A Regional Inversion Framework to Estimate Urban Fluxes
Abstract
Studies have shown that urban greenhouse gas emissions in the U.S. significantly contribute to the National total. Currently, urban emission research (for both CO2 and CH4) has focused on particular metropolitan areas such as Indianapolis, Boston, Los Angeles, etc. For many cities, isolating the GHG component of mole-fractions attributable to emissions within the urban domain is difficult due to concentrations of GHG's in the air entering the region (aka background) - a key requirement for accurately estimating urban emissions. This is especially true in the Northeastern U.S. As such, this research presents a CO2 pseudo-data experiment that helps demonstrate the utility of a regional inversion as a step towards reducing background errors. This approach has additional benefits than solely isolating the urban emission signal from urban observations. For example, the framework can provide a means to consistently compare emissions across different cities using a fine grid, regional and urban tower data, as well as nationally available datasets (e.g. priors and meteorological data). The estimates from such a framework can also be used against previously published fluxes for a given city in the regional domain. Our regional inversion covers the eastern portion of the United States which extends as far west as eastern Iowa and Missouri and south to Mississippi and South Carolina. Our pseudo data demonstration involves recovering hourly 10km2 fluxes using synthetic mole fractions associated with existing urban and regional towers using Vulcan, VPRM, and WRF-STILT. We utilize a geostatistical Bayesian method to both estimate fluxes and associated uncertainties. Future work will involve real measurements, a variety of prior fluxes and meteorological products, as well as nested urban grids of 1km2 around specific cities. The framework eventually will also be used to estimate CH4 emissions.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFM.A11F2270M
- Keywords:
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- 0322 Constituent sources and sinks;
- ATMOSPHERIC COMPOSITION AND STRUCTUREDE: 1910 Data assimilation;
- integration and fusion;
- INFORMATICSDE: 1986 Statistical methods: Inferential;
- INFORMATICSDE: 3260 Inverse theory;
- MATHEMATICAL GEOPHYSICS