Abstract
Abstract. Characterization of errors and sensitivity in remotely sensed observations of greenhouse gases is necessary for their use in estimating regional-scale fluxes. We analyze 15 orbits of the simulated Orbiting Carbon Observatory-2 (OCO-2) with the Atmospheric Carbon Observations from Space (ACOS) retrieval, which utilizes an optimal estimation approach, to compare predicted versus actual errors in the retrieved CO2 state. We find that the nonlinearity in the retrieval system results in XCO2 errors of ∼0.9 ppm. The predicted measurement error (resulting from radiance measurement error), about 0.2 ppm, is accurate, and an upper bound on the smoothing error (resulting from imperfect sensitivity) is not more than 0.3 ppm greater than predicted. However, the predicted XCO2 interferent error (resulting from jointly retrieved parameters) is a factor of 4 larger than predicted. This results from some interferent parameter errors that are larger than predicted, as well as some interferent parameter errors that are more strongly correlated with XCO2 error than predicted by linear error estimation. Variations in the magnitude of CO2 Jacobians at different retrieved states, which vary similarly for the upper and lower partial columns, could explain the higher interferent errors. A related finding is that the error correlation within the CO2 profiles is less negative than predicted and that reducing the magnitude of the negative correlation between the upper and lower partial columns from −0.9 to −0.5 results in agreement between the predicted and actual XCO2 error. We additionally study how the postprocessing bias correction affects errors. The bias-corrected results found in the operational OCO-2 Lite product consist of linear modification of XCO2 based on specific retrieved values, such as the CO2 grad del (δ∇CO2), (“grad del” is a measure of the change in the profile shape versus the prior) and dP (the retrieved surface pressure minus the prior). We find similar linear relationships between XCO2 error and dP or δ∇CO2 but see a very complex pattern of errors throughout the entire state vector. Possibilities for mitigating biases are proposed, though additional study is needed.
Highlights
The Orbiting Carbon Observatory-2 (OCO-2) was launched in July 2014 and began providing science data in September 2014, with the goal of estimating CO2 with the “precision, resolution, and coverage needed to characterize sources and sinks of this important green-house gas.” (Crisp et al, 2004)
A crude way to estimate the XCO2 error resulting from these Jacobian differences is to consider the completely linear case, where radiance is equal to K multiplied by XCO2
For real Atmospheric Carbon Observations from Space (ACOS)-GOSAT (B3.5) data, Kulawik et al (2017) found a slope of 0.39 for land and 0.31 for ocean for LMT and −0.11 and −0.09 for U land and ocean, respectively, which are similar values as seen in this simulated dataset. These results naturally lead to the following question: what is the effect of placing CO2 at the wrong pressure level? The mean Jacobian for the U partial column is only about 60 % (0.62) of the mean value for the lowermost four layers
Summary
The Orbiting Carbon Observatory-2 (OCO-2) was launched in July 2014 and began providing science data in September 2014, with the goal of estimating CO2 with the “precision, resolution, and coverage needed to characterize sources and sinks of this important green-house gas.” (Crisp et al, 2004). Error estimates from nonlinear retrievals of simulated radiances using a fast, simplified radiative transfer, called the “surrogate model” (Hobbs et al, 2017) This system does not result in the discrepancy of larger actual versus predicted error. 3. Error estimates from nonlinear retrievals of simulated radiances generated using the operational L2 forward model, called the “simplified true state”, which has the advantage that the true state is within the span of the retrieval vector and the linear estimate should be valid. Eq (1) would contain many additional error terms that are not considered in these simulations, e.g., spectroscopy, instrument characteristics, aerosol mismatch errors (i.e., picking the wrong aerosol type to retrieve) These are discussed in detail in Connor et al (2016) as linear error estimates.
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