1 Jim Crawford 1, Ken Pickering 2, Lok Lamsal 2, Bruce Anderson 1, Andreas Beyersdorf 1, Gao Chen 1, Richard Clark 3, Ron Cohen 4, Glenn Diskin 1, Rich Ferrare 1, Alan Fried 5, Brent Holben 2, Jay Herman 6, Ray Hoff 6, Chris Hostetler 1, Scott Janz 2, Mary Kleb 1, Jim Szykman 7, Anne Thompson 2, Andy Weinheimer 8, Armin Wisthaler 9, Melissa Yang 1, Jay Al-Saadi 1 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 Challenges and opportunities for remote sensing of air quality: Insights from DISCOVER-AQ

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 Thanks to Partners

Deriving Information on Surface Conditions from Column and VERtically Resolved Observations Relevant to Air Quality 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 O 3, NO 2, and CH 2 O 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 Investigation Overview 3

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. 4 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 Three major observational components:

5 Deployment Locations Maryland, July 2011Houston, September 2013 California, Jan-Feb 2013 Colorado, Jul-Aug 2014

6 Taken from Fishman et al., BAMS, 2008 Predicted NO 2 Column Behavior

7 Taken from Boersma et al., JGR, 2008 Emissions Photochemical Loss NO 2 Column NO 2 :NO x Predicted NO 2 Column Behavior

8 1 x mol/cm 2 = DU Pandora Statistics-Maryland Median and inner quartile values plotted

9 P-3B Average Profiles-Maryland

10 Pandora Statistics-Houston 1 x mol/cm 2 = DU Median and inner quartile values plotted

11 Pandora Statistics-California 1 x mol/cm 2 = DU Median and inner quartile values plotted

12 P-3B Profile Statistics-California Urban sites: Bakersfield+Fresno

13 P-3B Profile Statistics-California Sub-Urban sites

14 Pandora Statistics-Colorado 1 x mol/cm 2 = DU Median and inner quartile values plotted

15 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. NO 2 columns exhibit both unexpected and diverse diurnal trends that are consistent with vertically resolved profiles. 3. NO 2 tropospheric column retrievals are highly sensitive to diurnal variation in a-priori profile shapes. 4. Next analysis steps include looking beyond median statistics.

16 Implications for TEMPO (1 of 2) An airborne validation campaign is probably beyond TEMPO’s budget D-AQ core budget $30M for 5-year, 4-campaign study; partner contributions added ~$10M Typical R&A-directed campaign budgets $15-$20M (3-yr total) 1-month GeoTASO/GCAS deployment ~$1M for flights & data processing There’s no guarantee that an R&A airborne campaign will take place over North America during TEMPO prime mission NASA R&A 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

17 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 R&A, possibly HQ Applied Science, possibly TEMPO)

18 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.

Backups

20 P-3B Profile Statistics-California Urban sites: Bakersfield+Fresno

21 Pandora vs Surface-Colorado

22 Taken from Tzortziou et al., JGR, 2014 Observed NO 2 Column Behavior

CMAQ model: Three simulations Horizontal resolution4 km x 4 km Vertical levels45 (surface-100 hPa) DomainWashington-Baltimore Chemical mechanismCB05 AerosolsAE5 Dry depositionM3DRY Vertical diffusionACM2 Chemical and initial boundary condition RAQMS; 12 km x 12 km Biogenic emissions Calculated within CMAQ with BEIS Biomass burning emissions FINNv1 Lightning emissions Calculated within CMAQ Anthropogenic emissionsNEI-2005 projected to ACM2 Base2. ACM2 Mod3. YSU Mod PBL schemeACM2 YSU Mobile emissionsStandardReduced 50% Alkyl nitrate photolysisStandard10 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

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) ► NO 2 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, O 3 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

Comparison of NO 2 profiles and shape factors Filled circles: observed grid average Open circles: linear interpolation in log space Error bars: standard deviation f i  NO 2 shape factors Ω i  partial column w i  scattering weights (VLIDORT) AMF (Observation) = 1.94 AMF (Base) = 2.16 AMF (ACM2 Mod) = 2.3 AMF (YSU Mod) = 1.8

Errors in AMFs/retrievals from a-priori NO 2 profiles w i  scattering weights f i  NO 2 shape factors ► AMF calculated for ACAM (air-borne spectrometer located at ~8km) but is also relevant for tropospheric NO 2 column retrievals 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

NO 2 profiles and AMFs (11 AM) AMF (Observation) = 1.94 AMF (Base) = 2.16 AMF (ACM2 Mod) = 2.3 AMF (YSU Mod) = 1.8