Transcript NASA Air Quality Applied Sciences Team (AQAST)

The NASA Air Quality Applied Sciences Team (AQAST):
Exploitation of Aura data
Daniel J. Jacob, Harvard University
AQAST Leader
http://acmg.seas.harvard.edu/aqast
Air Quality Applied Sciences Team (AQAST)
EARTH SCIENCE SERVING AIR QUALITY MANAGEMENT NEEDS
Earth observing system
satellites
suborbital platforms
AQAST
models
AQAST
Air Quality Management Needs
• Pollution monitoring
• Exposure assessment
• AQ forecasting
• Source attribution of events
• Quantifying emissions
• Natural&foreign influences
• AQ processes
• Climate-AQ interactions
AQAST members
• Daniel Jacob (leader), Loretta Mickley (Harvard)
• Greg Carmichael (U. Iowa)
• Dan Cohan (Rice U.)
• Russ Dickerson (U. Maryland)
• Bryan Duncan, Yasuko Yoshida, Melanie Follette-Cook (NASA/GSFC); Jennifer
Olson (NASA/LaRC)
• David Edwards (NCAR)
• Arlene Fiore (NOAA/GFDL); Meiyun Lin (Princeton)
• Jack Fishman, Ben de Foy (Saint Louis U.)
• Daven Henze, Jana Milford (U. Colorado)
• Tracey Holloway, Steve Ackerman (U. Wisconsin); Bart Sponseller (Wisconsin DRC)
• Edward Hyer, Jeff Reid, Doug Westphal, Kim Richardson (NRL)
• Pius Lee, Tianfeng Chai (NOAA/NESDIS)
• Yang Liu, Matthew Strickland (Emory U.), Bin Yu (UC Berkeley)
• Richard McNider, Arastoo Biazar (U. Alabama – Huntsville)
• Brad Pierce (NOAA/NESDIS)
• Ted Russell, Yongtao Hu, Talat Odman (Georgia Tech); Lorraine Remer
(NASA/GSFC)
• David Streets (Argonne)
• Jim Szykman (EPA/ORD/NERL)
• Anne Thompson, William Ryan, Suellen Haupt (Penn State U.)
AQAST organization
• AQAST members are appointed for five years (starting May 2011)
• They carry out Investigator Projects (IPs) with core funding, and Tiger Team
Projects (TTPs) competed on annual basis to address urgent air quality
management needs.
• All AQAST projects involve partnerships with air quality managers and have
deliverable air quality management outcomes
• Biannual AQAST meetings provide forums for dialogue with air quality
managers: NCAR (May 2011), EPA (Nov 2011), U. Wisconsin (Jun 2012),
Cal/EPA (Nov 2012)
• We also have AQAST workshops, AQAST representation at air quality
meetings, four special sessions and Town Hall at the Fall 2012 AGU…
Scope of current AQAST projects (IPs and TTPs)
Partner agency
• Local: RAQC, BAAQD
• State: TCEQ, MDE,
Wisconsin DNR, CARB,
Iowa DNR, GAEPD, GFC
• Regional: LADCO, EPA
Region 8
• National: EPA, NOAA,
NPS
Theme
Satellites: MODIS, MISR, MOPITT, AIRS, OMI, TES, GOES, GOME-2
Suborbital: ARCTAS, DISCOVER-AQ, ozonesondes, PANDORA
Models: MOZART, CAM AM-3, GEOS-Chem, RAQMS, STEM, GISS, IPCC
Earth Science resource
AQAST decision support for the Iowa Landfill Fire of 2012
Uncontrolled landfill liner fire
within 5 miles of >150K people
•
7.5 acres burned, May-June 2012
•
1.3 million shredded tires
•
Irritants + mutagens + SO2 +
80 µg/m3 PM2.5
5-
AQAST Nowcasting tool helped
policymakers decide public health
response & favorable conditions for
fire intervention
WRF-AERMOD LandfillPM2.5 forecast
0.1
Maximum 8 hour average concentration (µg/m3)
Midnight Saturday - noon Monday
0.2
0.5
1
•
WRF-Chem + GSI 3DVAR
72hr forecast assimilating MODIS
data.
+ AERMOD @ 100 m
+ emissions factors from mobile
monitoring by 3 groups
= New decision support toolkit for
rapid public health response to
urban toxic releases
AQAST PIs: Carmichael, Spak
2
5
10
20
30
50+
4TH highest NA background
ozone (GEOS-Chem)
Annual maximum stratospheric
influence (AM-3)
Background forecasting using
AIRS CO over Pacific
AQAST estimates of US background
ozone for EPA revision of NAAQS
• Peer-reviewed global model statistics for
US, North American, and natural
background ozone from AQAST form the
basis for the EPA Integrated Science
Assessment (ISA) and Risk and Exposure
Assessment (REA) in the current NAAQS
revision
• Estimates from two independent models
(GEOS-Chem and AM-3) provide a first
error characterization on background
estimates
• Satellite observations are being used for
background forecasting, model evaluation
Zhang et al. [2011], Lin et al. [2012ab]
AQAST PIs: Fiore, Jacob
TES & OMI data test model estimates of US ozone background
TES and OMI 500 hPa ozone for spring 2006 compared to AM-3 and GEOS-Chem
Bias vs. N midlatitude sondes
subtracted from
retrievals
• AM-3 tends to be high, GEOS-Chem low compared to TES & OMI
• Suggests that AM-3 and GEOS-Chem bracket true background
L. Zhang (Harvard), A. Fiore (Columbia)
Using TES instantaneous ozone radiative forcing data
to quantify radiative forcing efficiency of emission controls
TES ozone radiative forcing per ppb
Aug 2006, land, daytime
GEOS-Chem
model adjoint
Relative efficacy
of NOx emissions controls
on ozone radiative forcing
Worden et al., (2008; 2011), Aghedo et al., 2011
NOx emission controls are most beneficial
at low latitudes
Bowman and Henze, submitted
AQAST PI: Henze
Using OMI formaldehyde to improve isoprene emission inventories
for State Implementation Plan (SIP) modeling and AQ forecasts
HCHO column,
Jun-Aug 2005
Correlation of monthly mean HCHO with air T
NE Texas, JJA 2005-2008
Exponential fit
MEGAN inventory
2006
2007
285
290
295
300 K
Daily data in Southeast US binned by air temperature
turnover
at 310 K
2008
1
5
10
15
1015 molecules cm-2
290
Lei Zhu, Harvard
295
300
305
310 K
AQAST PIs: Mickley, Cohan
Observation of SO2 point sources in US by OMI oversampling
SO2 point sources, 2004-2007
3 km-resolution data enables
analysis of SO2 emission trends,
SO2 atmospheric lifetime
OMI SO2 (3 km oversampling)
AQAST PI: De Foy
Google map overlay
May-Aug 2009-2011
Los Angeles
May-Aug 2005-2007
Mapping trends of urban NO2 using OMI with oversampling
Y. Yoshida and B. Duncan (NASA/GSFC)
AQAST PI: Duncan
Using OMI observations
to monitor growth in emissions from Canadian oil sands
Oil sand recovery
In Alberta
OMI NO2 columns, 2004-2010
NO2 increase of 10.4 ±3.5% per year
McLinden et al. [GRL 2012]
AQAST PI: DIckerson
Using OMI observations
to monitor NOx emission growth in China and India
2007/2005 ratio
of OMI NO2 tropospheric columns:
Circles are new power plants
[Streets et al., 2012]
1996-2010 trend
of OMI NO2 tropospheric columns
over Indian power plants regions:
70% increase is consistent with
bottom-up emission inventory
[Lu and Streets, 2012]
AQAST PI: Streets
Using OMI NO2 to improve NOx mobile source emissions
and surface NO2 monitoring
trucks
rail
trucks
2007 annual AQS NO2
E. Bickford and T. Holloway
• OMI NO2 constrains poorly
constrained emissions from freight
• OMI enables national NO2 monitoring
beyond the limited EPA AQS network
OMI correlation with AQS
AQAST PI: Holloway
Using OMI NO2 to constrain NOx emissions
for State Implementation Planning (SIP) in Texas
Optimization of NOx emissions by Kalman filter using OMI NO2 together with
aircraft (TEXAQS-II) and surface AQS data
OMI NO2, June 2006
CAMx NO2,
base emissions
CAMx NO2,
optimized emissions
AQAST PI: Cohan
OMI and aircraft NO2 observations during DISCOVER-AQ
show that model NOx lifetimes are too short
OMI
OMI
Implies model errors in NOx
chemistry, underestimate of
ozone production efficiency
downwind
from Baltimore
upwind
Canty, Brent, Dickerson,
et al. (in prep.)
AQAST PI: DIckerson
AQAST progress toward an OMI AQ management toolkit:
AQ managers can now…
1. Easily obtain useful data in familiar formats
Custom OMI NO2 “Level 3” products on any grid in netCDF
with WHIPS (Holloway)
Annual NO2 shapefiles - OMI & CMAQ on CMAQ grids
(AQAST Tiger Team)
Google Earth
2. Find easy-to-use guidance & example scripts for
understanding OMI products and comparing to
simulated troposphere & PBL concentrations
One-stop user portal (Holloway & AQAST Tiger Team)
OMI NO2 KML in SARP
flight planning
OMI NO2 & SO2 guidance, field campaign example case
studies (Spak & AQAST Tiger Team)
3. Obtain OMI observational operators for assimilation &
emissions inversion in CMAQ
•NO2 in GEOS-Chem  CMAQ (Henze, Pye)
•SO2 in STEM  CMAQ (Spak, Kim)
•O3 in STEM  CMAQ (Huang, Carmichael, Kim)
AQAST PIs: Carmichael, Spak
AQAST lenticular produced by Bryan Duncan (ask for it!!)