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, Inc.pblmodel@hotmail.com

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

les

for

ex

tra

po

lati

on

in

Sa

cra

me

nto

, C

ali

forn

ia

0

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

ce

indu

stria

l

tran

sp/u

til

othe

r ur

b

lo-d

ens

resi

d

hi-d

ens

resi

d

mul

tifam

ily

lo-r

ise

com

m

hi-r

ise

com

m

urb

hi-r

ise

dntn

cor

e

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

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

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

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

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

20

25

30

35

40

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