Assessment of an atmospheric transport model for annual inverse estimates of California greenhouse gas emissions

Bagley, Justin E.; Jeong, Seongeun; Cui, Xinguang; Newman, Sally; Zhang, Jingsong; Priest, Chad; Campos‐Pineda, Mixtli; Andrews, Arlyn E.; Bianco, Laura; Lloyd, Matthew; Lareau, Neil; Clements, Craig; Fischer, Marc L.

doi:10.1002/2016jd025361

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The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.

i

Assessment of an atmospheric transport model for annual inverse estimates of California greenhouse gas emissions

2017
By Bagley, Justin E. ; Jeong, Seongeun ; Cui, Xinguang ; ...
Source: Journal of Geophysical Research-Atmospheres, 122(3), 1901-1918.

[PDF-4.95 MB]

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Journal Title:

Journal of Geophysical Research: Atmospheres
Personal Author:

Bagley, Justin E. ; Jeong, Seongeun ; Cui, Xinguang ; Newman, Sally ; Zhang, Jingsong ; ... More +

Bagley, Justin E. ; Jeong, Seongeun ; Cui, Xinguang ; Newman, Sally ; Zhang, Jingsong ; Priest, Chad ; Campos‐Pineda, Mixtli ; Andrews, Arlyn E. ; Bianco, Laura ; Lloyd, Matthew ; Lareau, Neil ; Clements, Craig ; Fischer, Marc L. Less -
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; PSL (Physical Sciences Laboratory) ; ESRL (Earth System Research Laboratory)

OAR (Oceanic and Atmospheric Research) ; PSL (Physical Sciences Laboratory) ; ESRL (Earth System Research Laboratory) Less -
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Atmospheric inverse estimates of gas emissions depend on transport model predictions, hence driving a need to assess uncertainties in the transport model. In this study we assess the uncertainty in WRF-STILT (Weather Research and Forecasting and Stochastic Time-Inverted Lagrangian Transport) model predictions using a combination of meteorological and carbon monoxide (CO) measurements. WRF configurations were selected to minimize meteorological biases using meteorological measurements of winds and boundary layer depths from surface stations and radar wind profiler sites across California. We compare model predictions with CO measurements from four tower sites in California from June 2013 through May 2014 to assess the seasonal biases and random errors in predicted CO mixing ratios. In general, the seasonal mean biases in boundary layer wind speed (< similar to 0.5 m/s), direction (< similar to 15 degrees), and boundary layer height (< similar to 200 m) were small. However, random errors were large (similar to 1.5-3.0 m/s for wind speed, similar to 40-60 degrees for wind direction, and similar to 300-500m for boundary layer height). Regression analysis of predicted and measured CO yielded near-unity slopes (i. e., within 1.0 +/- 0.20) for the majority of sites and seasons, though a subset of sites and seasons exhibit larger (similar to 30%) uncertainty, particularly when weak winds combined with complex terrain in the South Central Valley of California. Looking across sites and seasons, these results suggest that WRF-STILT simulations are sufficient to estimate emissions of CO to up to 15% on annual time scales across California.
Source:

Journal of Geophysical Research-Atmospheres, 122(3), 1901-1918.
DOI:

https://doi.org/10.1002/2016jd025361
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Journal Article
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urn:sha256:8f89e0f9439e8d10cd5677830dd449c278e506f938191ff29760c6de129cee38
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Assessment of an atmospheric transport model for annual inverse estimates of California greenhouse gas emissions

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