simulating tropical meteorology for air quality studies
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Simulating tropical meteorology
for air quality studies
Andrew Wiebe, Ella Castillo, Tania Haigh, Adam Thomas and
Anthony Parkinson September 10, 2013
Outline
• Motivation/objective
• Intro to TAPM and WRF – ease of use, features, flexibility
• Model setup
• Model evaluation
– surface data
– upper air data
• Conclusions
Motivation
• Models for generating meteorological data for
air quality assessments include
– WRF
– TAPM
• Potential industrial development in tropical
regions
Objective
• In-depth evaluation of the potential complex
model-generated meteorological datasets in
these regions
– Surface and upper level data
– Ability to capture meteorological features that may be important
for dispersion in tropical regions
TAPM and WRF features
TAPM WRF
Development CSIRO, 2008
NCAR, NOAA, FSL, AFWA,
FAA
Open source contributions
Physics options Default, few options No default, a number of
options
User interface User-friendly GUI Limited user interface, mostly
command line operated
Customisability Geographical features
Geographical features
Microphysics
Cloud parametrisation
Boundary layer
Radiation schemes
etc
Data assimilation Surface wind speed, direction
Surface wind speed, direction
Temperature, relative
humidity, rainfall, upper air
data
Study location
Weipa, QLD
Study location: Weipa, QLD
• Tropical
• Coastal
• Less populated area
• Some industrial activity
• Sufficient surface data
• Sufficient upper air data
Weipa, QLD
Model setup
• 5 days (11 – 14th Feb, 2011)
• Modelled using – TAPM v4.0.5
– WRF v3.4
• YSU
• AMC2
• QNSE
• Similar model setups where possible
• Best practice for each model setup
• No data assimilation
• Refined landuse for all model runs
Study period
Study period
Study period
Model evaluation- surface data
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11/02/2011 12/02/2011 13/02/2011 14/02/2011
Diu
rna
l C
yc
le I
nte
nsit
y (°C
)
Observed YSU ACM2 QNSE TAPM
Model evaluation- surface data
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34.0
Te
mp
era
ture
(°C
)
Observed YSU ACM2 QNSE TAPM
Model evaluation -surface data
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25%
30%
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10
Win
d S
pee
d (
m/s
)
Observed-WS YSU-WS ACM2-WS QNSE-WS TAPM-WS
Model evaluation- surface data
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Win
d D
irec
tio
n (°)
Observed YSU ACM2 QNSE TAPM
Model evaluation- surface data
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Bo
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ry L
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YSU-PBL ACM2-PBL QNSE-PBL TAPM-PBL
Model evaluation- surface data
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201102102300 201102112300 201102122300 201102132300 201102142300
Dail
y A
cc
um
ula
ted
Rain
fall
(m
m)
Observed YSU ACM2 QNSE TAPM
Model evaluation– upper air
11/2/2011 9pm
local time
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5 10 15 20 25 30 35
Tc
Td
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0 2 4 6 8 10 12 14
WS
WS
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0 50 100 150 200 250 300 350
WDIR
WDIR
Observations:
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5 10 15 20 25 30 35
TEMP(C)
DewPt
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0 2 4 6 8 10 12 14
WSPD(m/s)
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0 50100150200250300350
WDIR(deg)
TAPM
Model evaluation– upper air
11/2/2011 9pm
local time
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200
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5 10 15 20 25 30 35
Tc
Td
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0 2 4 6 8 10 12 14
WS
WS
0
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1600
0 50 100 150 200 250 300 350
WDIR
WDIR
Observations:
WRF - YSU
0
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800
1000
1200
1400
1600
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0 15 30
TC
TD
0
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600
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0 2 4 6 8 10 12 14
WSPD
0
200
400
600
800
1000
1200
1400
1600
1800
0 50 100 150 200 250 300 350
WDIR
Model evaluation – upper air
11/2/2011 9pm
local time
0
200
400
600
800
1000
1200
1400
1600
5 10 15 20 25 30 35
Tc
Td
0
200
400
600
800
1000
1200
1400
1600
0 2 4 6 8 10 12 14
WS
WS
0
200
400
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800
1000
1200
1400
1600
0 50 100 150 200 250 300 350
WDIR
WDIR
Observations:
WRF – ACM2
0
200
400
600
800
1000
1200
1400
1600
1800
0 15 30
TC
TD
0
200
400
600
800
1000
1200
1400
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1800
0 2 4 6 8 10 12 14 16 18
WSPD
0
200
400
600
800
1000
1200
1400
1600
1800
0 50 100 150 200 250 300 350
WDIR
Model evaluation – upper air
11/2/2011 9pm
local time
0
200
400
600
800
1000
1200
1400
1600
5 10 15 20 25 30 35
Tc
Td
0
200
400
600
800
1000
1200
1400
1600
0 2 4 6 8 10 12 14
WS
WS
0
200
400
600
800
1000
1200
1400
1600
0 50 100 150 200 250 300 350
WDIR
WDIR
Observations:
WRF – QNSE
0
200
400
600
800
1000
1200
1400
1600
1800
0 15 30
TC
TD
0
200
400
600
800
1000
1200
1400
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1800
0 2 4 6 8 10 12 14 16 18
WSPD
0
200
400
600
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1200
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0 50 100 150 200 250 300 350
WDIR
Summary/conclusions
• All models captured some parameters well
• WRF captured dry air moving in from north-east, and rainfall on 12th. TAPM did not.
• Not enough analysis to determine if any one model is ‘better’
• Identifying the ‘best’ model and setup depends on understanding where/why you are using it, e.g. are surface or upper air winds more important?
• In this study:
– Typically has SE to NE winds
– Occasional northerlies could blow pollution towards residences
– Should select a model that captures these winds well
Simulating tropical meteorology
for air quality studies
Andrew Wiebe, Ella Castillo, Tania Haigh, Adam Thomas and
Anthony Parkinson September 10, 2013
Model setup - TAPM
• TAPM v4.0.5 was configured as follows: – Four nests with grid resolutions of 30, 10, 3, and 1 km.
– All nests centred at a latitude of -12 ° 41' and a longitude of 141
° 55'.
– 40 x 40 grids for all nests.
– 25 vertical levels, set at the default heights.
– Geoscience Australia 9-second digital elevation model (DEM)
terrain data supplied with TAPM was used.
– Synoptic data provided with TAPM were used to initialise.
– No data assimilation was used
– Coastline delineation and land-use refinement using aerial
imagery
Model setup - WRF
• WRF V3.4 using the ARW dynamical core was
configured as follows: – Three nests with resolutions of 25, 5, and 1 km.
– All nests centred at a latitude of -12.667° and a longitude of
141.917°
– 41 x 41 grids for all nests
– 20-category MODIS-based land use data used with the NOAA
land surface model option
– Input GRIB meteorological reanalysis data “NCEP FNL
Operational Model Global Tropospheric Analyses” ds083.2
dataset (NOAA, 2000) was used to initialise WRF
– Same physics options for all domains
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