Airborne Trace Gas Retrievals from GeoTASO and GCAS Caroline Nowlan Harvard-Smithsonian Center for Astrophysics Kelly Chance, Gonzalo Gonzalez Abad, Xiong Liu, Amir Souri (Harvard-Smithsonian) James Leitch, Lyle Ruppert (Ball Aerospace) Alan Fried (INSTAAR, CU-Boulder) Scott Janz, Matthew Kowalewski, Melanie Follette-Cook, Ken Pickering (NASA GSFC) Chris Loughner (NOAA) Jay Al-Saadi, Laura Judd (NASA LaRC) Jay Herman (University of Maryland Baltimore County) Elena Spinei (Virginia Tech) Sally Pusede and Andrew Mondschein (University of Virginia)

GeoTASO and GCAS Airborne Instruments GeoTASO = Geostationary Trace gas and Aerosol Sensor Optimization GCAS = GEO-CAPE Airborne Simulator NASA test-bed instruments for geostationary missions Trace gas footprint at surface is ~250 m × 250 m to ~1 km x 1 km

GeoTASO NO2: DISCOVER-AQ Texas 13 September 2013 GeoTASO DISCOVER-AQ 9:15-10:20 Local Time [Nowlan et al., AMT, 2016]

Mapping Morning and Afternoon NO2 Over Denver 2 August 2014: GeoTASO DISCOVER-AQ Colorado 8:00 – 11:30 AM Local time 2:00 – 4:00 pm local time Slant Column

SO2 Power Plant Plumes Photo by J.B. Forbes NO2 and SO2 downwind from Labadie Power Station (Missouri) on GeoTASO DISCOVER-AQ transit, 13 August 2014

GCAS NO2: Houston Validation 25 September 2013 Ignoring Pandora viewing geometry leads to a 20% bias in comparisons P-3B spirals pass in and out of NO2 plumes

GCAS NO2: Houston Validation Pandora vs. GCAS NO2 P-3B NCAR P-CL vs. GCAS NO2 GCAS NO2 50% higher than Pandora direct Sun NO2 but agrees with P-3B

GCAS NO2: Colorado Validation Pandora NO2 vs. GCAS, DISCOVER-AQ Colorado 2014 Large difference with Pandora seen in Texas comparisons does not show up in Colorado

GCAS NO2: Colorado Validation Pandora NO2 vs. GCAS, DISCOVER-AQ Colorado 2014 Large difference with Pandora seen in Texas comparisons does not show up in Colorado Fort Collins site

First HCHO Observations from an Airborne Pushbroom Sensor GCAS, 09/25/2013, Houston Good agreement with P-3B spirals HCHO retrieval meets 1e16 molecules/cm2 TEMPO precision at: 1 km × 1 km (GCAS) 250 m × 250 m (GeoTASO)

Validation and Error Estimation DISCOVER-AQ provided a wealth of coincident data for validation and error estimation. Error examples from Texas: Heterogeneity of air mass factor along aircraft swath results almost entirely from 4 km CMAQ and 1 km MODIS surface BRDF Parameter Effect on NO2 VCD Source Neglecting aerosols +10% bias overall -15 – 25% individual Simulations with HSRL lidar profiles NO2 profile shape +4% bias CMAQ 4km P3B in situ profiles HCHO profile shape +2% bias Neglecting ground-based viewing geometry in comparisons +20% overall 0 to >100 % at site Pandora ground-based spectrometers MODIS BRDF uncertainties +5 – 23 % bias Pandora (Souri et al. 2018)

Airborne Remote Sensing Constraints on NOx Emissions Several studies of Southeast US emissions [e.g., Souri et al., 2016, AE; Travis et al., 2016, ACP; Li et al., 2017, ACP] show that models tend to overestimate anthropogenic NOx sources and underestimate biogenic ones. Possible causes: Inadequate sampling (e.g., footprint) Observation uncertainties Model uncertainties other than emissions Souri et al., 2016, AE

Airborne Remote Sensing Constraints on NOx Emissions Airborne observations may alleviate some of these concerns GCAS pixel is 2500 finer than OMI pixel We can validate airborne observations against other campaign instruments We performed a Kalman Filter inversion day by day using high resolution CMAQ-DDM (1x1 km2) in the Houston-Galveston-Brazoria area.

Houston, 25 Sept 2013: An Exceptional Event NO2 Column NO2 Surface The 25th September experienced elevated ozone, NO2 and several VOCs [Pan et al., 2017; 2018, AE]. Such an underprediction in the model could not be mitigated using OMI. GCAS provided a unique opportunity to detect and constrain an emission event anomaly in the Houston area. Results ensure that next generation satellites (with better spatial and temporal resolutions) should have a unique capability to detect and constrain emissions anomalies. Souri et al., 2018, JGR

Observational constraints on community-scale air pollution exposure inequalities Air pollution in U.S. cities is often higher in low-income communities and communities of color. U.S. EPA Surface Monitors Does higher-resolution data reveal greater disparities? Sally Pusede, University of Virginia Jeff Geddes, Boston University Caroline Nowlan, Harvard Smithsonian Center for Astrophysics Laura Judd and Jay Al-Saadi, NASA Langley Research Center GeoTASO in black box region

Observational constraints on community-scale air pollution exposure inequalities Exposure is a function of human travel patterns throughout the day. To quantify exposure disparities, we are combining GeoTASO and GCAS measurements with travel-activity data for tens of thousands of individuals in Los Angeles and Houston. Sally Pusede and Andrew Mondschein, University of Virginia

KORUS-AQ: New Molecules First Detection of Glyoxal from an Airborne Instrument First Detection of Nitrous Acid using Nadir Remote Sensing Seoul 9 June 2016 16-18 LT Seoul 9 June 2016 16-18 LT

Next Steps Final GCAS and GeoTASO trace gas products for DISCOVER-AQ Colorado should be archived this summer O3 profile work will be continued under KORUS-AQ and TEMPO projects SAO is starting projects to produce OMPS (Suomi-NPP and NOAA- 20) products Planning extensive satellite validation using in situ and remote sensing aircraft (GeoTASO and GCAS) HCHO data

Extra Slides

Air Quality Airborne Campaigns Location Date Instrument DISCOVER-AQ Texas September 2013 GeoTASO, GCAS Colorado July-August 2014 KORUS-AQ South Korea May-June 2016 GeoTASO Lake Michigan Ozone Study Wisconsin & Michigan May-June 2017 GOES-R lightning study (NO2) Southeast, Midwest US 2017 GCAS Student Airborne Research Program California June 2017 Both GeoTASO and GCAS have participated in several additional ocean color campaigns.

SO2 Power Plant Plumes Photo by J.B. Forbes NO2 and SO2 downwind from Labadie Power Station (Missouri) on GeoTASO DISCOVER-AQ transit, 13 August 2014

GeoTASO and GCAS Coincident flights within 20 minutes show NO2 variability over short time scales (Houston 09/13/2013 AM flight shown below) [Nowlan et al., AMT, 2016]

Ozone Profiles Ozone profile retrieval works but results are highly dependent on input parameters No reference spectrum is ideal for airborne measurements Current UV radiances imply a negative albedo  Absolute calibration and stray light corrections in early campaigns need improvement