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Observation & simulation of urban-effects on climate, weather, and air quality. Bob Bornstein Dept. of Meteorology, SJSU Haider Tahabbb, Altostratus, Inc. [email protected] presented at NCAR 8 August 2008. Acknowledgements. Ex-students: R. Balmori S. Kasaksch E. Weinroth Data - PowerPoint PPT Presentation
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Observation & simulation of urban-effects on Observation & simulation of urban-effects on climate, weather, and air qualityclimate, weather, and air quality
Bob BornsteinBob Bornstein
Dept. of Meteorology, SJSUDept. of Meteorology, SJSU
Haider Tahabbb, Altostratus, Inc.Haider Tahabbb, Altostratus, [email protected]
presented atpresented at
NCARNCAR
8 August 20088 August 2008
AcknowledgementsAcknowledgements
Ex-students: – R. Balmori– S. Kasaksch– E. Weinroth
Data – S. Burian, J. Ching– TCEQ, USFS– D. Byun
Urbanization of– A. Martilli– S. Dupont
Funds: NSF, USAID, DHS
OVERVIEW
> URBAN MESO-MET MODELS– FORMULATION– APPLICATIONS
Houston NYC Sacramento
> FUTURE EFFORTS
GOOD MESO-MET MODELINGGOOD MESO-MET MODELING
MUST CORRECTLY REPRODUCE:– UPPER-LEVEL Syn/GC FORCING FIRST:
pressure (“the” GC/Syn driver) Syn/GC winds
– TOPOGRAPHY NEXT:min horiz grid-spacing flow-channeling
– MESO SFC-CONDITIONS LAST:temp (“the” meso-driver) & roughness meso-winds
Mid-east Obs vs. MM5: 2 m tempMid-east Obs vs. MM5: 2 m temp (Kasakech; USAID)(Kasakech; USAID)
July 29 August 1 August 2
July 31 Aug 1 Aug2
Standard-MM5 summer night-time min-T,
But lower input deep-soil temp better 2-m T results better winds better O3
obs
Run 1
MM5:Run 4
Obs
Run 4:ReducedSeep-soil T
First 2 days show GC/Syn trend not in MM5, as MM5-runs had no analysis nudging
Recent Meso-met Model Urbanization
> Need to urbanize momentum, thermo , & TKE – surface & SfcBL diagnostic-Eqs.– PBL prognostic-Eqs.
> Start: veg-canopy model (Yamada 1982) > Veg-param replaced with GIS/RS urban-param/data
– Brown and Williams (1998)– Masson (2000)– Martilli et al. (2001) in TVM/URBMET– Dupont, Ching, et al. (2003) in EPA/MM5– Taha et al. (2005, 08), Balmori et al. (2006) in uMM5:
detailed input urban-parameters as f(x,y)
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 H to W, wall-plan, & impervious-area ratios building frontal, plan, & rooftop area densities wall and roof: ε, cρ, α, etc. vegetation: canopies, root zones, stomatal resistances
Urbanization day & nite on same line stability effects not important
Martilli/EPFL qMartilli/EPFL q2-results-results
Non-urban:
urban Urban-model values > rooftop max > match obs
uMM5 for Houston: Balmori (2006)uMM5 for Houston: Balmori (2006)
Goal: Accurate urban/rural temps & winds for Aug 2000 O3 episode via
– uMM5– Houston LU/LC & urban morphology parameters– TexAQS2000 field-study data– USFS urban-reforestation scenarios
UHI & O3 changes
H
Hi)
H L
14 UTC15
16
17
18
19
21
23j)
At 2300 UTC & summary of
N-max ----
uMM5 Simulation period: uMM5 Simulation period: 22-26 August22-26 August 2000 2000 Model configuration
– 5 domains: 108, 36, 12, 4, 1 km– (x, y) grid points:
(43x53, 55x55, 100x100, 136x151, 133x141– full- levels: 29 in D 1-4 & 49 in D-5; lowest ½ level=7 m– 2-way feedback in D 1-4
Parameterizations/physics options > Grell cumulus (D 1-2) > ETA or MRF PBL (D 1-4) > Gayno-Seaman PBL (D-5) > Simple ice moisture, > urbanization module NOAH LSM > RRTM radiative cooling
Inputs > NNRP Reanalysis fields, ADP obs data > Burian morphology from LIDAR building-data in D-5
> LU/LC modifications (from Byun)
Domain 4 (3 PM) :Domain 4 (3 PM) : cyclone off-Houston only on O cyclone off-Houston only on O33-day (25-day (25thth))
LL LL
EpisodeEpisode dayday
Urbanized Urbanized Domain 5:Domain 5: near-sfc 3-PM V, 4-days near-sfc 3-PM V, 4-days
EpisodeEpisode dayday
Cold-LCold-L
HotHot CoolCool
1 km uMM5 Houston UHI: 8 PM, 21 Aug1 km uMM5 Houston UHI: 8 PM, 21 Aug
Left:Left: MM5MM5 UHI = 2.0 K ; Right: UHI = 2.0 K ; Right: uMM5 uMM5 UHI = 3.5 UHI = 3.5 KK
UHI-Induced UHI-Induced CConvergence: obs vs. uMM5onvergence: obs vs. uMM5
OBSERVEDOBSERVED uMM5uMM5
C
C
C
C
Base-case (current) veg-cover (0.1’s) urban min (red) rural max (green)
Modeled changes of veg-cover (0.01’s) > Urban-reforestation (green)> Rural-deforestation (purple)
min
maxincrease
Run 12 (urban-max reforestation) minus Run 10 (base case): Run 12 (urban-max reforestation) minus 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
warmer
warmer
cooler
UHI(t): Base-case UHI(t): Base-case minusminus Runs 15-18 Runs 15-18
• UHI = Temp inUHI = Temp in Urban-Box minusUrban-Box minus Temp in Temp in Rural-Box Rural-Box • Runs 15-18: urbanRuns 15-18: urban re-forestation re-forestation scenariosscenarios• UHI = Run-17 UHI UHI = Run-17 UHI minusminus Run-13 UHI Run-13 UHI
max effect, green line max effect, green line • Reduced UHI Reduced UHI lower max-Olower max-O33 (not shown) (not shown)
EPA emission-reduction credits EPA emission-reduction credits $ $ savedsaved
Max-impact of –0.9 K of a 3.5 K Noon-UHI, of which1.5 K was from uMM5
URBAN
RURAL
NYC DHS NYC DHS Urban Dispersion Study:Urban Dispersion Study:
Emergency ResponseEmergency Response
NYC/UDSNYC/UDSMSG & MIDTOWNMSG & MIDTOWNDHS/STRADHS/STRAFrom: J. AllwineFrom: J. Allwine
uMM5 for NYC DHS MSG UDSuMM5 for NYC DHS MSG UDS
Goal: Accurate urban/rural temps & winds
for 9-15 March ‘05 tracer releases via – uMM5– NYC LU/LC & urban morphology
parameters from S. Burian– DHS MSG UDS field-study data
met tracer (not used as of yet)
NYC uMM5 DHS UDS MSG: NYC uMM5 DHS UDS MSG: 9-15 March9-15 March ‘05 ‘05 Model configuration
– 4 domains: 36, 12, 4, 1 km– (x, y) grid points:
(110x85, 91x91, 91x91, 33x33)– full- levels: 29 in D 1-3 & 48 in D-4; lowest ½ level=7 m– 2-way feedback in D 1-3
Parameterizations/physics options > Grell cumulus (D 1-2) > ETA or MRF PBL (D 1-4) > Gayno-Seaman PBL (D-5) > Simple ice moisture, > urbanization module NOAH LSM > RRTM radiative cooling
Inputs > NNRP Reanalysis fields, ADP obs data > Burian morphology from LIDAR building-data in D-5
> LU/LC modifications (from Byun)
NWS 700 hPa 3/NWS 700 hPa 3/1010/05: 00 & 12 UTC/05: 00 & 12 UTC
00 UTC = 19 EST00 UTC = 19 ESTon 3/9/05on 3/9/05
High speed zonal High speed zonal flow from Lowflow from Low
N of NYC N of NYC
12 UTC = 07 12 UTC = 07 ESTEST
on 3/10/05on 3/10/05
1 km uMM5 Domain1 km uMM5 Domain
MM5 movedMM5 movedLow awayLow awaytoo fasttoo fast
1 km uMM5 Domain1 km uMM5 Domain
Summary of uMM5 MSG flow fieldSummary of uMM5 MSG flow field
Low levelhigh speed (& thus weak UHI) roughness-
induced deceleration convergence upward motion
Upper level (“return flow”)compensating down motion acceleration
divergence
1 km1 km uMM5 Speed (flag = 5 m/s) & T (K) uMM5 Speed (flag = 5 m/s) & T (K)09 EST, 3/09 EST, 3/1010/05, 4 levels/05, 4 levels
WeakWeakUHIUHI
1 km uMM5 Speed (flag = 5 m/s): 1 km uMM5 Speed (flag = 5 m/s): 1010 EST, 3/ EST, 3/1010/05, 4 levels/05, 4 levels
SLOWSLOW
FASTFAST
1 km uMM5 Speed (flag = 5 m/s) & Con/Div (1/s)1 km uMM5 Speed (flag = 5 m/s) & Con/Div (1/s)11 EST, 3/11 EST, 3/1010/05, 4 levels/05, 4 levels
CONCON
DIVDIV
1 km uMM5 Speed (flag = 5 m/s) & w (m/s)1 km uMM5 Speed (flag = 5 m/s) & w (m/s)11 EST, 3/11 EST, 3/1010/05, 4 levels/05, 4 levels
UPUP
DownDown
Urban Ocean-Atmosphere Observatory (UOAO)Urban Ocean-Atmosphere Observatory (UOAO)by
Jorge E. González1, Mark Arend1, Fred Moshary1
Alan F. Blumberg2
Stuart Gaffin3, Cynthia Rosenzweig3 Dave Robinson4
Brian Colle5
Robert D. Bornstein6,1
1City College of New York (CCNY)2Stevens
3NASA Goddard Institute for Space Studies (GISS)4Rutgers University
5State University of NY (SUNY) at Stonybrook6San José State University (SJSU)
Presented to3rd Annual Interagency Workshop, NYC
15 July 2008
CCNY Met-Net: roof top sites, sodars, lidar
CCNY
NYCCT
PNT
CUNY
1900 2000 2080
UHIUHI
UHI
GW
GW
NYC Heat Burden:Past, Present, & Projected
(Columbia University & GISS)
~7oC / 13oF
2oC
7 days above 90oF 14 days above 90oF
3-4 days above 95oF
Most of Summer above 90oF
17-50 days above 95oF
GW = Global WarmingUHI = urban heat island
36
Modeling and applications of urbanized MM5 (uMM5) forHouston, Sacramento, and
SoCAB
by
Haider TahaAltostratus Inc.
37
Nested grids – two-way feedback
Drag coefficients – vegetation and buildings / shape-dependent
Multiple directions FAD-related wind and TKE computations
Multiple directions FAD / TAD directional grid-cell zo computations
Canyon orientation / urban radiation (and air flow, see next item)
Microscale model nest and feedback in grids of interest (e.g.,
high-rise or pollution/dispersion application)
Species-specific spatially-varying vegetation albedo
Spatiotemporally-varying indoor air temperature, as function of
building type, season, and heating/cooling loads
Watering schedules in evapotranspiration calculations
uMM5 updates (1 of 3)
3838
Modifications to input generation techniques, processing, and data
ingestion in model
Non-LULC-based input: remote-sensing, externally processed data, surveys, location-specific
Alternative UCP / morphology generation approach (using earth-PRO
data)
Adaptation for UHI studies; sets of surface modification scenarios
uMM5 updates, cont’d
39
Surface physical properties of roofs, walls, pavements, etc. (i.e., material, construction type, age, albedo, emissivity, etc.)
Surface types (i.e., flat roofs, sloped roofs, geometrical features, green/garden roofs, parking structures)
Canyon orientation (e.g., gridded 15º binned canyon lengths)
Vegetation-specific information: LAI (function of season), geometry, albedo, age, evergreen/deciduous, potential evapotranspiration, proximity to buildings
4-D anthropogenic heat flux (LULC-independent), source location (3-D)
4-D latent heat flux / water vapor sources, e.g., cooling towers
uMM5 updates, cont’d
40
e.g., per-LULC vertical profile averages in Downtown Sacramento (representative of that area only). Red: commercial, Brown: mixed, Light blue: industrial/commercial, Blue: residential, Yellow: industrial L
UL
C-b
ase
d b
ldg
PA
D p
rofi
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for
ex
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Sa
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0.050.1
0.150.2
0.250.3
0.350.4
102030405060708090100110120130140150160170180190200210220230240250260270280290300
m (
AG
L)
PAD (m2/m3)
resi
dent
ial
com
m/s
ervi
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indu
stria
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Building PAD profiles as basis for extrapolation into non-UCP regions of Greater Sacramento area. PAD then used in computing other parameters, e.g., FAD, TAD, h2w, w2p, mean building height, and SVF
Extrapolation to non-data regions:Vertical profiles of building and vegetation canopies
Plan-area density Top-area densityFrontal-area density
Plan-area density
Taha, H. 2008c, Atmospheric Environment
41
Sacramentonighttime heat island
Sacramentomorning cool island
for Sacramento, 1 August 2000
Meso-urban modeling; fine-resolution meteorological features
Taha, H. 2008c, Atmospheric Environment
42
Downtown Sacramento
Fine-resolution photochemical simulations
Sacramento 1-km uMM5 domain, 1300 PDT, 31 July 2000
Taha, H. 2008c, Atmospheric Environment
Change in sfc temp (top left) from increased urban surface albedo, compared to building PAD function at 1m AGL (top right). Air temp change at a randomly selected location (bottom left).
PAD (m2/m3)T (surface)
T (air)
43
e.g., impacts from UHI mitiga-tion: Sacramento Domain 5
August 1st, simulated ozone at a location in Sacramento (top of graph) and changes resulting from UHI control (bottom of graph)
Top: Simulated daily max 8-hour average ozone in Sacramento (at Folsom / Natoma monitor). Bottom: reduction (%) in daily max as RRF from UHI control.n
Potential air-quality improvements from UHI controlTaha, H. 2008c, Atmospheric Environment
10
15
20
25
30
35
40
22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18
10
15
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25
30
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40
22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18
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22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18
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22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18
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22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18
10
15
20
25
30
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22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18
C053 (urban residential)C010 Texas City (open)
C603 sub-urban industrialC034 Galveston (open)
C607 (urban industrial)KGLS Scholes Field (open)
Performance of uMM5 (base case) Houston Observed and simulated air temperature at sampling height for selected stations
(subset from 26 monitors). bold line=observed, thin line=uMM5
Near-shore stations: Note absence of characteristic diurnal signal
Urban stations: Locations are relatively removed from shore & exhibits diurnal pattern
Taha, H. 2008a, Boundary-Layer Meteorology
Overall LessonsOverall Lessons
> Models can’t assumed to be > perfect > black boxes
> Need good large-scale forcing-model fields > If obs not available, OK to make reasonable educated
estimates, e.g., for rural> deep-soil temp > soil moisture
> Need data for comparisons with simulated-fields > Need good urban
> morphological data > urbanization schemes > Need better rural-SfcBL parameterizations
FUTURE WORKFUTURE WORKuWRF
– Martilli-Taha-Chen urbanization– SST (x,y,t) from J. Pullen– S. Zilitinkevich, et al.
SfcBL stability-functions (convective to wave-q2) zoh
Sea-sfc zo
– D. Steyn diagnostic hi(x,y) scheme
– PBL-turbulence of: S. Zilitinkevich, F. Freedman, B. Galperin, L. Mahrt
FUTURE WORK (cont.)FUTURE WORK (cont.) > Applications
– Linkage (1- & 2-way) BC (x,y,t) for CFD & rapid-ER canyon-models for NYC
– UHI and heat-stress trends under climate-change conditions & Qf(x,y,z,t) (with D. Sailor for Portland)
– Urban thunderstorms (with NSF): initiation & splitting– urban Wx-forecasts (with NWS): stat & uWRF– Participation in EU MEGAPOLI urbanization project– With J. Gonzales: Silicon V. NSF Center of Excellence
(SCU); NYC UOAO (CCNY), San Juan UHI (UPR); & UHI impacts on Calif. coastal cooling with uRAMS (SCU) re O3 (with CARB), energy with CCEC), ag. (Wine Board)
Thanks for listening!Thanks for listening!
Time for discussion/questionsTime for discussion/questions