Previous tests of the OCO-2 L2 algorithm with synthetic spectra from a 1-D radiative transfer model showed that low clouds can be difficult to detect in prescreening. Quality control filters can be applied to the retrieved state information to screen out cloud contaminated retrievals, which mitigate the resulting errors in the retrieved XCO2.
In this study, we perform new tests of the L2 algorithm using simulated OCO-2 observations from a 3-D radiative transfer model, SHDOM. We focus on an idealized scene containing a single, spatially unresolved boundary layer cloud. These synthetic observations are processed with the L2 prescreening and retrieval algorithms. Our results show that in many cases, existing prescreening methods are effective at detecting the unresolved cloud. The prescreening is less effective in cases when the observation is primarily affected by the cloud shadow. Quality control filters applied to the retrieved states remain effective at mitigating the cloud contamination. In cases where the observation is not screened or filtered by quality control, the errors in the retrieved XCO2 are relatively low. However, other retrieved state variables, such as aerosol optical depth and surface albedo, show larger errors.