English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

. Urban regions are responsible for emitting significant amounts of fossil fuelcarbon dioxide ( FFCO2 ), and emissions at the finer, city scales are moreuncertain than those aggregated at the global scale. Carbon-observingsatellites may provide independent top-down emission evaluations andcompensate for the sparseness of surface CO2 observing networks in urbanareas. Although some previous studies have attempted to derive urban CO2 signals from satellite column-averaged CO2 data ( XCO2 ) using simplestatistical measures, less work has been carried out to link upwind emissionsources to downwind atmospheric columns using atmospheric models. In additionto Eulerian atmospheric models that have been customized for emissionestimates over specific cities, the Lagrangian modeling approach – inparticular, the Lagrangian particle dispersion model (LPDM) approach – hasthe potential to efficiently determine the sensitivity of downwindconcentration changes to upwind sources. However, when applying LPDMs tointerpret satellite XCO2 , several issues have yet to be addressed,including quantifying uncertainties in urban XCO2 signals due toreceptor configurations and errors in atmospheric transport and background XCO2 . In this study, we present a modified version of the Stochastic Time-InvertedLagrangian Transport (STILT) model, “X-STILT”, for extracting urban XCO2 signals from NASA's Orbiting Carbon Observatory 2 (OCO-2) XCO2 data. X-STILT incorporates satellite profiles and providescomprehensive uncertainty estimates of urban XCO2 enhancements ona per sounding basis. Several methods to initialize receptor/particlesetups and determine background XCO2 are presented and discussed viasensitivity analyses and comparisons. To illustrate X-STILT's utilities andapplications, we examined five OCO-2 overpasses over Riyadh, Saudi Arabia,during a 2-year time period and performed a simple scaling factor-basedinverse analysis. As a result, the model is able to reproduce most observed XCO2 enhancements. Error estimates show that the 68 % confidencelimit of XCO2 uncertainties due to transport (horizontal wind plusvertical mixing) and emission uncertainties contribute to ∼33  %and ∼20  % of the mean latitudinally integrated urbansignals, respectively, over the five overpasses, using meteorological fieldsfrom the Global Data Assimilation System (GDAS). In addition, a sizeablemean difference of −0.55  ppm in background derived from a previous studyemploying simple statistics (regional daily median) leads to a ∼39  % higher meanobserved urban signal and a larger posteriorscaling factor. Based on our signal estimates and associated error impacts,we foresee X-STILT serving as a tool for interpreting column measurements,estimating urban enhancement signals, and carrying out inverse modeling toimprove quantification of urban emissions.

geoscientific model development

A Lagrangian approach towards extracting signals of urban CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; emissions from satellite observations of atmospheric column CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; (XCO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;): X-Stochastic Time-Inverted Lagrangian Transport model (“X-STILT v1”)