use of satellite data to improve the physical atmosphere in air quality decision models
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
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 McNiderArastoo 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 IVRichard Monteith – EPA Region IVSteve Mueller – Tennessee Valley AuthorityJustin Walters – Southern CompanyJim Boylan – Georgia Environmental Protection DivisionTao Zeng - Georgia Environmental Protection DivisionLacy Brent – Discovery AQ/U. MarylandKiran Alapaty – EPA-NERLJim Szykman- EPA- NERLTed Russell - Georgia Tech Talat Odman – Georgia TechMaudood Khan – University Space Research AssociationScot 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
SatelliteObservation
Temperature Mixing HeightsClouds
AGENDAAQAST 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 SeamanSaffet TanrikuluJames BoylanScott Goodrick
Wayne Angevine
4:00 Break
4:15 Physical Issues and Shortcomings in Physical Atmosphere Modeling for SIP or Forecasting (continued)General
Pius LeeBright Dornblauser Jeff McQueenSteve MuellerLacey Brent (Discovery AQ)Maudood Khan
Clouds and PhotolysisArastoo BiazarKiran 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 PleimJohn Nielsen-GammonTed 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 KhanClouds and Photolysis– Arastoo Biazar, Kiran Alapaty, Saffet TanrikuluWinds – 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. Coastal clouds in California2. Nighttime Mixing in Houston and Atlanta3. Winds for forest fire smoke transport in Georgia4. Snow cover in Spring in West (photolysis and land
surface energetics)5. Tropospheric/Stratospheric exchange for
background ozone in the Pacific Northwest6. Topographic effects on 8 hour standards7. Urban/Rural bias in NO2 which may be related to
physical atmosphere in Mid-Atlantic8. 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
Time seriesO3
O3
Mon Tue Wed Thur Fri Sat Sun
James Boylan
Ozone in HoustonOriginal Kzz
8-h
Ozo
ne C
once
ntra
tion
(ppm
)
0
0.02
0.04
0.06
0.08
12 13 14 15 16 17 18 19 20 21 22 23
MeasuredSimulated
9525 1/2 Clinton Dr
Date (July 2006 CDT)
0
0.02
0.04
0.06
0.08
12 13 14 15 16 17 18 19 20 21 22 23
MeasuredSimulated
Date (July 2006 CDT)
9525 1/2 Clinton Dr
Modified Kzz
Driven by reanalysis of nocturnal boundary layer mixing
Russell-Odman
AQAST Physical Atmosphere Meeting, April 25-26, Atlanta GA 16
Model deficiency: mismatch in night time decoupling?
Stable regime
regional average surface wind speed for period June 4 – 12 (dark color) and June 26 – July 3 (light color).
Night time high wind-speed bias Occurred repeatedly for many daysright after sunset
Frequent surface wind-speed high-bias
Similarity theory for surface layer; e.g. Ulrike Pechinger et al. COST 710, 1997
Texas – Dornblaser – Lee
1-h Ozone Concentration
Original Kzz Modified Kzz
Russell-Odman
Ramifications
• 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
Ted Russell
Cold pool modeling
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
from Avey, Utah DEQ)
Brian Lamb
20
Sub-Km Modeling of the Stable Boundary Layer
Combined modeling and observation studies Nittany Valley, Central PA
10 km scale
WRF smallest domain (0.444 km horizontal resolution) Observation Network
21
a) 0500-0700 UTC b) 0600-0800 UTC c) 0700-0900 UTC
e) 0600-0800 UTC f) 0700-0900 UTCd) 0500-0700 UTC
Releases at one-hour intervals from Site 9 at 5 m AGL
Sub-Km Modeling of the Stable Boundary Layer
Tu
ssey R
idge
Path Forward
Explore mixing formulations for stable boundary layer and role of resolution with MODIS skin temperatures as evaluation metric.
0 0.1 0.2 0.3 0.4 0.5 0.60
0.2
0.4
0.6
0.8
1
1.2
England-McNiderDuynkerkeBeljaars-HoltslagLouis
F h(Ri)
Ri
Coarse grid models
Theory
Use MODIS Skin Temperatures for Model Evaluation
GOES Derived Skin Temperature MODIS Derived Skin Temperature
Nocturnal boundary layer formation dependent on topography has implications for 8 hour attainment at high elevations.
Steve Mueller
CO profiles from P3 upwind, over, and downwind of Nashville (symbols)
Tracer profile from 1D cloud-aware PBL model (early version of TEMF)
Lower panel shows what happens when cloud-induced mixing is not present
CO profiles from P3 upwind, over, and downwind of Nashville (symbols)
Tracer profile from 1D cloud-aware PBL model (early version of TEMF)
Lower panel shows what happens when cloud-induced mixing is not present
Wayne Angevine
Cloud Mixing Changes Effective PBL Height
Southeast Land cells
10X10 cells Over RDU
Surface InsolationDiff: (KFC-BASE)W/m^2
Kiran Alapaty
Tests in Texas showed changes in cloud locations and radiative properties can change ozone by 70ppb
Too Many Options Not Enough Information on Performance!
It Rains Cats & Dogs in a Clear Sky!!!(for convective clouds in WRF)
Radiative effects were not included for WRF subgrid scale clouds.
Kiran Alapaty
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 cloudProvide 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 surfaceTop-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)
)()()()( 23221
min
as
ststb TFRHFwFPARF
LAIRR
Pleim-Xiu - Land surface energy budget
22 TTLEHRC
tT
snTs ----
bwa
satsgpvegag RR
qTqwfE
-- 1)()(1
bwa
satsvegar RR
qTqfE
- 1)(
stbbwa
satsvegatr RRR
qTqfE-
- 1)(1 Soil moisture
Soil Moisture Nudging
fafag RHRHTTt
w-- 21
fafa RHRHTTt
w-- 21
2
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
Qv-2m bias relative to analysis for January 2006
obsmT TTNdtT
-
222
Mean bias for 2m T – August 2006
12km domain: Most around -1 to +1Positive bias: N and W regionsNegative bias: S, E
4km:Most around: -0.5 to +0.5Negative bias: high along the coast
1km:Most Negative within -0.5Negative bias: high along the coast
1( )GN
b
dT R HG Edt C
McNider et al. 1995 Surface Energy Budget
Bulk Heat Capacity Evaporative Heat FluxShort-wave radiation
obtained from Satellite
mG
Satellite
GbSatellite E
dtdT
dtdTCE
-
model
model
/dt
dTdt
dTC G
Satellite
Gb
Morning
Evening
SatelliteObservation
Assimilation Control
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.
Model BL Heights (CNTRL)
Aug. 26, 2000, 19:00-21:00 GMT averaged
Model BL Heights (ASSIMALATED)
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
)()()()( 23221
min
as
ststb TFRHFwFPARF
LAIRR
Pleim-Xiu - Land surface energy budget
22 TTLEHRC
tT
snTs ----
bwa
satsgpvegag RR
qTqwfE
-- 1)()(1
bwa
satsvegar RR
qTqfE
- 1)(
stbbwa
satsvegatr RRR
qTqfE-
- 1)(1 Soil moisture
Use satellite derived albedo and insolation
Soil Moisture Nudging
fafag RHRHTTt
w-- 21
fafa RHRHTTt
w-- 21
2
Nudge according to model bias in 2-m T and RH compared to surface air analysis
Use satellite skin temperatures rather than NWS temperatures
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