2019
DOI: 10.5194/acp-2019-880
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Inverse modeling of SO<sub>2</sub> and NO<sub><i>x</i></sub> emissions over China using multi-sensor satellite data: 2. Downscaling techniques for air quality analysis and forecasts

Abstract: Abstract. Top-down emissions estimates provide valuable up-to-date information on pollution sources; however, the computational effort involved with developing these emissions often requires them to be estimated at resolutions that are much coarser than is necessary for regional air-quality forecasting. This work thus introduces several approaches to downscaling coarse-resolution (2° × 2.5°) posterior SO2 and NOx emissions (derived through inverse modeling in Part I of this study) for improving air quality ass… Show more

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Cited by 3 publications
(6 citation statements)
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“…Retrievals of NO 2 from satellite ultraviolet-visible (UV-Vis) spectral measurements are powerful tools for the characterization of the spatiotemporal variation of NO x . Benchmarked by the Global Ozone Monitoring Experiment (GOME) instrument onboard the European Remote Sensing Satellite (ERS-2), global monitoring of tropospheric NO 2 has been possible since 1995 [12][13][14], which boosted the investigations of spatial variability and long-term changes of NO 2 abundance [15,16], various NO x emission from anthropogenic [17][18][19][20][21] and natural sources [22,23], NO x lifetime [24,25], etc. Moreover, a global geostationary constellation of NO 2 monitoring is emerging [26][27][28], with hourly and km-scale monitoring capabilities to further facilitate the investigation of diurnal variability of NO x -relevant processes.…”
Section: Introductionmentioning
confidence: 99%
“…Retrievals of NO 2 from satellite ultraviolet-visible (UV-Vis) spectral measurements are powerful tools for the characterization of the spatiotemporal variation of NO x . Benchmarked by the Global Ozone Monitoring Experiment (GOME) instrument onboard the European Remote Sensing Satellite (ERS-2), global monitoring of tropospheric NO 2 has been possible since 1995 [12][13][14], which boosted the investigations of spatial variability and long-term changes of NO 2 abundance [15,16], various NO x emission from anthropogenic [17][18][19][20][21] and natural sources [22,23], NO x lifetime [24,25], etc. Moreover, a global geostationary constellation of NO 2 monitoring is emerging [26][27][28], with hourly and km-scale monitoring capabilities to further facilitate the investigation of diurnal variability of NO x -relevant processes.…”
Section: Introductionmentioning
confidence: 99%
“…and Sentinel-4 (monitoring Europe) are to be launched in the next several years, and all of these satellites will provide hourly SO2 and NO2 observations during the daytime. Furthermore, in Part II of this work, we develop various downscale methods to apply these coarser-resolution top-down estimates of emissions for air quality forecasts and evaluate the forecasts with surface measurements, both at the finer spatial scale (Wang et al, 2019).…”
Section: Discussionmentioning
confidence: 99%
“…All these experiments use OMPS SO2 and NO2 retrievals to optimize corresponding emissions over China in October 2013 at a horizontal resolution of 2°x2.5°. Although finer resolution options such as 0.5°x0.625° or 0.25°x0.3125° are available for China, the 2°x2.5° resolution is selected to save computational time; in Part II (Wang et al, 2019) of this study, we develop downscaling tools for regional air quality modeling.…”
Section: Experiments Designmentioning
confidence: 99%
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“…Existing downscaling methods include dynamical downscaling and statistical downscaling. Dynamical downscaling simulates using high-resolution physical local-area models based on low-resolution boundary conditions; however, it is computational demanding (Hong et al, 2016;Yahya et al, 2017;Wang et al, 2020). Statistical downscaling trains linear or nonlinear statistical models to estimate high-resolution information, but the downscaled variable is generally the same as the lowresolution origin (Zhu et al, 2016;Ahmed et al, 2018;Oteros et al, 2019;Khan et al, 2019).…”
Section: Preprintmentioning
confidence: 99%