neil wheeler, kenneth craig, and clinton macdonald sonoma technology, inc. petaluma, california
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Innovative Methods for Evaluating Meteorological Model Performance during the Central California Air Quality Studies. Neil Wheeler, Kenneth Craig, and Clinton MacDonald Sonoma Technology, Inc. Petaluma, California Presented at the - PowerPoint PPT PresentationTRANSCRIPT
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Neil Wheeler, Kenneth Craig, and Clinton MacDonaldSonoma Technology, Inc.
Petaluma, California
Presented at theSixth Annual Community Modeling and Analysis
System (CMAS) ConferenceOctober 1-3, 2007
Chapel Hill, North Carolina
STI-3229
Innovative Methods for Evaluating Meteorological Model
Performance during the Central California Air Quality Studies
2
Introduction
• Prior Measurement, Analysis, and Modeling Studies
• The Question
• The Central California Air Quality Studies (CCAQS)– California Regional PM10/PM2.5 Air Quality
Study (CRPAQS)– Central California Ozone Study (CCOS)
3
The Central Valley of California
4
Central California Air Quality Studies
• Multi-year• Meteorological and Air Quality Monitoring• Quality Assurance and Quality Control• Data Analysis• Emission Inventory Development• Meteorological and Air Quality Modeling
• Back to basics…
5
Meteorological Assessment
• Objective: Assess the readiness of meteorological data and models to drive the air quality simulation models
• Issues investigated: (1) the sufficiency of data precision, accuracy, bias,
consistency, and time-resolution;(2) The adequacy and validity of measurement
methods; (3) the ability of models to represent important
processes and phenomena; and(4) new model evaluation techniques.
6
Model Performance Evaluations
• Typical Operational Evaluations Focus on “important” Parameters
• Statistical
• Graphical – Temporal and Spatial Comparisons; Animations
• Diagnostic and Sensitivity Simulations
7
“Innovative” Methods (1 of 2)
• Data-Based Analysis: Understanding Processes and Phenomena
Community Modeling and Analysis System (CMAS): 1997 – 2007
• Analysis Replication• Derived and Integrated Parameters
– Transport Statistics– Flux Calculations– Trajectories and Tracers
8
“Innovative” Methods (2 of 2)
• Process-Based Analysis
• Assess Meteorology with an AQM
• Assess Processes and Performance between Sources and Receptors but…
• Synthesis – Relate Physical and Chemical Processes– Multi-Parameter Analysis– “Big Picture”
9
ExamplesBased on Important Data Analysis Findings
• Tracer Concentration Distribution• Wildfires and Ozone Aloft• Flux Calculations and Transports Statistics• Plume Rise• Carbon vs. Nitrate Aerosols• Recirculation• Nighttime Nitrate Formation Aloft • Fog and Stratus• Soil Temperature-Air Temperature-Fog-Mixing
Heights
10
Tracer Distribution• CRPAQS: MM5-CAMx with 1 ppm initial concentration• Analysis after 60 hours:
– Surface concentration– Peak tracer concentrations by region– Mass balance
11
High Ozone Day TemperaturesEpisode2, PLR, Surface Gases
0
24
48
72
96
120
144
168
192
216
240
9/15
12:
00 A
M
9/15
12:
00 P
M
9/16
12:
00 A
M
9/16
12:
00 P
M
9/17
12:
00 A
M
9/17
12:
00 P
M
9/18
12:
00 A
M
9/18
12:
00 P
M
9/19
12:
00 A
M
9/19
12:
00 P
M
9/20
12:
00 A
M
9/20
12:
00 P
M
9/21
12:
00 A
M
9/21
12:
00 P
M
9/22
12:
00 A
M
9/22
12:
00 P
M
9/23
12:
00 A
M
Date
O3
, NO
, NO
2 (
pp
b)
0
1
2
3
4
5
6
7
8
9
10
CO
(p
pm
)
O3
NO
NO2
NOY
CO
Episode2, PLR (T), FAT (MH)
0
5
10
15
20
25
30
35
40
45
50
9/15
12:
00 A
M
9/15
12:
00 P
M
9/16
12:
00 A
M
9/16
12:
00 P
M
9/17
12:
00 A
M
9/17
12:
00 P
M
9/18
12:
00 A
M
9/18
12:
00 P
M
9/19
12:
00 A
M
9/19
12:
00 P
M
9/20
12:
00 A
M
9/20
12:
00 P
M
9/21
12:
00 A
M
9/21
12:
00 P
M
9/22
12:
00 A
M
9/22
12:
00 P
M
9/23
12:
00 A
M
Date
De
gre
es (C
)
0
200
400
600
800
1000
1200
1400
1600
1800
2000
Mix
ing
Hei
gh
t (m
)
AMB
MH
12
Maximum Predicted Temperatures
September 19, 2000
13
Air Quality Aloft
14
Aircraft Spirals
37.0N 120.1W 35.9N 19.5W
15
OzonesondesPLR 07/31/2000 0500 PST
0
1000
2000
3000
4000
5000
6000
7000
-20 0 20 40 60 80 100 120 140
Ozone (ppb)
He
igh
t (m
ag
l)
PLR 07/31/2000 1400 PST
0
1000
2000
3000
4000
5000
6000
7000
0 20 40 60 80 100 120 140 160
Ozone (ppb)
He
igh
t (m
ag
l)
16
Ozone AloftO3 Aloft ComparisonTC vs. Observations
y = 0.3951x + 47.744
R2 = 0.4367
0
50
100
150
200
250
0 50 100 150 200 250
Obs
TC
Pre
d
17
Ozone Correlation by Level
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
3 4 5 6 7 8 9 10 11 12 17
CAMx layer
R-s
qu
are
d
0.9 km
1.8 km
7.5 km
0.25 km
18
Ozonesonde – Transport?
Trinidad Head
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
0 20 40 60 80 100
Ozone (ppbV)
He
igh
t (m
) 07/21/00
08/01/00
08/30/00
09/21/00
19
Wild Fire Tracers
16 km
20
Hydrocarbons Aloft
NMHC
0
10
20
30
40
50
60
70
80
90
100
7/30/2000 12:00:00AM
7/30/2000 12:00:00PM
7/31/2000 12:00:00AM
7/31/2000 12:00:00PM
8/1/2000 12:00:00AM
8/1/2000 12:00:00PM
8/2/2000 12:00:00AM
Date-Time
pp
bC
21
Transport Statistics
RWP
MM5
22
Ventilation Index
23
Mixing Depth Growth
bkf
0
100
200
300
400
500
600
700
7/ 30/ 2005 7/ 31/ 2005 8/ 1/ 2005 8/ 2/ 2005 8/ 3/ 2005
tmr
0
100
200
300
400
500
600
700
7/ 30/ 2005 7/ 31/ 2005 8/ 1/ 2005 8/ 2/ 2005 8/ 3/ 2005
24
Vertical Wind Profiles
RWP
CAMx InputMM5
25
Mass Flux Analysis
CAMx Ozone Mass Fluxes - TC Simulation
-1,000,000,000
-800,000,000
-600,000,000
-400,000,000
-200,000,000
0
200,000,000
400,000,000
600,000,000
800,000,000
1,000,000,000
1,200,000,000
70
0
10
00
13
00
16
00
19
00
22
00
10
0
40
0
70
0
10
00
13
00
16
00
19
00
22
00
10
0
40
0
70
0
10
00
13
00
16
00
19
00
22
00
10
0
40
0
70
0
10
00
13
00
16
00
19
00
22
00
10
0
40
0
70
0
10
00
13
00
16
00
19
00
22
00
Ozo
ne
Ma
x F
lux
(m
ole
s/h
r)
-10,000,000,000
-8,000,000,000
-6,000,000,000
-4,000,000,000
-2,000,000,000
0
2,000,000,000
4,000,000,000
6,000,000,000
8,000,000,000
10,000,000,000
12,000,000,000
Ozo
ne
Ma
ss
(mo
les
)
North South East West Top Deposition Chemistry Residual Mass
July 30July 29 July 31 Aug 1 Aug 2
26
Concentration Fluxes
27
Plume Rise Experiments
28
Soil TemperatureSoil Temperatures at Davis
0
2
4
6
8
10
12
De
g. C
CIMIS 15-cm MM5 15-cm MM5 47-cm
Soil Temperatures at Parlier
0
2
4
6
8
10
12
Date
De
g. C
CIMIS 15-cm MM5 15-cm MM5 47-cm
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Extent of FogMM5 tends to overestimate the extent of fog and stratus.
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Summary• Think beyond traditional approaches
• Analysis• Multi-method• Multi-parameter• Phenomena and Processes • Synthesis
• Challenge models to replicate the synthesis• Maybe then the atmosphere will behave as
models predict
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Acknowledgements
The evaluation methods discussed in this paper were developed over the past decade with funding from many agencies. Analyses and evaluations specific to the CCAQS were funded by the San Joaquin Valleywide Air Pollution Study Agency. The statements and conclusions in this paper are those of the authors and not necessarily those of the California Air Resources Board, the San Joaquin Valleywide Air Pollution Study Agency, or its Policy Committee, their employees or their members. The mention of commercial products, their source, or their use in connection with the material reported herein is not to be construed as actual or implied endorsement of such products.
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Parting Thought
Why aren’t meteorological models instrumented with process analysis tools like photochemical grid models?