Abstract
Abstract. The number of greenhouse gas (GHG) observing satellites has greatly expanded in recent years, and these new datasets provide an unprecedented constraint on global GHG sources and sinks. However, a continuing challenge for inverse models that are used to estimate these sources and sinks is the sheer number of satellite observations, sometimes in the millions per day. These massive datasets often make it prohibitive to implement inverse modeling calculations and/or assimilate the observations using many types of atmospheric models. Although these satellite datasets are very large, the information content of any single observation is often modest and non-exclusive due to redundancy with neighboring observations and due to measurement noise. In this study, we develop an adaptive approach to reduce the size of satellite datasets using geostatistics. A guiding principle is to reduce the data more in regions with little variability in the observations and less in regions with high variability. We subsequently tune and evaluate the approach using synthetic and real data case studies for North America from NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite. The proposed approach to data reduction yields more accurate CO2 flux estimates than the commonly used method of binning and averaging the satellite data. We further develop a metric for choosing a level of data reduction; we can reduce the satellite dataset to an average of one observation per ∼ 80–140 km for the specific case studies here without substantially compromising the flux estimate, but we find that reducing the data further quickly degrades the accuracy of the estimated fluxes. Overall, the approach developed here could be applied to a range of inverse problems that use very large trace gas datasets.
Highlights
Satellite observations of greenhouse gases (GHGs) have dramatically expanded over the past decade
We develop an approach to data reduction for inverse modeling of GHG observations
New satellite datasets are too large to assimilate in an inverse model given the current computational limitations of existing atmospheric models
Summary
Satellite observations of greenhouse gases (GHGs) have dramatically expanded over the past decade. Scientists at NOAA have generated trajectory simulations for OCO-2 data over North America using STILT as part of the CarbonTrackerLagrange project (e.g., NOAA Global Monitoring Laboratory, 2020a; Miller et al, 2020) These runs have been generated for a single location every 2 s along each satellite flight track, thereby reducing the number of model simulations required. It is not always practical to re-run the atmospheric model and inverse model with different levels of data reduction to decide on an optimal approach due to the computing time involved Instead, this decision is often based upon the spatial resolution of the atmospheric transport model (e.g., NOAA Global Monitoring Laboratory, 2020a) or the anticipated spatial resolution of the flux estimate The approach described here is designed for OCO-2 but could be applied to current and future observations of CO2 (e.g., from GeoCarb) and observations of CH4 (e.g., from the TROPOspheric Monitoring Instrument, TROPOMI, and GeoCarb)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.