Comparing Lagrangian and Eulerian models for CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; transport – a step towards Bayesian inverse modeling using WRF/STILT-VPRM

Pillai, Dhanyalekshmi; Gerbig, Christoph; Kretschmer, R.; Beck, Veronika; Karstens, Ute; Neininger, B.; Heimann, Martin

doi:10.5194/acpd-12-1267-2012

Cited by 7 publications

(9 citation statements)

References 28 publications

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“…For each measurement receptor (every 0.1° × 0.1° averaged AJAX measurement) during each flight an ensemble of 100 particles were released and transported backward in time for 7 days in WRF‐STILT simulations. The number of particles used in past applications of the WRF‐STILT model typically vary between 100 and 500 particles [e.g., Gerbig et al ., ; Lin et al ., ; Pillai et al ., ], and here we chose to use 100 particles in order to increase computational efficiency. The starting locations for each 0.1° × 0.1° averaged measurement receptor in WRF‐STILT simulations were the average latitude, longitude, and height from the measurements made in each grid cell.…”

Section: Methodsmentioning

confidence: 99%

Investigating seasonal methane emissions in Northern California using airborne measurements and inverse modeling

Johnson

Jeong

et al. 2016

JGR Atmospheres

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Seasonal methane (CH4) emissions in Northern California are evaluated during this study by using airborne measurement data and inverse model simulations. This research applies Alpha Jet Atmospheric eXperiment (AJAX) measurements obtained during January–February 2013, July 2014, and October–November 2014 over the San Francisco Bay Area (SFBA) and northern San Joaquin Valley (SJV) in order to constrain seasonal CH4 emissions in Northern California. The California Greenhouse Gas Emissions Measurement (CALGEM) a priori emission inventory was applied in conjunction with the Weather Research and Forecasting and Stochastic Time‐Inverted Lagrangian Transport model and inverse modeling techniques to optimize CH4 emissions. Comparing model‐predicted CH4 mixing ratios with airborne measurements, we find substantial underestimates suggesting that CH4 emissions were likely larger than the year 2008 a priori CALGEM emission inventory in Northern California. Using AJAX measurements to optimize a priori emissions resulted in CH4 flux estimates from the SFBA/SJV of 1.77 ± 0.41, 0.83 ± 0.31, and 1.06 ± 0.39 Tg yr−1 when using winter, summer, and fall flight data, respectively. Averaging seasonal a posteriori emission estimates (weighted by posterior uncertainties) results in SFBA/SJV annual CH4 emissions of 1.28 ± 0.38 Tg yr−1. A posteriori uncertainties are reduced more effectively in the SFBA/SJV region compared to state‐wide values indicating that the airborne measurements are most sensitive to emissions in this region. A posteriori estimates during this study suggest that dairy livestock was the source with the largest increase relative to the a priori CALGEM emission inventory during all seasons.

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Section: Methodsmentioning

confidence: 99%

Investigating seasonal methane emissions in Northern California using airborne measurements and inverse modeling

Johnson

Jeong

et al. 2016

JGR Atmospheres

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“…Each hour, we released 500 particles from the receptor (tall tower 100‐m level) and tracked these particles backward in time for 7 days and identified their locations in 3D following the methods of Karion et al () and Chen et al (). Intercomparison of two Global 3‐D background concentration data sets and comparisons with the global background CO 2 network observations demonstrated that these products have relatively high accuracy (Peters et al, ; Pillai et al, ). Both products (Carbon Tracker and Jana inversion) are generated from the TM5 transport model with optimized CO 2 fluxes (Peters et al, ; Pillai et al, ).…”

Section: Materials and Methodologymentioning

confidence: 99%

Top‐Down Constraints on Anthropogenic CO₂ Emissions Within an Agricultural‐Urban Landscape

Griffis

Lee

et al. 2018

JGR Atmospheres

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Anthropogenic carbon dioxide (CO2) emissions dominate the atmospheric greenhouse gas radiative forcing budget. However, these emissions are poorly constrained at the regional (102–106 km2) and seasonal scales. Here we use a combination of tall tower CO2 mixing ratio and carbon isotope ratio observations and inverse modeling techniques to constrain anthropogenic CO2 emissions within a highly heterogeneous agricultural landscape near Saint Paul, Minnesota, in the Upper Midwestern United States. The analyses indicate that anthropogenic emissions contributed 6.6, 6.8, and 7.4 μmol/mol annual CO2 enhancements (i.e., departures from the background values) in 2008, 2009, and 2010, respectively. Oil refinery, the energy industry (power and heat generation), and residential emissions (home heating and cooking) contributed 2.9 (42.5%), 1.4 (19.8%), and 1.1 μmol/mol (15.8%) of the total anthropogenic enhancement over the 3‐year period according to a priori inventories. The total anthropogenic signal was further partitioned into CO2 emissions derived from fuel oil, natural gas, coal, gasoline, and diesel consumption using inverse modeling and carbon isotope ratio analyses. The results indicate that fuel oil and natural gas consumption accounted for 52.5% of the anthropogenic CO2 sources in winter. Here the a posteriori CO2 emission from natural gas was 79.0 ± 4.1% (a priori 20.0%) and accounted for 63% of the total CO2 enhancement including both biological and anthropogenic sources. The a posteriori CO2 emission from fuel oil was 8.4 ± 3.8% (a priori 32.5%)—suggesting a more important role of residential heating in winter. The modeled carbon isotope ratio of the CO2 source (δ13Cs , −29.3 ± 0.4‰) was relatively more enriched in 13C‐CO2 compared to that derived from Miller‐Tans plot analyses (−35.5‰ to −34.8‰), supporting that natural gas consumption was underestimated for this region.

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“…Lagrangian particle dispersion models (LPDM) have been widely used in CO 2 flux inference (e.g., [16] and references therein) on regional to urban scales, because they can conveniently establish the source-receptor relationship needed for flux inversion. In principle, the two modeling approaches, i.e., Eulerian and Lagrangin modeling, could be used simultaneously and complement each other [78].…”

Section: The Role Of Cmaq: a State-of-the-art Eulerian Regional Chemimentioning

confidence: 99%

Greenhouse Gas Source Attribution: Measurements Modeling and Uncertainty Quantification

Liu

Safta

Sargsyan

et al. 2014

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In this project we have developed atmospheric measurement capabilities and a suite of atmospheric modeling and analysis tools that are well suited for verifying emissions of greenhouse gases (GHGs) on an urban-through-regional scale. We have for the first time applied the Community Multiscale Air Quality (CMAQ) model to simulate atmospheric CO 2. This will allow for the examination of regional-scale transport and distribution of CO 2 along with air pollutants traditionally studied using CMAQ at relatively high spatial and temporal resolution with the goal of leveraging emissions verification efforts for both air quality and climate. We have developed a bias-enhanced Bayesian inference approach that can remedy the well-known problem of transport model errors in atmospheric CO 2 inversions. We have tested the approach using data and model outputs from the TransCom3 global CO 2 inversion comparison project. We have also performed two prototyping studies on inversion approaches in the generalized convection-diffusion context. One of these studies employed Polynomial Chaos Expansion to accelerate the evaluation of a regional transport model and enable efficient Markov Chain Monte Carlo sampling of the posterior for Bayesian inference. The other approach uses deterministic inversion of a convection-diffusion-reaction system in the presence of uncertainty. These approaches should, in principle, be applicable to realistic atmospheric problems with moderate adaptation. We outline a regional greenhouse gas source inference system that integrates (1) two approaches of atmospheric dispersion simulation and (2) a class of Bayesian inference and uncertainty quantification algorithms. We use two different and complementary approaches to simulate atmospheric dispersion. Specifically, we use a Eulerian chemical transport model CMAQ and a Lagrangian Particle Dispersion Model-FLEXPART-WRF. These two models share the same WRF assimilated meteorology fields, making it possible to perform a hybrid simulation, in which the Eulerian model (CMAQ) can be used to compute the initial condition needed by the Lagrangian model, while the source-receptor relationships for a large state We are very grateful to our colleagues and collaborators, Drs.

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Comparing Lagrangian and Eulerian models for CO<sub>2</sub> transport – a step towards Bayesian inverse modeling using WRF/STILT-VPRM

Cited by 7 publications

References 28 publications

Investigating seasonal methane emissions in Northern California using airborne measurements and inverse modeling

Investigating seasonal methane emissions in Northern California using airborne measurements and inverse modeling

Top‐Down Constraints on Anthropogenic CO₂ Emissions Within an Agricultural‐Urban Landscape

Greenhouse Gas Source Attribution: Measurements Modeling and Uncertainty Quantification

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Comparing Lagrangian and Eulerian models for CO&lt;sub&gt;2&lt;/sub&gt; transport – a step towards Bayesian inverse modeling using WRF/STILT-VPRM

Cited by 7 publications

References 28 publications

Investigating seasonal methane emissions in Northern California using airborne measurements and inverse modeling

Investigating seasonal methane emissions in Northern California using airborne measurements and inverse modeling

Top‐Down Constraints on Anthropogenic CO2 Emissions Within an Agricultural‐Urban Landscape

Greenhouse Gas Source Attribution: Measurements Modeling and Uncertainty Quantification

Contact Info

Product

Resources

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Comparing Lagrangian and Eulerian models for CO<sub>2</sub> transport – a step towards Bayesian inverse modeling using WRF/STILT-VPRM

Top‐Down Constraints on Anthropogenic CO₂ Emissions Within an Agricultural‐Urban Landscape