Evaluating Orbiting Carbon Observatory 2 (OCO-2) Nadir and Glint Observing Sequences

Thursday, 18 December 2014
Ray Nassar1, Feng Deng2, Saroja Polavarapu1, Mike Neish1, Dylan B. A. Jones2 and Christopher O'Dell3, (1)Environment Canada, Toronto, ON, Canada, (2)University of Toronto, Physics, Toronto, ON, Canada, (3)Colorado State University, Fort Collins, CO, United States
NASA’s Orbiting Carbon Observatory 2 (OCO-2) mission successfully launched on July 2, 2014. OCO-2 measures spectra of reflected solar radiation from the Earth’s surface, which are used to derive high precision column-averaged CO2 mole fractions (XCO2). OCO-2 will alternate between nadir and glint mode every 16 days (233 orbits) with occasional target observations primarily for calibration and validation. Nadir mode typically has better signal-to-noise ratio (SNR) over land and the highest probability of avoiding clouds, but poor SNR over water. Glint mode yields good SNR over water (from the specular reflectance of solar radiation) and land, but glint observations are more susceptible to encountering clouds due to their longer paths through the atmosphere, especially at high solar zenith angles. Is there a quantifiable benefit to cycling between nadir and glint more frequently or increasing the fraction of observations from either mode? This question is investigated by generating synthetic OCO-2 observations for the baseline observing sequence (16-day nadir/glint) and by more frequent alternation (per orbit). Observation distributions (after application of filters) demonstrate the different coverage obtained by the two observing scenarios on a 16-day scale. A forward CO2 simulation of the Environment Canada Carbon Assimilation System (EC-CAS) is designated as the ‘truth’ in an Observing System Simulation Experiment (OSSE) and sampled with the observational coverage from each observing sequence, yielding two sets of synthetic XCO2 observations. The GEOS-Chem CO2 adjoint is used to evaluate the ability of the different synthetic datasets to constrain surface CO2 fluxes. The combination of two model systems in this OSSE enables assessment of the sensitivity of the fluxes to transport errors as well as biases in the OCO-2 observations, leading to a more robust overall assessment of the strengths and weakness of the two observing sequences.