A GIS-based assessment of the suitability of SCIAMACHY satellite sensor measurements for estimating reliable CO concentrations in a low-latitude climate

Fagbeja, Mofoluso; Hill, Jennifer; Chatterton, Tim; Longhurst, James

doi:10.1007/s10661-014-4227-2

Cited by 1 publication

(1 citation statement)

References 38 publications

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“…CO is not only measured in surface but also at different altitudes employing remote sensing, such as satellite measurements from Measurement of Pollution in the Troposphere (MOPITT), Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), Infrared Atmospheric Sounding Interferometer (IASI), among others (Clerbaux et al, 2008a;Turquety et al, 2008;Luo et al, 2007). These observations can also provide information of CO surface concentrations and its transport over polluted urban areas (Clerbaux et al, 2008b;Fortems-Cheiney et al, 2009;Fagbeja et al, 2015;Rakitin et al, 2015;Bauduin et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Carbon monoxide emissions assessment by using satellite and modeling data: Central Mexico case study

Maldonado-Pacheco

García-Reynoso

Stremme

et al. 2021

ATM

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This paper quantifies and reduces the differences in emissions from the 2008 inventory with respect to the real ones through the use of satellite observations and modeling. Carbon monoxide column comparisons from the Infrared Atmospheric Sounding Interferometer (IASI) satellite data were made against columns obtained from the WRF-Chem model, during the February month 2011. The analysis was carried out at the satellite passage local time (approximately 10:00 a.m.) over Mexico City. The 2008 national emissions inventory generated by the Ministry of Environment and Natural Resources (SEMARNAT) was employed. An inversion method was applied with the modeled and observed column data. With the above, scaling factors were obtained for 5 regions and the concentration from the model domain boundaries, which were used to update the emissions. The updated emissions were employed in modeling and the result was compared with surface measurements. For Mexico City and the Metropolitan Area, a scaling factor equal to 0.43 was obtained when using the 2008 emissions inventory; For Toluca, Morelos and Puebla, a less than one factor was estimated, while for Hidalgo and the concentration from model boundaries were close to two. The model performance was improved by an increment in the agreement index and a reduction on the mean square error when the updated CO emissions were used. initial results. Geophysical Research Letters, 33: L22806.

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