remote sensing derived land use/cover data for urban modeling in mm5 and wrf susanne grossman-clarke...

Remote Sensing Derived Remote Sensing Derived Land Use/Cover Data for Land Use/Cover Data for Urban Modeling in MM5 Urban Modeling in MM5

and WRF and WRF

Susanne Grossman-ClarkeSusanne Grossman-Clarke11

Joseph A. ZehnderJoseph A. Zehnder11

William StefanovWilliam Stefanov22

Matthias MoellerMatthias Moeller11

11International Institute for Sustainability, Arizona State International Institute for Sustainability, Arizona State UniversityUniversity

22Image Science and Analysis Laboratory, NASA Johnson Image Science and Analysis Laboratory, NASA Johnson Space CenterSpace CenterFunded by National Science Foundation, NASA, Salt River Project

Representation of urban areas Representation of urban areas in mesoscale meteorological in mesoscale meteorological

modelsmodels

Land use/cover classes and Land use/cover classes and physical characteristics.physical characteristics.

Physical description of Physical description of momentum, energy and matter momentum, energy and matter exchange between the urban exchange between the urban surface and the atmosphere.surface and the atmosphere.

Land use data in MM5 & WRFLand use data in MM5 & WRF

Global 24-category USGS Land Global 24-category USGS Land Use/Cover.Use/Cover.

Vegetation cover from 1-km Advanced Vegetation cover from 1-km Advanced Very High Resolution Radiometer Very High Resolution Radiometer data (1992/1993).data (1992/1993).

Urban areas from Digital Chart of the Urban areas from Digital Chart of the World (Defense Mapping Agency World (Defense Mapping Agency 1992; photogrammetric analyses 1992; photogrammetric analyses imagery acquired in the 1960s).imagery acquired in the 1960s).

The Phoenix Metropolitan area

Brazel et al. 2000: The tale of two climates—Baltimore and Phoenix urban LTER sites. Clim Res., Vol. 15: 123–135.

Remote sensing derived land Remote sensing derived land use/cover data for Phoenix, AZuse/cover data for Phoenix, AZ

Based on Landsat Thematic Mapper Based on Landsat Thematic Mapper satellite images.satellite images.

Postclassification in expert system Postclassification in expert system using additional data sets.using additional data sets.

Land cover data 30 meter Land cover data 30 meter resolution.resolution.

Stefanov et al. 2001, Remote Sens. Environ., 77, 173-185.

Convert data for use in MM5Convert data for use in MM5

Re-projecting data to the geographic Re-projecting data to the geographic projection parameters of 30-second projection parameters of 30-second USGS data set.USGS data set.

Mapping categories to 24 USGS Mapping categories to 24 USGS categories.categories.

Land cover class with highest fraction of Land cover class with highest fraction of cover assigned to 30 s grid cell.cover assigned to 30 s grid cell.

Additional urban land use/cover classes:Additional urban land use/cover classes: urban built-up (no vegetation)urban built-up (no vegetation) mesic residential (well-watered)mesic residential (well-watered) xeric residential (drought-adapted vegetation)xeric residential (drought-adapted vegetation)

MM5 TERRAIN 2 km land MM5 TERRAIN 2 km land use/coveruse/cover

standard improved

Urban built-up

Urban mesic residential

Urban xeric residential

Fraction of vegetation cover (/)

0 0.23 (irrigated)0.1 (partly irrigated)

Moisture availability factor (/)

0.005 0.12 0.02

Roughness length for momentum [m]

0.8 0.5 0.5

Volumetric heat capacity (106 J m-3 K-1)

3.0 2.4 2.7

Thermal conductivity (W m-1 K-1)

3.24 2.4 2.6

Sky view factor (/) 0.85 0.85 0.85

Refinements to model physics

Sky view factor. Anthropogenic heating from cars and

air conditioners (Sailor and Lu 2004). Volumetric heat capacity and thermal

conductivity of man-made materials.

Sky view factorSky view factor

whwhroad /1/5.02

4ggskylong TLR

road Sky view factor for roads

sky Sky view factor

h Building height

w Road width

Rlong Long-wave radiation balance

L Incoming long-wave radiation

Tg Ground temperature

g Emissivity of the ground

Anthropogenic heatAnthropogenic heat

Qa,v Qa,e Anthropogenic heat from traffic and electricity consumption

pop Avg. population density for urban LU classes

h Hour of day

Ft Fe Hourly fractional traffic profiles and electricity consumption

DVDc Avg. daily vehicle distance traveled per person in Phoenix

EV Energy release per vehicle per meter of travel

Ec Daily per capita electricity consumption

3600/, EVDVDhFhhQ ctipop

iva

3600/, ceipop

iea EhFhhQ

Diurnal variation of anthropogenic Diurnal variation of anthropogenic heat for Phoenixheat for Phoenix

0

5

10

15

20

25

30

35

40

0 2 4 6 8 10 12 14 16 18 20 22

Hour (LST)

Qa (

W m

-2)

(-x -) urban built-up; (--) mesic residential; (--) xeric residential

Design of numerical Design of numerical simulationssimulations

1700 LST June 07 – 1700 LST June 10, 1998.

Nested 54, 18, 6, 2 km. 51 vertical layers. NCEP Eta Analysis 40 km. 30 sec terrain & land use data. MRF boundary layer scheme (Liu et al.

2004). 5 layer soil model.

Simulations of the surface temperature

Correcting landcover improvesdaytime temperatures.

Heat storage, anthropogenic heat, sky view factor improves nighttime temperatures.

Model performance validated with observations from the National Weather Service, Salt River Project (PRISMS) and U of Arizona Agricultural Extension (AZMET) through a variety of landcover categories

300

500

400800

500

1200

1400

500

700

500

800

46 51 55 60 64 69 735 am temperature current landcover

300

500

400800

500

1200

1400

500

700

500

800

60 65 70 75 80 85 905 pm temperature current landcover

Regional surface temperature prediction (8 June 1998)

5 pm 5 am

Relatively little temperature difference between urban and desert areas during the day

Urban heat island at nightTemperatures 8-10 oF in city compared to outlying areas

Simulated surface energy fluxes for 7-10 June Simulated surface energy fluxes for 7-10 June 19981998

at Sky Harbor Airport at Sky Harbor Airport

(—) net radiation; (---) sensible heat flux; (---) latent heat flux;(—) net radiation; (---) sensible heat flux; (---) latent heat flux;(—) heat storage flux (—) heat storage flux

for original and ‘urbanized’ values of for original and ‘urbanized’ values of ccgg and and TT..

-200

-100

0

100

200

300

400

500

600

700

800

17 23 5 11 17 23 5 11 17 23 5 11

Hour (LST)

Su

rfa

ce

En

erg

y F

lux

es

(W

m-2

)


at Pringl (change from ‘shrubland’ to ‘xeric at Pringl (change from ‘shrubland’ to ‘xeric residential’)residential’)

-200

-100

0

100

200

300

400

500

600

700

800

17 23 5 11 17 23 5 11 17 23 5 11

Hour (LST)

Su

rfa

ce

En

erg

y F

lux

es

(W

m-2

)


for the 1998 26-category and USGS 24-category LULC.for the 1998 26-category and USGS 24-category LULC.


at Sheely (change from ‘irrig. ag.’ to ‘xeric at Sheely (change from ‘irrig. ag.’ to ‘xeric residential’)residential’)



-200

-100

0

100

200

300

400

500

600

700

800

17 23 5 11 17 23 5 11 17 23 5 11

Hour (LST)

Su

rfa

ce

En

erg

y F

lux

es

(W

m-2

)


at Pera (change from ‘urban’ to ‘xeric at Pera (change from ‘urban’ to ‘xeric residential’)residential’)



-200

-100

0

100

200

300

400

500

600

700

800

17 23 5 11 17 23 5 11 17 23 5 11

Hour (LST)

Su

rfac

e E

ner

gy

Flu

xes

(W m

-2)

WRF/Noah/UCM WRF/Noah/UCM (Kusaka and Kimura 2004; Chen (Kusaka and Kimura 2004; Chen et al.et al. 2004) 2004)

Land use/cover characteristics Land use/cover characteristics in WRF/Noah/UCMin WRF/Noah/UCM

Fraction cover of vegetation and man-made Fraction cover of vegetation and man-made surfaces.surfaces.

Four urban LULC classes (high, medium, low Four urban LULC classes (high, medium, low density and commercial industrial).density and commercial industrial).

Building height, coverage area, roof and wall area Building height, coverage area, roof and wall area ratio.ratio.

Roughness length and zero plane displacement Roughness length and zero plane displacement height.height.

Albedo and emissivity.Albedo and emissivity. Thermal conductivity and volumetric heat capacity Thermal conductivity and volumetric heat capacity

of roof, wall and road.of roof, wall and road.

ChallengeChallenge

Derive WRF/Noah/UCM input Derive WRF/Noah/UCM input parameters in an efficient and parameters in an efficient and easy to update way for US cities easy to update way for US cities and cities around the globe.and cities around the globe.

Use of remote sensing derived Use of remote sensing derived data from Satellite or Lidar.data from Satellite or Lidar.

ASTER derived land use cover ASTER derived land use cover for use in WRF/Noah/UCMfor use in WRF/Noah/UCM

Advanced Spaceborne Thermal Emission Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER).and Reflection Radiometer (ASTER).

3 bands visible to near-infrared 3 bands visible to near-infrared 15m/pixel; 6 bands shortwave infrared 15m/pixel; 6 bands shortwave infrared 30m/pixel; 5 bands thermal infrared 30m/pixel; 5 bands thermal infrared 90m/pixel.90m/pixel.

In comparison to Landsat:In comparison to Landsat: Expanded wavelength range Expanded wavelength range Improved spectral resolution Improved spectral resolution Higher Spatial resolutionHigher Spatial resolution

Two step image post-processing Two step image post-processing using an object-oriented using an object-oriented classification approachclassification approach

1.1. Derive urban land use/cover classes Derive urban land use/cover classes from fractions of non-vegetation from fractions of non-vegetation cover.cover.

2.2. Derive input parameters.Derive input parameters.1.1. Roof cover from vnir and shape Roof cover from vnir and shape

detection.detection.

2.2. Vegetation cover from vegetation index.Vegetation cover from vegetation index.

3.3. Roads, gravel etc. from swir.Roads, gravel etc. from swir.

4.4. Albedo from bidirectional reflectance.Albedo from bidirectional reflectance.

vegetation 12%roofs 74%

streets, parking 14%

impervious area 88%pervious area 12%

overall albedo 83%




overall albedo 61%




overall albedo 66%

commercial - industrial high residential low residential

Pixel fractions in a 1 km grid

ConclusionsConclusions

Urban land use/cover classification Urban land use/cover classification and associated physical parameters and associated physical parameters important for mesoscale important for mesoscale meteorological modeling.meteorological modeling.

LULC and some of the physical LULC and some of the physical paramters can be efficiently derived paramters can be efficiently derived from satellite imagery.from satellite imagery.

Support from funding agencies Support from funding agencies necessary.necessary.

remote sensing derived land use/cover data for urban modeling in mm5 and wrf susanne grossman-clarke...

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