Surface and Column Variations of CO2 using Weighting Functions for Future Active Remote CO2 sensors and Data from DISCOVER-AQ Field Campaign

Abstract:

Fast response (1 Hz) and high precision (< 0.1 ppmv) in situ CO₂ measurements were recorded onboard the NASA P-3B during the DISCOVER-AQ (Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality) Field Campaign, to investigate the ability of space-based observations to accurately assess near surface conditions related to air quality. The campaign spanned 4 years and took place over four geographically different locations. These included, Washington DC/Baltimore, MD (July 2011), San Joaquin Valley, CA (January – February 2013), Houston, TX (September 2013), and Denver, CO (July-August 2014). With the objective of obtaining better CO₂ column calculations, each of these campaigns consisted of missed approaches and approximately two hundred vertical soundings of CO₂ (from the surface to about 5 km). In this study, surface and column-averaged CO₂ mixing ratio values from the vertical soundings in the four different urban areas are used to examine the temporal and spatial variability of CO₂ within the lower troposphere. Tracers such as CO, CH₂O, NOx, and NMHCs will be used to identify the source of variations observed in these urban sites. Additionally, we apply nominal CO₂ column weighting functions for potential future active remote CO₂ sensors operating in the 1.57-mm and 2.05-mm measurement regions to convert the in situ CO₂ vertical mixing ratio profiles to variations in CO₂ column optical depths, which is what the active remote sensors actually measure. Using statistics calculated from the optical depths at each urban site measured during the DISCOVER-AQ field campaign and for each nominal weighting function, we compare the natural variability of CO₂ columns in the lower troposphere; relate the CO₂ column variability to surface emissions; and show the measurement requirements for the future ASCENDS (Active Sensing of CO₂ Emissions over Nights, Days, and Seasons) in the continental U.S. urban areas.