Improving OCO-2 X C O 2 ${X}_{{\mathbf{C}\mathbf{O}}_{\mathbf{2}}}$ Retrievals Through the Scaling of Singular Value Decomposition-Based Temperature and Water Vapor Profiles

Abstract

NASA's Orbiting Carbon Observatory-2 (OCO-2) has the goal of accurately estimating column-averaged dry-air mole fractions of carbon dioxide ($X_{{CO}_2}$). In order to fit the measured radiances, many parameters besides ${\text{CO}}_2$ are included in the optimal estimation state vector, including atmospheric water vapor and temperature. The current operational $X_{{CO}_2}$ retrieval algorithm (v11) solves for a multiplicative scaling factor on an a priori water vapor profile and an additive offset on an a priori temperature profile. However, simulations have indicated that water vapor and temperature each have 1.5–3 degrees of freedom in the vertical column. This means that the retrieval is limited in its ability to fit the true profiles of temperature and water vapor. Here, we use singular value decomposition to determine the three most explanatory profile “shapes” of water vapor and temperature error, then retrieve a single scaling factor applied to each shape. We assess retrieval errors by comparing to the Total Carbon Column Observing Network (TCCON) and multiple atmospheric ${\text{CO}}_2$ inverse models. We find that after applying quality filtering using Data Ordering Genetic Optimization and a custom bias correction, the scatter of the $X_{{CO}_2}$ error versus TCCON is reduced from 1.02 to 1.01 ppm (2.3% reduction in variance) for land glint observations, 1.04 to 0.96 ppm (14.5% reduction in variance) for land nadir observations, and 0.68 to 0.66 ppm (4.7% reduction in variance) for ocean glint observations. We also see a small improvement in the agreement between OCO-2 $X_{{CO}_2}$ and ${\text{CO}}_2$ models over oceans and the Amazon.