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Yver, CE, Graven HD, Lucas DD, Cameron-Smith PJ, Keeling RF, Weiss RF.  2013.  Evaluating transport in the WRF model along the California coast. Atmospheric Chemistry and Physics. 13:1837-1852.   10.5194/acp-13-1837-2013   AbstractWebsite

This paper presents a step in the development of a top-down method to complement the bottom-up inventories of halocarbon emissions in California using high frequency observations, forward simulations and inverse methods. The Scripps Institution of Oceanography high-frequency atmospheric halocarbons measurement sites are located along the California coast and therefore the evaluation of transport in the chosen Weather Research Forecast (WRF) model at these sites is crucial for inverse modeling. The performance of the transport model has been investigated by comparing the wind direction and speed and temperature at four locations using aircraft weather reports as well at all METAR weather stations in our domain for hourly variations. Different planetary boundary layer (PBL) schemes, horizontal resolutions (achieved through nesting) and two meteorological datasets have been tested. Finally, simulated concentration of an inert tracer has been briefly investigated. All the PBL schemes present similar results that generally agree with observations, except in summer when the model sea breeze is too strong. At the coarse 12 km resolution, using ERA-interim (ECMWF Re-Analysis) as initial and boundary conditions leads to improvements compared to using the North American Model (NAM) dataset. Adding higher resolution nests also improves the match with the observations. However, no further improvement is observed from increasing the nest resolution from 4 km to 0.8 km. Once optimized, the model is able to reproduce tracer measurements during typical winter California large-scale events (Santa Ana). Furthermore, with the WRF/CHEM chemistry module and the European Database for Global Atmospheric Research (EDGAR) version 4.1 emissions for HFC-134a, we find that using a simple emission scaling factor is not sufficient to infer emissions, which highlights the need for more complex inversions.

Yadav, V, Duren R, Mueller K, Verhulst KR, Nehrkorn T, Kim J, Weiss RF, Keeling R, Sander S, Fischer ML, Newman S, Falk M, Kuwayama T, Hopkins F, Rafiq T, Whetstone J, Miller C.  2019.  Spatio-temporally resolved methane fluxes from the Los Angeles megacity. Journal of Geophysical Research-Atmospheres. 124:5131-5148.   10.1029/2018jd030062   AbstractWebsite

We combine sustained observations from a network of atmospheric monitoring stations with inverse modeling to uniquely obtain spatiotemporal (3-km, 4-day) estimates of methane emissions from the Los Angeles megacity and the broader South Coast Air Basin for 2015-2016. Our inversions use customized and validated high-fidelity meteorological output from Weather Research Forecasting and Stochastic Time-Inverted Lagrangian model for South Coast Air Basin and innovatively employ a model resolution matrix-based metric to disentangle the spatiotemporal information content of observations as manifested through estimated fluxes. We partially track and constrain fluxes from the Aliso Canyon natural gas leak and detect closure of the Puente Hills landfill, with no prior information. Our annually aggregated fluxes and their uncertainty excluding the Aliso Canyon leak period lie within the uncertainty bounds of the fluxes reported by the previous studies. Spatially, major sources of CH4 emissions in the basin were correlated with CH4-emitting infrastructure. Temporally, our findings show large seasonal variations in CH4 fluxes with significantly higher fluxes in winter in comparison to summer months, which is consistent with natural gas demand and anticorrelated with air temperature. Overall, this is the first study that utilizes inversions to detect both enhancement (Aliso Canyon leak) and reduction (Puente Hills) in CH4 fluxes due to the unintended events and policy decisions and thereby demonstrates the utility of inverse modeling for identifying variations in fluxes at fine spatiotemporal resolution.