Trees and Heat Island Modeling for the - …...2004/01/21 · 2. Improved version of a 2-layer...

University of HoustonIMAQS

Trees and Heat Island Modeling for the Houston-Galveston Region:

Land Use, Meteorology, Air Quality Study(Preliminary Progress Report)

Daewon W. ByunUniversity of Houston

Institute for Multidimensional Air Quality Studies (IMAQS)

Research TeamUniversity of Houston (Prof. Daewon Byun)

San Jose State University (Prof. Bob Bornstein)GEM (Mr. Stephen Stetson)

TCEQ (Dr. Mark Estes)USDA (Dr. David Nowak)

Project supportHARC (sponsor), TCEQ, TFS (Pete Smith),

USDA, EPA (Eva Wong, Brian Timin, J. Edwards, S.T. Rao)

Objectives:Study of the effects of land use and land cover modification on the urban heat island development and on the air quality in the Houston-Galveston metropolitan area.

Methods:•Conduct meteorological, emissions, and

air quality sensitivity modeling •Improve meteorological simulations by

applying better physics•Incorporate most up-to-dated detailed

land use and land cover data

1. Ground heat budget model with multiple soil layers (5) 1, 2, 4, 8, 16cm thick

2. Improved version of a 2-layer force-restore surface energy balance model

3. Couples surface momentum, heat, and moisture fluxes

4. But, no treatment of evapotranspiration5. No moisture diffusion in the soil layer6. Requires good prescription of soil moisture7. Satellite skin temperature assimilation is a good way

to overcome items 4, 5, and 6.

Most Recent TAMU Base Simulation Uses“5-layer” slab model with Satellite Skin

Temperature Assimilation

• Standard LU/LC data used in MM5 simulation: USGS ~1-km resolution, aggregated up to 4-km.

• Higher resolution of LU/LC data available from Texas Forest Service

• MM5/slab with satellite data assimilation cannot reflect the effects of changes in the land use because it bypasses physical simulation of land-surface processes

• Test MM5/slab requires precise prescription of soil moisture

To study the effects of land use change on meteorology and air quality…

How to study the land use change effects in MM5?

• Use comprehensive land-surface model• NOAH LSM (N:National Center for

Environmental Prediction; O: Oregon State University; A: Air Force; H: Hydrological Research Lab.) (Ek et al., 2001).

• 4-layers (10, 30, 60, and 100 cm thick)• Predicts soil temperature, soil water, canopy

water, and snow/ice

Summary of problems with the MM5/NOAH simulation with default parameters.

1. Simulated daytime temperature too high, and nighttime temperature too low at urban sites.

2. The urban area was treated as if totally covered with impervious surfaces. Therefore, we have large diurnal variations in temp and very low latent/sensible heat flux ratio.

3. At rural sites, we have no daytime temperature bias, but serious nighttime temperature bias.

4. Serious delays in the development of diurnal wind speed build up – related to #3.

5. Initially, we thought it were soil moisture problem, but …?

Add canopy moisture (CM) in urban area

NOAHS3

Increase SM in urban area; Decrease SM in rural area;

(Dr. Nielsen-Gammon, J. W., 2002)

Treatment of Soil Moisture

NOAHSLABLSMS2S1 (TAMU)

Design of Meteorological Simulation

• Question: How to treat the evapotranspiration from 20% tree/vegetation coverage when the current dominant category of USGS 25-LU/LC is used?

• Experiment S3: We added new anthropogenic canopy water source in the urban area in the NOAH LSM to reflect Houston’s 20% urban vegetation and thus reduce the daytime temperature bias.

Addition of Canopy Moisture

is intercepted canopy water content

is green vegetation fraction

is input total precipitation

canopy evaporation

If exceeds S (maximum canopy capacity: 0.5 mm), the excess precipitation or drip, reaches the ground.

ucfc EEDPtW

+−−=∂∂

uE is the anthropogenic contribution to the canopy water content. A reasonable value 3x10-6 (meter of available water per second) was picked for the simulation (* need to be justified).

Surface (2-m) Temp Analysis

• Now, temperature simulations at urban areas improved, not much change in rural sites.

• Minimum temperature problem still exists

•S3A ; fixed min. temperature problems by adding LU/LC dependent emissivity,…but

82500 82512 82600 82612 82700 82712 82800 82812 82900 82912 83000 83012 83100 83112

urban_avg S1 S2 S3

82500 82512 82600 82612 82700 82712 82800 82812 82900 82912 83000 83012 83100 83112

rural_avg S1 S2 S3

Scattered Diagram of 2-m Temp2-m Temperature (S2--NOAH LSM)(Aug.29~31)

Observed (oC)20 25 30 35 40 45 50

50s152(urban)s100(urban)s115(urban)s181(urban)s167(urban)s404(urban)s169(urban)s81(urban)s1(urban)s403(urban)s11s610s611s147s10s150s154s148s145s410s48s603s604s607s608

2-m Temperature (S3--NOAH_Modified CM)(Aug.29~31)

Observed (oC)20 25 30 35 40 45 50

50s152(urban)s100(urban)s115(urban)s181(urban)s167(urban)s404(urban)s169(urban)s81(urban)s1(urban)s403(urban)s11s610s611s147s10s150s154s148s145s410s48s603s604s607s608

Scattered diagram of 2-m temperature with (a) S2; (b) S3 simulations.

(a) (b)

University of HoustonIMAQSMM5 sensitivity test continued to

correct minimum temperature problem: S3Aa. Add canopy water into urban area

b. Test emissivity value for different LU categories

(original emissivity in NOAH =1.0)

etemperaturairdailyaverageT

tconsBoltzmannStefansurfacetheofemissivitya

TaL surface

0.955Cropland0.982Dryland

0.9514Evergreen forest0.957Grassland

0.901UrbanEmissivityCategoryName

2-m temperature

UH_mod= (modified thermal conductivity + added canopy moisture + modified emissivity) for urban area+ modified emissivity in rural LU/LC

82500 82512 82600 82612 82700 82712 82800 82812 82900 82912 83000 83012 83100 83112

urban_avg GOES NOAH_fei UH_modify

Urban CAMS site average

82500 82512 82600 82612 82700 82712 82800 82812 82900 82912 83000 83012 83100 83112

rural_avg GOES NOAH_fei UH_modify

Rural CAMS site average

University of HoustonIMAQSScattered diagram of 2-m temperature the urban sites

A consistent low bias in the urban sites especially in s404 (in the boundary of urban and rural area) and s100 (close to coast area).

2-m Temperature (NOAH_UHmodify_urban)(Aug.25~31)

Observed (oC)20 25 30 35 40 45 50

s152s403s115s181s167s404s169s81s1s100

2-m Temperature (GOES_TAMU_urban)(Aug.25~31)

Observed (oC)20 25 30 35 40 45 50

s152s403s115s181s167s404s169s81s1s100

2-m Temperature (GOES_TAMU_rural)(Aug.29~31)

Observed (oC)20 25 30 35 40 45 50

s11s610s611s147s10s150s154s148s145s410s48s603s604s607s608

2-m Temperature (NOAH_UHmodify_rural)(Aug.29~31)

Observed (oC)20 25 30 35 40 45 50

s11s610s611s147s10s150s154s148s145s410s48s603s604s607s608

Scattered diagram of 2-m temperature the rural sites

Urban : 10 sites Rural : 18 sites

dominant landuse data used in MM5

82500 82600 82700 82800 82900 83000 83100

1 GOES_1 UH_1

82500 82600 82700 82800 82900 83000 83100

81 GOES_81 UH_81

82500 82600 82700 82800 82900 83000 83100

167 GOES_167 UH_167

2-m temperature in the urban sites

82500 82600 82700 82800 82900 83000 83100

115 GOES_115 UH_ 115

82500 82600 82700 82800 82900 83000 83100

181 GOES_181 UH_181

82500 82600 82700 82800 82900 83000 83100

404 GOES_404 UH_404

82500 82600 82700 82800 82900 83000 83100

603 GOES_603 UH_603

82500 82600 82700 82800 82900 83000 83100

48 GOES_48 UH_48

2-m temperature in the rural sites

82500 82600 82700 82800 82900 83000 83100

65 GOES_65 UH_ 65

82500 82600 82700 82800 82900 83000 83100

150 GOES_150 UH_150

82500 82600 82700 82800 82900 83000 83100

410 GOES_410 UH_ 410

82500 82600 82700 82800 82900 83000 83100

51 GOES_51 UH_ 51

2-m temperature in the coast sites

82500 82600 82700 82800 82900 83000 83100

152 GOES_152 UH_152

82500 82600 82700 82800 82900 83000 83100

100 GOES_100 UH_100

82500 82600 82700 82800 82900 83000 83100

11 GOES_11 UH_11

82500 82600 82700 82800 82900 83000 83100

10 GOES_10 UH_10

University of HoustonIMAQSPBL Height

Wind Profiler Sites (TexAQS-2000)Mostly on rural LU/LC

S2_Aug. 25 PBl ht(m)

8:00 10:00 12:00 14:00 16:00 18:00

LM EL HS WH LBLMp Elp HSp WHp LBp

GOES_Aug. 25 PBl ht(m)

8:00 10:00 12:00 14:00 16:00 18:00

UHmod_Aug. 25 PBl ht(m)

8:00 10:00 12:00 14:00 16:00 18:00

PBL Height Analysis with slab/satellite data assimilation, S2 and S3A on Aug. 25, 2000

8:00 10:00 12:00 14:00 16:00 18:00

Observed/Predicted Wind Direction (S1--SLAB)

120180240300360

8/24 8/25 8/26 8/27 8/28 8/29 8/30 8/31 9/ 1

Obs P rdWndDir (S 1-S LAB)

Observed/Predicted Wind Direction (S2--NOAH)

120180240300360

8/24 8/25 8/26 8/27 8/28 8/29 8/30 8/31 9/ 1

Obs P rdWndDir (S 2-NOAH)

10m Wind Direction from S1, S2 and S3 simulations:

Observed/Predicted Wind Direction (S3--NOAH_CM)

120180240300360

8/24 8/25 8/26 8/27 8/28 8/29 8/30 8/31 9/ 1

ObsWndDir P rdWndDir

10m Wind Direction from S1, S2 and S3 simulations:

Observed/Predicted Windspeed (S2--NOAH)

8/24 8/25 8/26 8/27 8/28 8/29 8/30 8/31 9/ 1

Obs P rdWndS pd (S 2-NOAH)

Observed/Predicted Windspeed (S1--SLAB)

8/24 8/25 8/26 8/27 8/28 8/29 8/30 8/31 9/ 1

Obs P rdWndS pd (S 1-S LAB)

Observed/Predicted Windspeed (S3--NOAH_CM)

8/24 8/25 8/26 8/27 8/28 8/29 8/30 8/31 9/ 1

ObsWndS pd P rdWndS pd

Shows 2-hr delay in wind speed development during day time!

Summary of UH MM5/NOAH simulation with standard USGS LU/LC input

1. Bias in daytime temperature fixed with added canopy moisture in NOAH.

2. Still there are serious delays in the development of daytime wind speed build up.

3. Nighttime min. temperature bias issues addressed by applying the LU/LC dependent emissivity in NOAH

4. But, this DID NOT SOLVE the wind problem and made PBL height prediction somewhat worse

5. Started detailed land use and land cover data

- developed methods to incorporate TFS LU/LC effects

- estimate biogenic emissions for the new LU/LC data

Preliminary Simulation of CAMx 4.02 using TAMU-Satellite assimilation

and UH-S3A

UH MET TAMU MET

UH met case shows higher ozone in east Harris County.

Differences were higher later in the afternoon in this case.

UH met – TAMU met

UH MET TAMU MET

Higher ozone is observed closer to the source areas in the UH met case.

University of HoustonIMAQSUH met – TAMU met

University of HoustonIMAQSHourly Average O3 concentration (ppb) at Layer one (08/23/2000-09/01/2000)

Model3 CAMx: TAMU_met_CB4 vs UH_met_CB4

Station TLMC (EPA481671002, C10, Texas City, Galveston Co., TX)

0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 00

Station HLAC (EPA482010024, Aldine C08, Harris Co., TX)

0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 00

Station HWAA (EPA482010046, North Wayside S-13, Hampton, Harris Co., TX)

0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 00

Station HLAA (EPA482010047, Lang T-26, Hampton, Harris Co., TX)

0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 00

CAMSTAMU_metUH_met

08/23/00 08/24/00 08/25/00 08/26/00 08/27/00 08/28/00 08/29/00 08/30/00 08/31/00 09/01/00

University of HoustonIMAQSHourly Average O3 concentration (ppb) at Layer one (08/23/2000-09/01/2000)

Model3 CAMx: TAMU_met_CB4 vs UH_met_CB4

Station HROC (EPA482010070, Houston Reg OFC C81, Hampton, Harris Co., TX)

0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 00

Station HOEA (EPA482011034, Houston East C01, Hampton, Harris Co., TX)

0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 00

Station C35C (EPA482011035, Clinton, Hampton, Harris Co., TX)

0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 00

Station HCFA (EPA482011037, Crawford T-19, Hampton, Harris Co., TX)

0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 0 6 1218 00

CAMSTAMU_metUH_met

08/23/00 08/24/00 08/25/00 08/26/00 08/27/00 08/28/00 08/29/00 08/30/00 08/31/00 09/01/00

Simulated wind appears to lag in the UH met, giving higher ozone concentrations closer to the source.

Peak ozone values are similar (sometimes UH met is higher, sometimes TAMU met is higher). UH met shows higher ozone values overall.

Ozone variations (differences observed in specific locations) are very high due to the placement of the ozone plume.

Discussion

Current Other Efforts to Improve MM5 Simulations

AMS Conference, Seattle, January 10-15, 20041. Liu et al. – Different efinition of convective velocity, linkage with LSM, new

PBL height algorithms;

(MRF’s parameterized one vs. standard CBL similarity)

2. Lee et al. – Application of different parameterizations for momentum and heat diffusivity to improve nighttime and morning transition time (Monti et al, 2002)

3. Da-lin Zhang – Diurnal variation in surface temperature and wind generated by various boundary layer schemes

5.0* )( vavgCW θθ −=3/1

= vwTghW θ

2*22 aWUUc +=

02.02 )34.0(

//−= g

m RidzdV

49.02 )08.0(

//−= g

h RidzdV

Observed and Predicted Windspeed at a=1

1/ 0 8/25 8/26 8/27 8/28 8/29 8/30 8/31 9/ 1

ObsWndSpd PrdWndSpd

Observed and Predicted Windspeed at a=0.5

8/24 8/25 8/26 8/27 8/28 8/29 8/30 8/31 9/ 1

ObsWndSpd PrdWndSpd

Observed and Predicted Windspeed at a=0

8/24 8/25 8/26 8/27 8/28 8/29 8/30 8/31 9/ 1

ObsWndSpd PrdWndSpd

2*22 aWUUc +=

Additional UH-MM5 Sensitivity Study with different a(similar to Craig Trembeck’s experiment)

Use of new LU/LC data

Replace current USGS 25-category LU data with new LU datasets for the 8 counties in the Houston-Galveston area.

1. Change land use data definition inside TERRAIN output.

2. Using newly-generated TERRAIN file which contains newer land use definition to create initial condition for MM5.

3. Increase the LANDUSE table definition from original 25-category to 42-category.

4. Modify the code of MM5 modeling system to be able to compatible with the added LU categories.

5. Modify the code of MM5 modeling system to be able to feed in thegrid-based emissivity, albedo and roughness parameters.

Stephen Stetsen, GEMPete Smith, Texas Forest Service

#####################################################################################################################################################

4km_USGS LU data# Urban# Dryland Cropland# Cropland/Grassland# Cropland/Woodland# Grassland# Shrubland# Savanna# Deciduous Broadleaf# Evergreen Needleleaf Forest# Water Bodies# Wooded Wetland

#####################################################################################################################################################

4km_ New lu data# Urban# Dryland Cropland# Cropland/Grassland# Cropland/Woodland# Grassland# Shrubland# Savanna# Deciduous Broadleaf# Evergreen Needleleaf Forest# Water Bodies# Wooded Wetland# agriculture bare# airports_urban# forests# parks# rangeland# residential# rural impervious# urban commercial# urban forest# urban grass# urban industrial# water

I-km resolution albedo estimated from LANDSAT data

4-km resolution digitizedalbedo to be used with MM5 modeling

I-km resolution emissivityestimated from LANDSAT data

1/19/2004 35

Estimation of EmissivityRoughness length from the LANDSATland use and land cover data (Stephen Stetson)

I-km resolution roughness length estimated from LANDSAT data

Trees and Heat Island Modeling for the - …...2004/01/21 · 2. Improved version of a 2-layer...

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