Use of Satellite Data to Improve the
Physical Atmosphere in Air Quality
Decision Models
AQAST Project
Physical Atmosphere Panel Meeting
April 25-26, 2012
Atlanta, GA
Richard McNider
Arastoo Pour Biazar (or Arastoo McBiazar)
University of Alabama in Huntsville
Physical Atmosphere Advisory Team
Wayne Angevine - NOAA – Boundary Layer Observations
Bright Dornblauser – State of Texas – Regulator Model Evaluation
Mike Ek/Jeff McQueen – NOAA – Land Surface Modeling
Georg Grell – NOAA – Clouds and Modeling
John Nielsen-Gammon – Texas A&M – Model Evaluation
Brian Lamb – Washington State University – Emissions/ Model Evaluation
Pius Lee – NOAA – Air Resources Laboratory – Air Quality Forecasting
Jon Pleim – US EPA – Boundary Layer Modeling
Nelsen Seaman – Penn State University – Meteorological Modeling
Saffett Tanrikulu - SF Bay Area Air Quality District – Meteorological
Modeling
Also had participation from Local and Regional
Air Quality Community in and around Atlanta
Brenda Johnson – EPA Region IV
Richard Monteith – EPA Region IV
Steve Mueller – Tennessee Valley Authority
Justin Walters – Southern Company
Jim Boylan – Georgia Environmental Protection Division
Tao Zeng - Georgia Environmental Protection Division
Lacy Brent – Discovery AQ/U. Maryland
Kiran Alapaty – EPA-NERL
Jim Szykman- EPA- NERL
Ted Russell - Georgia Tech
Talat Odman – Georgia Tech
Maudood Khan – University Space Research Association
Scot t Goodrick – U.S. Forest Service
Physical Atmosphere Can Significantly Impact
Atmospheric Chemistry and Resulting Air Quality
Most Importantly the Physical Atmosphere Can Impact
Control Strategy Efficacy and Response
Temperature, Clouds, Mixing Heights, Humidity and
Turbulence Can All Impact Air Quality
Clouds
Temperature
Satellite
Observation
Mixing Heights
AGENDA
AQAST PHYSICAL ATMOSPHERE MEETING
April 25-26, Atlanta Georgia
April 25
12:30 PM Lunch
2:00 PM Introductions
2:15 PM Background and Charge – Dick McNider
2:45 PM Physical Issues and Shortcomings in Physical Atmosphere
Modeling for SIP or Forecasting (10-15 minute presentations)
General
Nelson Seaman
Saffet Tanrikulu
James Boylan
Scott Goodrick
Wayne Angevine
4:00 Break
4:15
Physical Issues and Shortcomings in Physical
Atmosphere Modeling for SIP or Forecasting (continued)
General
Pius Lee
Bright Dornblauser
Jeff McQueen
Steve Mueller
Lacey Brent (Discovery AQ)
Maudood Khan
Clouds and Photolysis
Arastoo Biazar
Kiran Alapaty
6:00 PM
Recap and Adjourn
6:30 -8:30 PM Reception
April 26
8:00AM -8:30AM Continental Breakfast
8:30 AM Physical Issues and Shortcomings in Physical
Atmosphere Modeling for SIP or Forecasting (continued)
Land Surface –PBL - Emissions
Jon Pleim
John Nielsen-Gammon
Ted Russell
Brian Lamb
9:30 AM Discussion of Use of Satellite Information to Improve
the Physical Atmosphere
Overview – Dick McNider
Land Surface – Jon Pleim, Jeff
McQueen, Maudood Khan
Clouds and Photolysis– Arastoo
Biazar, Kiran Alapaty, Saffet
Tanrikulu
Winds – Bill Murphrey/ Dick
McNider/Seaman
10:30 AM Break
10:45 AM Discussion of Use of Satellite Information to Improve
the Physical Atmosphere
General ( Participation by all)
12:00 NOON
Lunch
1:00 PM
Selection of Priorities – Lead (Dick McNider)
Participation by All
2:00 PM Formation of Application Paths and Team Formation
3:00 PM Recap and Adjourn
The presentations by both members of the panel and
by local participants brought up a wide variety of
topics
1.
2.
3.
4.
5.
6.
7.
8.
Coastal clouds in California
Nighttime Mixing in Houston and Atlanta
Winds for forest fire smoke transport in Georgia
Snow cover in Spring in West (photolysis and land
surface energetics)
Tropospheric/Stratospheric exchange for
background ozone in the Pacific Northwest
Topographic effects on 8 hour standards
Urban/Rural bias in NO2 which may be related to
physical atmosphere in Mid-Atlantic
Representativeness of SIP Meteorology in Georgia
Categorization Summary
Clouds for Photolysis, Vertical Mixing and Aqueous Chemistry ‐ Angevine,
Tanrikulu, Biazar, Alapaty, Boylan
Stable Boundary Layer for Vertical Mixing, Winds, Cold Pooling - Seaman,
Boylan, Lee, Russell, Lamb
Land Surface for Fluxes and Deposition - Angevine, DornBluaser, Pleim,
Tanrikulu, Lee
Winds for Transport and Dilution - Dornblaser, Lee, Odman
Mixing Heights for Dilution and Plume Rise - McQueen, Goodrick
Topography – Seaman, Mueller, Lamb
Snow Cover for Land Surface and Photolysis ‐ Tanrikulu
Tropospheric/Stratospheric Exchange for Ozone Background - Lamb, Biazar
Potential For Use of Satellite Data For Improvement and/or
Verification
Clouds for Photolysis, Vertical Mixing and Aqueous Chemistry ‐ Angevine, Tanrikulu,
Biazar, Alapaty, Boylan - VERY HIGH
Stable Boundary Layer for Vertical Mixing, Winds, Cold Pooling - Seaman, Boylan, Lee,
Russell, Lamb - MODERATE
Land Surface for Fluxes and Deposition - Angevine, DornBluaser, Pleim, Tanrikulu, Lee HIGH
Winds for Transport and Dilution - Dornblauser, Lee, Odman – MODERATE
Mixing Heights for Dilution and Plume Rise - McQueen, Goodrick – LOW/MODERATE
Topography – Seaman, Mueller, Lamb - LOW
Snow Cover for Land Surface and Photolysis ‐ Tanrikulu VERY HIGH
Tropospheric/Stratospheric Exchange for Ozone Background - Lamb, Biazar –
MODERATE/HIGH
Based on Importance to Physical Atmosphere and Potential for
Use of Satellite Data Selected Three Major Themes
1. Clouds
2. Stable Boundary Layer
3. Land Surface
Stable Nocturnal Boundary Layer
James Boylan
Time series
O3
Mon
O3
Tue
Wed
Thur
Fri
Sat
Sun
Russell-Odman
Ozone in Houston
Original Kzz
0.08
0.06
8-h Ozone Concentration (ppm)
9525 1/2 Clinton Dr
Measured
Simulated
0.04
0.02
0
12
13
14
15
16
17
18
19
20
21
22
23
Date (July 2006 CDT)
Modified Kzz
0.08
9525 1/2 Clinton Dr
Measured
0.06
Simulated
0.04
0.02
0
12
13
14
15
16
17
18
19
20
Date (July 2006 CDT)
Driven by reanalysis of nocturnal boundary layer mixing
21
22
23
Texas – Dornblaser – Lee
Stable regime
Model deficiency: mismatch in night time decoupling?
regional average surface wind speed for
period June 4 – 12 (dark color)
and June 26 – July 3 (light color).
Frequent surface wind-speed high-bias
Night time high wind-speed bias
Occurred repeatedly for many days
right after sunset
Similarity theory for surface layer;
e.g. Ulrike Pechinger et al. COST 710, 1997
AQAST Physical Atmosphere Meeting, April 25-26, Atlanta GA
16
Russell-Odman
1-h Ozone Concentration
Original Kzz
Modified Kzz
Ramifications
Ted Russell
• Significantly changes model performance
– Less effect on peak ozone
• Still non-zero
– Major effect on primary/pseudo-primary species
concentrations
• EC, CO, NO2, PM2.5
– New standards raise importance of NO2.
– Use of models in health effects research raise importance of
bias, diurnal variation
Brian
Lamb
Cold pool modeling
from Avey, Utah DEQ)
Routine application of prognostic meteorological models including the Fifth-Generation
NCAR/Penn State Mesoscale Model (MM5) and Weather Research and Forecasting Model (WRF)
with a variety of different physics options, initialization input, vertical and horizontal resolutions,
and nudging approaches have failed to replicate the degree and persistence of stagnant
meteorological conditions. (Baker et al., 2011, ES&T).
AIRPACT Forecasts don’t
capture elevated
wintertime PM2.5 levels
• stagnant valley
meteorology
•woodstove emissions
Sub-Km Modeling of the Stable Boundary Layer
Combined modeling and observation studies Nittany
Valley, Central PA
WRF smallest domain (0.444 km horizontal resolution)
Observation Network
10 km scale
20
Sub-Km Modeling of the Stable Boundary Layer
Releases at one-hour intervals from Site 9 at 5 m AGL
a) 0500-0700 UTC
b) 0600-0800 UTC
c) 0700-0900 UTC
d) 0500-0700 UTC
e) 0600-0800 UTC
f) 0700-0900 UTC
21
Path Forward
Explore mixing formulations for stable boundary layer and role of
resolution with MODIS skin temperatures as evaluation metric.
1.2
Fh(Ri)
1
0.8
Coarse grid
models
0.6
England-McNider
Duynkerke
Beljaars-Holtslag
Louis
0.4
0.2
0
0
Theory
0.2
0.4
Ri
0.6
Use MODIS Skin
Temperatures for
Model Evaluation
GOES Derived Skin Temperature
MODIS Derived Skin Temperature
Steve Mueller
Nocturnal boundary layer formation dependent on topography has
implications for 8 hour attainment at high elevations.
Wayne Angevine
Cloud Mixing
Changes Effective
PBL Height
CO profiles from P3
CO profiles from P3
upwind, over, and
upwind, over, and
downwind of Nashville
downwind of Nashville
(symbols)
(symbols)
Tracer profile from 1D
Tracer profile from 1D
cloud-aware PBL model
cloud-aware PBL model
(early version of TEMF)
(early version of TEMF)
Lower panel shows what
Lower panel shows what
happens when cloudhappens when cloudinduced mixing is not
induced mixing is not
present
present
Kiran Alapaty
Surface
Insolation
Diff:
10X10 cells
Over RDU
(KFC-BASE)
W/m^2
Southeast Land cells
Tests in Texas showed changes in cloud locations and
radiative properties can change ozone by 70ppb
Too Many Options Not Enough Information on Performance!
Kiran Alapaty
It Rains Cats & Dogs in a Clear Sky!!!
(for convective clouds in WRF)
Radiative effects were not included for WRF subgrid scale
clouds.
Inconsistency in Cloud Handling in Models
1. MM5/WRF do not consider sub-grid clouds in
radiation calculations.
2. Clouds in MM5/WRF not used in CMAQ
(clouds rediagnosed) for wet chemistry
mixing.
3. CMAQ photolysis rates not based on CMAQ
clouds but on MM5/WRF liquid water
profiles.
These inconsistencies make correction difficult!
Satellite data can be used as a metric to test model
cloud agreement
Path Forward
1. Insert satellite measures of radiative properties directly in models.
Use satellite derived measures of insolation based on satellite clouds
rather than modeled insolation using model clouds (McNider et al.
1995)
Use satellite cloud transmittance in photolysis calculations (Biazar et al.
2007)
2. Improve physical parameterizations using satellite data as
performance metric
Correct model radiation (Alapaty et al. 2012)
Connect PBL and cloud schemes (Angevine 2012)
3. Assimilate satellite data to improve the location and timing of cloud
Provide dynamical cloud support and cloud clearing (McNider and Biazar
2012)
Land Surface
Factors controlling surface temperatures are complex
and many models have created complex land use
models that in the end require many ill defined
parameters.
Land surface
Top-level soil temperature and
moisture
BLLAST, 30 June 2011, 14Z
Air Quality Simulations for SIPs Are Retrospective Studies
Allows use of observations to constrain forecast models
Simple Surface Models Constrained by Observations
1.
Pleim Xiu Scheme
2.
McNider et al. 1994 / Norman et al. 1995 (ALEXI)
Pleim-Xiu - Land surface energy budget
Ts
2
Ts - T2 
 CT Rn - H - LE  t


qs (Tsat ) - q1 
E g   a 1 - f veg  p ( wg )
Ra  Rbw

qs (Tsat ) - q1 
Er   a f veg
Ra  Rbw

qs (Tsat ) - q1 
Etr   a f veg 1 -  
Soil moisture
Ra  Rbw  R stb
Rst min LAI
Rstb 
F1 ( PAR) F2 ( w2 ) F3 ( RH s ) F2 (Ta )
Soil Moisture Nudging
wg
a
f
a
f
 1 T - T   2 RH - RH 
t
w2
a
f
a
 1 T - T    2 RH - RH
t
f

Nudge according to model bias in 2-m T and RH compared
to surface air analysis
T-2m bias
relative to
analysis for
January 2006
T2
 N T 2 T2m - Tobs 
dt
Qv-2m bias
relative to
analysis for
January 2006
Mean bias for 2m T – August 2006
12km domain:
Most around -1 to +1
Positive bias: N and W
regions
Negative bias: S, E
1km:
Most Negative within 0.5
Negative bias: high
along the coast
4km:
Most around: -0.5 to
+0.5
Negative bias: high
along the coast
McNider et al. 1995
Surface Energy Budget
 dTG  1
 dt   C (RN  H  G  E )


b
Bulk Heat Capacity
Short-wave radiation
obtained from Satellite
Evaporative Heat
Flux
 dT 

 dT 
ESatellite  Cb  G 
- G 
  Em
 dt  Satellite  dt  model 
 dTG 

 dTG 
Cb  
/




dt
dt




Satellite
model 

Morning
Evening
Satellite Data Can Provide
Many More Opportunities for
Data Skin Temperature
Assimilation (GOES ~5 km
and MODIS ~1 km).
Land characteristics especially
in Eastern U.S. fine scale
variations.
Assimilation
Satellite
Observation
Control
Model BL Heights (CNTRL)
Model BL Heights (ASSIMALATED)
Aug. 26, 2000, 19:00-21:00 GMT averaged
Aug. 26, 2000, 19:00-21:00 GMT averaged
Path Forward
1. Use satellite skin temperatures in Pleim –Xiu
scheme rather than National Weather Service
2 m temperatures
2. Test McNider et al. scheme using new
corrections (use of model skin temperatures
and aerodynamic temperatures) suggested by
Mackaro
Pleim-Xiu - Land surface energy budget
Use satellite derived albedo
and insolation
Ts
2
Ts - T2 
 CT Rn - H - LE  t


qs (Tsat ) - q1 
E g   a 1 - f veg  p ( wg )
Ra  Rbw

qs (Tsat ) - q1 
Er   a f veg
Ra  Rbw

qs (Tsat ) - q1 
Etr   a f veg 1 -  
Soil moisture
Ra  Rbw  R stb
Rst min LAI
Rstb 
F1 ( PAR) F2 ( w2 ) F3 ( RH s ) F2 (Ta )
Soil Moisture Nudging
Use satellite skin temperatures rather
than NWS temperatures
wg
a
f
a
f
 1 T - T   2 RH - RH 
t
w2
a
f
a
 1 T - T    2 RH - RH
t
f

Nudge according to model bias in 2-m T and RH compared
to surface air analysis
Teams are being formed for priority areas
1. Clouds ( Pour-Biazar,Alapaty,Nielsen –Gammon)
2. Stable Boundary Layer (McNider, Angevine,
Russell,Lee)
3. Land Surface – (Pleim, Angevine, Tanrikulu,
McQueen/Ek)
Next Meeting (12-18 mos) will be on West Coast