Evaluating NO2 Variability of In-Situ and Remote Sensing Observations from Aircraft and Ground Sites During DISCOVER-AQ

Monday, 15 December 2014
Morgan L Silverman1,2, James Szykman3, Gao Chen2, James H Crawford2, Scott J Janz4, Matthew G Kowalewski4,5, Lok N Lamsal4,5, Russell Long6 and Melinda R Beaver6, (1)Science Systems and Applications, Inc. - Hampton, Hampton, VA, United States, (2)NASA Langley Research Center, Hampton, VA, United States, (3)US EPA, ORD, National Exposure Research Laboratory, Hampton, VA, United States, (4)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (5)Universities Space Research Association Columbia, Columbia, MD, United States, (6)US EPA, RTP, NC, United States
Spatial variability of NO2 has largely been examined from satellite NO2 column measurements. Understanding this variability is important for emission controls, health impacts, and photochemistry. However, due the short lifetime of NO2, its variability is difficult to capture. Ground based monitors are extremely important to evaluate satellite column measurements and provide more detailed spatial information. Unfortunately, ground monitors are limited in number and geographically sparse. The DISCOVER-AQ campaign provides a unique dataset that allows for the assessment of spatial variability from aircraft in-situ measurements on the NASA P-3B, remote sensing measurements from the Airborne Compact Atmospheric Mapper (ACAM) on the NASA UC-12 and NASA B200, and ground site measurements over the same area. We use first order structure functions to provide an analysis of spatial gradients over a given distance seen by the P-3B in-situ instruments and ACAM. The spatial variability of these measurements are then compared to ground measurements across the flight domain. Column densities are also calculated from the DISCOVER-AQ vertical profiles to assess the variability of a column within the aircraft profile. Results show that spatial variability depends on the airmass being sampled, polluted versus background conditions.