Detecting Urban Emissions Changes and Events With a Near‐Real‐Time‐Capable Inversion System

Ware, John R.; Kort, E. A.; Duren, Riley; Mueller, Kimberly D.; Verhulst, K. R.; Yadav, Vineet

doi:10.1029/2018jd029224

Cited by 23 publications

(20 citation statements)

References 49 publications

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“…Due to this reason, we restricted our use of ACARS profiler data to point-by-point comparison of winds and temperature. This comparison with winds and temperature is shown in the supporting information of the manuscript (coauthored by the author) that uses the same WRF-STILT output as in this study and has been accepted for publication in the Journal of Geophysical Research: Atmospheres (see Ware et al, 2019).…”

Section: 1029/2018jd030062mentioning

confidence: 99%

“…A methodological advancement in rapidly (near real time) identifying these anomalies through inverse modeling that does not rely on tailored meteorological output is presented in Ware et al (2019). This network provides an unprecedented capability to determine spatiotemporal variability of CH 4 emissions at a fine spatiotemporal resolution.…”

Section: Introductionmentioning

confidence: 99%

“…This allows us to provide subbasin estimates of CH 4 emissions and spatio-temporally identify its anomalies in the basin. A methodological advancement in rapidly (near real time) identifying these anomalies through inverse modeling that does not rely on tailored meteorological output is presented in Ware et al (2019).…”

Section: Introductionmentioning

confidence: 99%

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Spatio‐temporally Resolved Methane Fluxes From the Los Angeles Megacity

et al. 2019

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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.

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Section: 1029/2018jd030062mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatio‐temporally Resolved Methane Fluxes From the Los Angeles Megacity

et al. 2019

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“…To infer gridded methane emissions, we build on the inverse framework described in Yadav et al (2019), with modifications to account for multiple observing systems. Given a set of observations y and a Jacobian matrix H , we employ an inverse method to derive the optimal estimate

\overset{x}{true}

using the Maximum A Posteriori (MAP) method similar to previous methane inversion studies (e.g., Bergamaschi et al, 2009; Kort et al, 2014; Ware et al, 2019; Wecht et al, 2014; Yadav et al, 2019). Here an objective function J ( x ) is created to balance model‐data mismatch and the deviation from a prior emission field x A , which are both assumed to be distributed normally (Rodgers, 2000):

J ()(, x) = {(y - Hx)}^{T} R^{- 1} ()(, boldy - boldHx) + {(x - {boldx}_{boldA})}^{T} S^{- 1} ()(, boldx - x_{A}),

where R is the observational error covariance matrix and S is the prior error covariance matrix.…”

Section: Multitiered Observing and Analytics Systemmentioning

confidence: 99%

Synthesis of Methane Observations Across Scales: Strategies for Deploying a Multitiered Observing Network

Cusworth

Duren

Yadav

et al. 2020

Geophysical Research Letters

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Regional methane emissions monitoring is rapidly expanding with increased coverage of surface, airborne, and satellite instruments. We pilot a multitiered observing system in the Los Angeles Basin. We combine surface methane measurements from the Los Angeles Megacities Carbon Project, mountaintop retrievals from the CLARS-FTS instrument, and space-based X CH4 retrievals from the TROPOMI instrument into a single monitoring framework. We simulate these observations using a high-resolution tracer transport model. Using inverse methods, we compare the sensitivity of each observing system component to various emissions sources. Combining multiple observing system into one framework allows for increased spatial and temporal sensitivity to methane emissions. We find a close correspondence between these inverse flux trends and independent airborne AVIRIS-NG methane plume trends over a large landfill in the Los Angeles Basin. These results show that multitiered observing systems can reveal insights about sub-basin scale methane emissions, which can be used to drive decision support. Plain Language SummaryMethane is a powerful greenhouse gas. In order to effectively reduce its atmospheric concentrations, we need advanced methane observing strategies to pinpoint large emissions on small spatial scales. In this study, we combine surface, mountaintop, and satellite observations of methane over Los Angeles (called a multitiered observing system) and use these data to infer information about urban methane emissions. We assess how much information each component of the observing system provides to this analytics system. We validate our findings with independent airborne methane fluxes derived from the AVIRIS-NG airborne instrument over a large landfill. Both systems detected large emission reductions that resulted from improved management practices. A multitiered observing and analytics system can potentially provide sub-basin scale decision support for methane mitigation.

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“…Both models use the Japan 25-year reanalysis (JRA-25)/JMA Climate Data Assimilation System (JCDAS) meteorology (Onogi et al, 2007), with 40 vertical levels interpolated to a 1.25° × 1.25° grid. The use of low-resolution wind fields for high resolution transport is better justified for cases of nearly geostrophic flow over flat terrain (Ganshin et al, 2012), but was also considered applicable in regions with more complex topography (Ware et al, 2019). The coupled transport model adjoint was derived from the Global Eulerian-Lagrangian Coupled Atmospheric transport model (GELCA) (Ganshin et al, 2012;Zhuravlev et al, 2013;Shirai et al, 2017).…”

mentioning

confidence: 99%

Technical note: A high-resolution inverse modelling technique for estimating surface CO<sub>2</sub> fluxes based on the NIES-TM – FLEXPART coupled transport model and its adjoint

Maksyutov¹,

Oda²,

Saito³

et al. 2020

Preprint

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Abstract. We developed a high-resolution surface flux inversion system based on the global Lagrangian–Eulerian coupled tracer transport model composed of National Institute for Environmental Studies Transport Model (NIES-TM) and FLEXible PARTicle dispersion model (FLEXPART). The inversion system is named NTFVAR (NIES-TM-FLEXPART-variational) as it applies variational optimisation to estimate surface fluxes. We tested the system by estimating optimized corrections to natural surface CO2 fluxes to achieve best fit to atmospheric CO2 data collected by the global in-situ network, as a necessary step towards capability of estimating anthropogenic CO2 emissions. We employ the Lagrangian particle dispersion model (LPDM) FLEXPART to calculate the surface flux footprints of CO2 observations at a 0.1° × 0.1° spatial resolution. The LPDM is coupled to a global atmospheric tracer transport model (NIES-TM). Our inversion technique uses an adjoint of the coupled transport model in an iterative optimization procedure. The flux error covariance operator is being implemented via implicit diffusion. Biweekly flux corrections to prior flux fields were estimated for the years 2010–2012 from in-situ CO2 data included in the Observation Package (ObsPack) dataset. High-resolution prior flux fields were prepared using Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) for fossil fuel combustion, Global Fire Assimilation System (GFAS) for biomass burning, the Vegetation Integrative SImulator for Trace gases (VISIT) model for terrestrial biosphere exchange and Ocean Tracer Transport Model (OTTM) for oceanic exchange. The terrestrial biospheric flux field was constructed using a vegetation mosaic map and separate simulation of CO2 fluxes at daily time step by the VISIT model for each vegetation type. The prior flux uncertainty for terrestrial biosphere was scaled proportionally to the monthly mean Gross Primary Production (GPP) by the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD17 product. The inverse system calculates flux corrections to the prior fluxes in the form of a relatively smooth field multiplied by high-resolution patterns of the prior flux uncertainties for land and ocean, following the coastlines and vegetation productivity gradients. The resulting flux estimates improve fit to the observations at continuous observations sites, reproducing both the seasonal variation and short-term concentration variability, including high CO2 concentration events associated with anthropogenic emissions. The use of high-resolution atmospheric transport in global CO2 flux inversion has the advantage of better resolving the transport from the mix of the anthropogenic and biospheric sources in densely populated continental regions and shows potential for better separation between fluxes from terrestrial ecosystems and strong localised sources such as anthropogenic emissions and forest fires. Further improvements in the modelling system are needed as the posterior fit is better than that by the National Oceanic and Atmospheric Administration (NOAA) CarbonTracker only for a fraction of the monitoring sites, mostly at coastal and island locations experiencing mix of background and local flux signals.

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Detecting Urban Emissions Changes and Events With a Near‐Real‐Time‐Capable Inversion System

Cited by 23 publications

References 49 publications

Spatio‐temporally Resolved Methane Fluxes From the Los Angeles Megacity

Spatio‐temporally Resolved Methane Fluxes From the Los Angeles Megacity

Synthesis of Methane Observations Across Scales: Strategies for Deploying a Multitiered Observing Network

Technical note: A high-resolution inverse modelling technique for estimating surface CO<sub>2</sub> fluxes based on the NIES-TM – FLEXPART coupled transport model and its adjoint

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Detecting Urban Emissions Changes and Events With a Near‐Real‐Time‐Capable Inversion System

Cited by 23 publications

References 49 publications

Spatio‐temporally Resolved Methane Fluxes From the Los Angeles Megacity

Spatio‐temporally Resolved Methane Fluxes From the Los Angeles Megacity

Synthesis of Methane Observations Across Scales: Strategies for Deploying a Multitiered Observing Network

Technical note: A high-resolution inverse modelling technique for estimating surface CO&lt;sub&gt;2&lt;/sub&gt; fluxes based on the NIES-TM – FLEXPART coupled transport model and its adjoint

Contact Info

Product

Resources

About

Technical note: A high-resolution inverse modelling technique for estimating surface CO<sub>2</sub> fluxes based on the NIES-TM – FLEXPART coupled transport model and its adjoint