Data Compression to Improve the Computational Efficiency of Inverse Modeling with Large Satellite Datasets

In the last decade, satellite observations of atmospheric gases have been widely employed to investigate total emissions, seasonal and inter-annual variability, and to attribute emissions to different natural and anthropogenic source types. A continuing challenge for satellite-based inverse modeling is the sheer size of many satellite datasets. The large number of observations often makes it prohibitive to run trajectory models (e.g., STILT or FLEXPART), save the Jacobian or sensitivity matrix, or implement some inverse modeling calculations (e.g., calculate posterior uncertainties). Many satellite datasets of atmospheric gases are very large, but the information content of any single total column observation can be modest due to column averaging and due to retrieval errors. What's more, the information content of each retrieval often has partial redundancy with neighboring retrievals. These facts indicate that many satellite trace gas datasets may be compressible to much smaller size without losing information about the underlying fluxes or emissions. In this study, we develop a satellite-based data compression approach based on geostatistical principles. We subsequently tune and evaluate the data compression using a case study from the Orbiting Carbon Observatory-2 (OCO-2), NASA's first satellite capable of observing atmospheric CO₂ with the sensitivity, precision, and coverage needed to characterize surface CO₂ sources and sinks. We compare CO₂ sources and sinks estimated from the original and compressed satellite dataset using a geostatistical inverse modeling approach. We find that data compression can efficiently reduce the size and computational burden of inverse modeling with OCO-2 data while still producing robust maps of CO₂ sources and sinks. Furthermore, the approach developed here could be applied broadly to a range of inverse problems that use very large trace gas datasets.