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, CApblmodel@hotmail.com

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

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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?