V SYN Wx Patterns: p & then V TOPOGRAPHYTOPOGRAPHY (via grid spacing): V-channeling (via grid spacing): V-channeling MESO SFCMESO SFC: T & then V: T & then V
SCOS TempsSCOS Temps
RUN 1
03-Aug-96 04-Aug-96 05-Aug-96 06-Aug-96
RUN 5
uMM5 for Houston OuMM5 for Houston O3 SIP SIP GIS/RS GIS/RS griddedgridded urban sfc parameters urban sfc parameters uMM5 + reforestationuMM5 + reforestation
reduced daytime max-reduced daytime max-UHI UHI
CMAQ/CAMx CMAQ/CAMx OO3-model + uMM5 output -model + uMM5 output reduced: reduced: emissions & photolysis ratesemissions & photolysis rates
lower lower OO3 emission-reduction emission-reduction creditscredits big big $$-savings-savings
From S. Stetson: Houston zFrom S. Stetson: Houston zoo data data
Coastal Cold-Coastal Cold-Core L on Core L on
episode day episode day at 3 PM forat 3 PM for
Domains: 1-3Domains: 1-3LL
Domain 4 Domain 4 (3 PM) :(3 PM) : Note cold-core L off of Houston on O Note cold-core L off of Houston on O33 day day (25(25thth))
LL LL
EpisodeEpisode dayday
Domain 3 (12 km) 4 PM:Domain 3 (12 km) 4 PM: cold-core L cold-core L (from SST-eddy??) (from SST-eddy??)
LL
From Julie PullenFrom Julie Pullen
LL
SST and cold-core lowsSST and cold-core lows
““Correct” Correct” wind directionwind direction + right angle + right angle coast coast
Sea-surface low-p Sea-surface low-p eddyeddy ConvergenceConvergence upwellingupwelling
Cold Cold ocean-water ocean-water
Cold-core Cold-core atm lowatm low
Urbanized Urbanized Domain 5Domain 5: near-sfc 3 PM V on 4 : near-sfc 3 PM V on 4 successive dayssuccessive days
EpisodeEpisode dayday
Base-case (current) vegetation cover (urban min)
Modeled increases in vegetation cover (urban max); values are 0.1 of those above
Soil moisture increaseSoil moisture increase for: for: Run 12 (entire area, left) and Run 12 (entire area, left) and Run 13 (urban area only, right)Run 13 (urban area only, right)
Run 12 (urban-max reforestation) Run 12 (urban-max reforestation) minus minus Run 10 (base case):Run 10 (base case): near-sfc ∆T at 4 PMnear-sfc ∆T at 4 PM
reforested central urban-area reforested central urban-area coolscools & &surrounding deforested rural-areas surrounding deforested rural-areas warmwarm
u1
sea
rur
u2
Urban temp difference between runs at location 1 (3-pt smoothing)
-1.2
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
20 0 4 8 12 16 20 0 4 8 12 16
LST
Tdif
f (K
)
run14-run13s
run15-run13s
run16-run13s
run17-run13s
run18-run13s
CMAQ ozone modeling for Houston SIPCMAQ ozone modeling for Houston SIP:: 6 tree-planting scenarios 6 tree-planting scenarios reduced UHIs (right) in urban-box 1 (left) for run reduced UHIs (right) in urban-box 1 (left) for run
1717 lower max-ozone lower max-ozone EPA emission-reduction creditsEPA emission-reduction credits
Max impactMax impact
CONCLUSIONSCONCLUSIONS
Need to capture changes in Need to capture changes in large scale large scale forcing forcing
Need to good Need to good urbanizationurbanization for urban for urban winds, temp (especially at sfc), winds, temp (especially at sfc), turbulence, etc. turbulence, etc.
Need to also have good Need to also have good SSTSST, as it is the , as it is the horiz horiz temp-gradienttemp-gradient that drives sea that drives sea breezesbreezes
Urban Urban treestrees can reduce daytime UHIs can reduce daytime UHIs and thus ozoneand thus ozone
FUTURE EFFORTSFUTURE EFFORTS Better urban meso-met modelsBetter urban meso-met models
Better Better urbanizationurbanization Better Better turbulenceturbulence (Frank Freedman’s work) (Frank Freedman’s work) Smaller horizontal Smaller horizontal gridsgrids WRFWRF
Urban meso-scale models linked withUrban meso-scale models linked with CFD urban CFD urban canyon scalecanyon scale models models
BC as f (x, y, z, t)BC as f (x, y, z, t) One and two way nestingOne and two way nesting
Downscaling Downscaling global climate-changeglobal climate-change model-results model-results UHI and thermal stressUHI and thermal stress Urban Wx (e.g., thunderstorms and flooding)Urban Wx (e.g., thunderstorms and flooding) Urban air qualityUrban air quality
The EndThe EndQuestions?Questions?