Showing papers by "Tomohiro Oda published in 2018"

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

Abstract: . Urban regions are responsible for emitting significant amounts of fossil fuel carbon dioxide ( FFCO2 ), and emissions at the finer, city scales are more uncertain than those aggregated at the global scale. Carbon-observing satellites may provide independent top-down emission evaluations and compensate for the sparseness of surface CO2 observing networks in urban areas. Although some previous studies have attempted to derive urban CO2 signals from satellite column-averaged CO2 data ( XCO2 ) using simple statistical measures, less work has been carried out to link upwind emission sources to downwind atmospheric columns using atmospheric models. In addition to Eulerian atmospheric models that have been customized for emission estimates over specific cities, the Lagrangian modeling approach – in particular, the Lagrangian particle dispersion model (LPDM) approach – has the potential to efficiently determine the sensitivity of downwind concentration changes to upwind sources. However, when applying LPDMs to interpret satellite XCO2 , several issues have yet to be addressed, including quantifying uncertainties in urban XCO2 signals due to receptor configurations and errors in atmospheric transport and background XCO2 . In this study, we present a modified version of the Stochastic Time-Inverted Lagrangian 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 provides comprehensive uncertainty estimates of urban XCO2 enhancements on a per sounding basis. Several methods to initialize receptor/particle setups and determine background XCO2 are presented and discussed via sensitivity analyses and comparisons. To illustrate X-STILT's utilities and applications, we examined five OCO-2 overpasses over Riyadh, Saudi Arabia, during a 2-year time period and performed a simple scaling factor-based inverse analysis. As a result, the model is able to reproduce most observed XCO2 enhancements. Error estimates show that the 68 % confidence limit of XCO2 uncertainties due to transport (horizontal wind plus vertical mixing) and emission uncertainties contribute to ∼33 % and ∼20 % of the mean latitudinally integrated urban signals, respectively, over the five overpasses, using meteorological fields from the Global Data Assimilation System (GDAS). In addition, a sizeable mean difference of −0.55 ppm in background derived from a previous study employing simple statistics (regional daily median) leads to a ∼39 % higher mean observed urban signal and a larger posterior scaling 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 to improve quantification of urban emissions.

Southern California megacity CO 2 , CH 4 , and CO flux estimates using ground- and space-based remote sensing and a Lagrangian model

Abstract: . We estimate the overall CO2 , CH4 , and CO flux from the South Coast Air Basin using an inversion that couples Total Carbon Column Observing Network (TCCON) and Orbiting Carbon Observatory-2 (OCO-2) observations, with the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and the Open-source Data Inventory for Anthropogenic CO2 (ODIAC). Using TCCON data we estimate the direct net CO2 flux from the SoCAB to be 104 ± 26 Tg CO2 yr −1 for the study period of July 2013–August 2016. We obtain a slightly higher estimate of 120 ± 30 Tg CO2 yr −1 using OCO-2 data. These CO2 emission estimates are on the low end of previous work. Our net CH4 (360 ± 90 Gg CH4 yr −1 ) flux estimate is in agreement with central values from previous top-down studies going back to 2010 (342–440 Gg CH4 yr −1 ). CO emissions are estimated at 487 ± 122 Gg CO yr −1 , much lower than previous top-down estimates (1440 Gg CO yr −1 ). Given the decreasing emissions of CO, this finding is not unexpected. We perform sensitivity tests to estimate how much errors in the prior, errors in the covariance, different inversion schemes, or a coarser dynamical model influence the emission estimates. Overall, the uncertainty is estimated to be 25 %, with the largest contribution from the dynamical model. Lessons learned here may help in future inversions of satellite data over urban areas.

Editorial: Data publication – ESSD goals, practices and recommendations

Abstract: . Earth System Science Data (ESSD) provides a wide range of openly accessible, high-quality, well-documented and highly useful data products while ensuring recognition of and credit to data providers. As authors, reviewers, and editors of many ESSD publications, we encounter uncertainty about mechanisms and requirements for open access, about what constitutes a published data product, and about how one goes about submitting, evaluating or using ESSD products. With this short note, published during an important editorial transition, we use our combined experience to define guidelines, requirements and benefits of the ESSD processes.

Southern California Megacity CO 2 , CH 4 , and CO flux estimatesusing remote sensing and a Lagrangian model

Abstract: . We estimate the overall CO 2 , CH 4 , and CO flux from the South Coast Air Basin using an inversion that couples Total Carbon Column Observing Network (TCCON) and Orbiting Carbon Observatory-2 (OCO-2) observations, with the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model, and the Open-source Data Inventory for Anthropogenic CO 2 (ODIAC). Using TCCON data we estimate the direct net CO 2 flux from the SoCAB to be 139 ± 35 Tg CO 2 yr −1 for the study period of July 2013–August 2016. We obtain a slightly lower estimate of 118 ± 29 Tg CO 2 yr −1 using OCO-2 data. These CO 2 emission estimates are in general agreement with previous work. Our net CH 4 (325 ± 81 Gg CH 4 yr −1 ) flux estimate is slightly lower than central values from previous top-down studies going back to 2010 (342–440 Gg CH 4 yr −1 ). CO emissions are estimated at 555 ± 136 Gg CO yr −1 , much lower than previous top-down estimates (1440 Gg CO yr −1 ). Given the decreasing emissions of CO, this finding is not unexpected. We perform sensitivity tests to estimate how much errors in the prior, errors in the covariance, different inversions schemes or a coarser dynamical model influence the emission estimates. Overall, the uncertainty is estimated to be 25 %, with the largest contribution from the dynamical model. The methods described are scalable and can be used to estimate direct net CO 2 fluxes from other urban regions.