Jim Crawford1, Ken Pickering2, Lok Lamsal2, Bruce Anderson1, Andreas Beyersdorf1, Gao Chen1, Richard Clark3, Ron Cohen4, Glenn Diskin1, Rich Ferrare1, Alan Fried5, Brent Holben2, Jay Herman6, Ray Hoff6, Chris Hostetler1, - PowerPoint PPT Presentation

Title: Jim Crawford1, Ken Pickering2, Lok Lamsal2, Bruce Anderson1, Andreas Beyersdorf1, Gao Chen1, Richard Clark3, Ron Cohen4, Glenn Diskin1, Rich Ferrare1, Alan Fried5, Brent Holben2, Jay Herman6, Ray Hoff6, Chris Hostetler1,


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Challenges and opportunities for remote sensing
of air quality Insights from DISCOVER-AQ
Jim Crawford1, Ken Pickering2, Lok Lamsal2, Bruce
Anderson1, Andreas Beyersdorf1, Gao Chen1,
Richard Clark3, Ron Cohen4, Glenn Diskin1, Rich
Ferrare1, Alan Fried5, Brent Holben2, Jay
Herman6, Ray Hoff6, Chris Hostetler1, Scott
Janz2 , Mary Kleb1, Jim Szykman7, Anne Thompson2,
Andy Weinheimer8, Armin Wisthaler9, Melissa
Yang1, Jay Al-Saadi1 1 NASA Langley Research
Center, 2 NASA Goddard Space Flight Center, 3
Millersville University, 4 University of
California-Berkeley, 5 University of
Colorado-Boulder, 6 University of
Maryland-Baltimore County, 7 Environmental
Protection Agency, 8 National Center for
Atmospheric Research, 9 University of Innsbruck
http//discover-aq.larc.nasa.gov/
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Thanks to Partners
Maryland Department of the Environment (MDE) San
Joaquin Valley Air Pollution Control District
(SJV APCD) California Air Resource Board
(CARB) Bay Area Air Quality Management District
(BAAQMD) Texas Commission on Environmental
Quality (TCEQ) Colorado Department of Public
Health and Environment (CDPHE) Environmental
Protection Agency, Office of Res. and
Dev. National Center for Atmospheric
Research National Science Foundation National
Oceanic and Atmospheric Administration National
Park Service University of Maryland, College
Park Howard University University of California,
Davis University of California,
Irvine University of Houston Rice University
University of Texas Baylor University
Princeton University of Colorado-Boulder
Colorado State University
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Investigation Overview
Deriving Information on Surface Conditions from
Column and VERtically Resolved Observations
Relevant to Air Quality
A NASA Earth Venture campaign intended to improve
the interpretation of satellite observations to
diagnose near-surface conditions relating to air
quality
Objectives 1. Relate column observations to
surface conditions for aerosols and key trace
gases O3, NO2, and CH2O 2. Characterize
differences in diurnal variation of surface and
column observations for key trace gases and
aerosols 3. Examine horizontal scales of
variability affecting satellites and model
calculations
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Deployment Strategy
Systematic and concurrent observation of
column-integrated, surface, and
vertically-resolved distributions of aerosols and
trace gases relevant to air quality as they
evolve throughout the day.
Three major observational components
NASA UC-12 (Remote sensing) Continuous mapping of
aerosols with HSRL and trace gas columns with ACAM
NASA P-3B (in situ meas.) In situ profiling of
aerosols and trace gases over surface measurement
sites
Ground sites In situ trace gases and
aerosols Remote sensing of trace gas and aerosol
columns Ozonesondes Aerosol lidar observations
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Deployment Locations
Maryland, July 2011
Houston, September 2013
California, Jan-Feb 2013
Colorado, Jul-Aug 2014
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Predicted NO2 Column Behavior
Taken from Fishman et al., BAMS, 2008
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Predicted NO2 Column Behavior
NO2NOx
NO2 Column
Photochemical Loss
Emissions
Taken from Boersma et al., JGR, 2008
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Pandora Statistics-Maryland
1 x 1015 mol/cm2 0.037 DU
Median and inner quartile values plotted
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P-3B Average Profiles-Maryland
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Pandora Statistics-Houston
1 x 1015 mol/cm2 0.037 DU
Median and inner quartile values plotted
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Pandora Statistics-California
1 x 1015 mol/cm2 0.037 DU
Median and inner quartile values plotted
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P-3B Profile Statistics-CaliforniaUrban sites
BakersfieldFresno
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P-3B Profile Statistics-CaliforniaSub-Urban sites
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Pandora Statistics-Colorado
1 x 1015 mol/cm2 0.037 DU
Median and inner quartile values plotted
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Summary

• 1. DISCOVER-AQ has collected a dataset of
  unprecedented detail on the diurnal trends in air
  quality as it is discerned from in situ and
  remote sensing methods.
• 2. NO2 columns exhibit both unexpected and
  diverse diurnal trends that are consistent with
  vertically resolved profiles.
• 3. NO2 tropospheric column retrievals are highly
  sensitive to diurnal variation in a-priori
  profile shapes.
• 4. Next analysis steps include looking beyond
  median statistics.

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Implications for TEMPO (1 of 2)

• An airborne validation campaign is probably
  beyond TEMPOs budget
• D-AQ core budget 30M for 5-year, 4-campaign
  study partner contributions added 10M
• Typical RA-directed campaign budgets 15-20M
  (3-yr total)
• 1-month GeoTASO/GCAS deployment 1M for flights
  data processing
• Theres no guarantee that an RA airborne
  campaign will take place over North America
  during TEMPO prime mission
• NASA RA led airborne campaigns for atmospheric
  composition historically occur every 2-4 years
  have to take turns with other science focus areas
• Competing resource demands, within atmospheric
  composition and with EV-S

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Implications for TEMPO (2 of 2)

• So what do we do?
• Science studies related to TEMPO observations are
  a logical priority for tropospheric chemistry
  programs post-launch
• Analysis of DISCOVER-AQ, KORUS-AQ, TROPOMI and
  other data sets will help clarify priorities for
  pre- post-launch airborne campaigns related to
  TEMPO
• Spatial representativeness, diurnal influences on
  products, vertical profile shapes,
• Get in line and build advocacy NASA campaign
  concepts typically start as community grass-roots
  efforts leading to development of white papers
• Consider Earth Venture proposal?
• Continue preparatory activities as funding
  permits (GEO-CAPE, HQ RA, possibly HQ Applied
  Science, possibly TEMPO)

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Thoughts from Jim Crawford on Possible Approaches

• There are at least two initial approaches to
  take.  
• One is to leverage what we have learned from
  DISCOVER-AQ, KORUS-AQ, etc. to define a field
  campaign to support TEMPO.  In this case, you
  would hope to be able to get airborne as soon as
  possible after TEMPO launches.  
• Another approach would be to get the right
  surface measurements in place and use them to
  identify the locations where TEMPO needs the most
  help. This would delay a field campaign in favor
  of getting a chance to evaluate TEMPO performance
  using ground obs, sondes, Pandora, TROPOMI,
  etc.  Such a campaign would hopefully be more
  targeted on TEMPO performance, but would also
  hope to see TEMPO observations continue beyond
  the initial 2-years to take advantage of what is
  learned.

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Backups
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P-3B Profile Statistics-CaliforniaUrban sites
BakersfieldFresno
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Pandora vs Surface-Colorado
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Observed NO2 Column Behavior
Taken from Tzortziou et al., JGR, 2014
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CMAQ model Three simulations CMAQ model Three simulations CMAQ model Three simulations CMAQ model Three simulations
Horizontal resolution 4 km x 4 km 4 km x 4 km 4 km x 4 km
Vertical levels 45 (surface-100 hPa) 45 (surface-100 hPa) 45 (surface-100 hPa)
Domain Washington-Baltimore Washington-Baltimore Washington-Baltimore
Chemical mechanism CB05 CB05 CB05
Aerosols AE5 AE5 AE5
Dry deposition M3DRY M3DRY M3DRY
Vertical diffusion ACM2 ACM2 ACM2
Chemical and initial boundary condition RAQMS 12 km x 12 km RAQMS 12 km x 12 km RAQMS 12 km x 12 km
Biogenic emissions Calculated within CMAQ with BEIS Calculated within CMAQ with BEIS Calculated within CMAQ with BEIS
Biomass burning emissions FINNv1 FINNv1 FINNv1
Lightning emissions Calculated within CMAQ Calculated within CMAQ Calculated within CMAQ
Anthropogenic emissions NEI-2005 projected to 2012 NEI-2005 projected to 2012 NEI-2005 projected to 2012
1. ACM2 Base 2. ACM2 Mod 3. YSU Mod
PBL scheme ACM2 ACM2 YSU
Mobile emissions Standard Reduced 50 Reduced 50
Alkyl nitrate photolysis Standard 10 times faster 10 times faster

• Two PBL schemes selected based on the study by
  Clare Flynn
• Emissions and photolysis rate changed based on
  Anderson et al., 2014 and Canty et al., 2014

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Remote Sensing Column Air Mass Factor
Sensitivity Observations and Methods

• Location Padonia, Maryland
• Observation period 3-4 spirals for 14 days in
  July 2011 (Hours covered 6 AM 5 PM, local time)
• NO2 observations
• Aircraft (P3B) measurements (200 m - 4 km) NCAR
  data (accuracy better than10)
• Surface measurements by photolytic converter
  instrument (accuracy better than 10)
• Spatial resolution comparable between model
  (4x4km) and spiral (radius 4km)
• Observed PBL heights Estimation based on
  temperature, water vapor, O3 mixing ratios, and
  RH (Donald Lenschow)
• Methods
• Model and surface measurements sampled for the
  days and time of aircraft measurements
• Spiral data sampled at model vertical grids

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Comparison of NO2 profiles and shape factors
Filled circles observed grid average Open
circles linear interpolation in log space Error
bars standard deviation
fi? NO2 shape factors Oi? partial column wi?
scattering weights (VLIDORT)
AMF (Observation) 1.94 AMF (Base) 2.16 AMF
(ACM2 Mod) 2.3 AMF (YSU Mod) 1.8
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Errors in AMFs/retrievals from a-priori NO2
profiles

• AMF calculated for ACAM (air-borne spectrometer
  located at 8km) but is also relevant for
  tropospheric NO2 column retrievals

wi? scattering weights fi? NO2 shape factors
Surface reflectivities 0.1 to 0.14 at 0.01
steps Solar zenith angles 10 to 80 at 10
steps Aerosol optical depths 0.1 to 0.9 at 0.1
steps
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NO2 profiles and AMFs (11 AM)
AMF (Observation) 1.94 AMF (Base) 2.16 AMF
(ACM2 Mod) 2.3 AMF (YSU Mod) 1.8