scholarly journals Improving air quality forecasting with the assimilation of GOCI aerosol optical depth (AOD) retrievals during the KORUS-AQ period

2020 ◽  
Vol 20 (10) ◽  
pp. 6015-6036
Author(s):  
Soyoung Ha ◽  
Zhiquan Liu ◽  
Wei Sun ◽  
Yonghee Lee ◽  
Limseok Chang
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
South Korea ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Three Dimensional ◽  
Upstream Region ◽  
Temporal And Spatial Distribution ◽  
The Impact ◽  
Air Quality Forecasting

Abstract. The Korean Geostationary Ocean Color Imager (GOCI) satellite has monitored the East Asian region in high temporal (e.g., hourly) and spatial resolution (e.g., 6 km) every day for the last decade, providing unprecedented information on air pollutants over the upstream region of the Korean Peninsula. In this study, the GOCI aerosol optical depth (AOD), retrieved at the 550 nm wavelength, is assimilated to enhance the quality of the aerosol analysis, thereby making systematic improvements to air quality forecasting over South Korea. For successful data assimilation, GOCI retrievals are carefully investigated and processed based on data characteristics such as temporal and spatial distribution. The preprocessed data are then assimilated in the three-dimensional variational data assimilation (3D-Var) technique for the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). For the Korea–United States Air Quality (KORUS-AQ) period (May 2016), the impact of GOCI AOD on the accuracy of surface PM2.5 prediction is examined by comparing with effects of other observations including Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and surface PM2.5 observations. Consistent with previous studies, the assimilation of surface PM2.5 measurements alone still underestimates surface PM2.5 concentrations in the following forecasts, and the forecast improvements last only for about 6 h. When GOCI AOD retrievals are assimilated with surface PM2.5 observations, however, the negative bias is diminished and forecast skills are improved up to 24 h, with the most significant contributions to the prediction of heavy pollution events over South Korea.

Improving air quality forecasting with the assimilation of GOCI AOD retrievals

2020 ◽  
Author(s):  
Soyoung Ha ◽  
Zhiquan Liu
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
South Korea ◽  
Positive Impact ◽  
Fine Particulate Matter ◽  
Three Dimensional ◽  
Basis Set ◽  
Upstream Region ◽  
The Impact ◽  
Air Quality Forecasting

<p>The Korean Geostationary Ocean Color Imager (GOCI) satellite has monitored the East Asian region in high temporal and spatial resolution every day for the last decade, providing unprecedented information on air pollutants over the upstream region of the Korean peninsula. In this study, the GOCI Aerosol optical depth (AOD), retrieved at 550 nm wavelength, is assimilated to ameliorate the analysis quality, thereby making systematic improvements on air quality forecasting in South Korea. For successful data assimilation, GOCI retrievals are carefully investigated and processed based on data characteristics. The preprocessed data are then assimilated in the three-dimensional variational data assimilation (3DVAR) technique for the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). Over the Korea-United States Air Quality (KORUS-AQ) period (May 2016), the impact of GOCI AOD on the accuracy of air quality forecasting is examined by comparing with other observations including Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and fine particulate matter (PM2.5) observations at the surface. Consistent with previous studies, the assimilation of surface PM2.5 concentrations alone systematically underestimates surface PM2.5 and its positive impact lasts mainly for about 6 h. When GOCI AOD retrievals are assimilated with surface PM2.5 observations, however, the negative bias is diminished and forecasts are improved up to 24 h, with the most significant contributions to the prediction of heavy pollution events over South Korea. The talk will be finished with an introduction of our ongoing efforts on developing the assimilation capability for more sophisticated aerosol schemes such as Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) and the Modal Aerosol Dynamics Model for Europe (MADE)-Volatility basis set (VBS).</p>

scholarly journals Improving air quality forecasting with the assimilation of GOCI AOD retrievals during the KORUS-AQ period

2019 ◽  
Author(s):  
Soyoung Ha ◽  
Zhiquan Liu ◽  
Wei Sun ◽  
Yonghee Lee ◽  
Limseok Chang
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
South Korea ◽  
Positive Impact ◽  
Fine Particulate Matter ◽  
Three Dimensional ◽  
Upstream Region ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Impact ◽  
Air Quality Forecasting

Abstract. The Korean Geostationary Ocean Color Imager (GOCI) satellite has monitored the East Asian region in high temporal and spatial resolution every day, providing unprecedented information on air pollutants over the upstream region of the Korean peninsula for the last decade. In this study, the GOCI Aerosol optical depth (AOD), retrieved at 550 nm wavelength, is assimilated to ameliorate the analysis quality, thereby making systematic improvements on air quality forecasting in South Korea. For successful data assimilation, GOCI retrievals are carefully investigated and processed based on data characteristics. The preprocessed data are then assimilated in the three-dimensional variational data assimilation (3DVAR) technique for the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). During the Korea-United States Air Quality (KORUS-AQ) period (May 2016), the impact of GOCI AOD on the accuracy of air quality forecasting is examined by comparing with other observations including Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and fine particulate matter (PM2.5) observations at the surface. Consistent with previous studies, the assimilation of surface PM2.5 concentrations alone systematically underestimates surface PM2.5 and its positive impact lasts mainly for about 6 h. When GOCI AOD retrievals are assimilated with surface PM2.5 observations, however, the negative bias is diminished and forecasts are improved up to 24 h, with the most significant contributions to the prediction of heavy pollution events over South Korea.

scholarly journals The impact of data assimilation on the prediction of Asian desert dust using an operational 4D-Var system

2018 ◽  
Author(s):  
Angela Benedetti ◽  
Francesca Di Giuseppe ◽  
Luke Jones ◽  
Vincent-Henri Peuch ◽  
Samuel Rémy ◽  
...  
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Asian Dust ◽  
Added Value ◽  
Desert Dust ◽  
Deep Blue ◽  
Dust Events ◽  
The Impact

Abstract. Asian Dust is a seasonal meteorological phenomenon which affects East Asia, and has severe consequences on the air quality of China, North and South Korea and Japan. Despite the continental extent, the prediction of severe episodes and the anticipation of their consequences is challenging. Three one-year experiments were run to assess the skill of the model of the European Centre for Medium-Range Weather Forecasts (ECMWF) in monitoring Asian dust and understand its relative contribution to air quality over China. Data used were the MODIS Dark Target and the Deep Blue Aerosol Optical Depth. In particular the experiments aimed at understanding the added value of data assimilation runs over a model run without any aerosol data. The year 2013 was chosen as representative for the availability of independent Aerosol Optical Depth (AOD) data from two established ground-based networks (AERONET and CARSNET), which could be used to evaluate experiments. Particulate Matter (PM) data from the China Environmental Protection Agency (CEPA) were also used in the evaluation. Results show that the assimilation of satellite AOD data is beneficial to predict the extent and magnitude of desert-dust events and to improve the forecast of such events. The availability of observations from the MODIS Deep Blue algorithm over bright surfaces is an asset, allowing for a better localization of the sources and definition of the dust events. In general both experiments constrained by data assimilation perform better that the unconstrained experiment, generally showing smaller mean normalized bias and fractional gross error with respect to the independent verification datasets. The impact of the assimilated satellite observations is larger at analysis time, but lasts well into the forecast. While assimilation is not a substitute for model development and characterization of the emission sources, results indicate that it can play a big role in delivering improved forecasts of Asian Dust.

scholarly journals Monitoring the Spatial Variation of Aerosol Optical Depth and Its Correlation with Land Use/Land Cover in Wuhan, China: A Perspective of Urban Planning

2021 ◽  
Vol 18 (3) ◽  
pp. 1132
Author(s):  
Qijiao Xie ◽  
Qi Sun
Keyword(s):  
Land Use ◽  
Air Quality ◽  
Urban Planning ◽  
Land Cover ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Water Bodies ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
The Impact

Aerosols significantly affect environmental conditions, air quality, and public health locally, regionally, and globally. Examining the impact of land use/land cover (LULC) on aerosol optical depth (AOD) helps to understand how human activities influence air quality and develop suitable solutions. The Landsat 8 image and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products in summer in 2018 were used in LULC classification and AOD retrieval in this study. Spatial statistics and correlation analysis about the relationship between LULC and AOD were performed to examine the impact of LULC on AOD in summer in Wuhan, China. Results indicate that the AOD distribution expressed an obvious “basin effect” in urban development areas: higher AOD values concentrated in water bodies with lower terrain, which were surrounded by the high buildings or mountains with lower AOD values. The AOD values were negatively correlated with the vegetated areas while positively correlated to water bodies and construction lands. The impact of LULC on AOD varied with different contexts in all cases, showing a “context effect”. The regression correlations among the normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference water index (NDWI), and AOD in given landscape contexts were much stronger than those throughout the whole study area. These findings provide sound evidence for urban planning, land use management and air quality improvement.

The impact of Sahara dust on air quality and public health in European countries

2021 ◽  
Author(s):  
Qiaoqiao Wang ◽  
Jianwei Gu ◽  
Xurong Wang
Keyword(s):  
Air Quality ◽  
Health Effects ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Health Effect ◽  
Population Attributable Fraction ◽  
European Countries ◽  
Dust Exposure ◽  
The Impact ◽  
Sahara Dust

<p>The frequent transport of Sahara dust toward Europe degrades the air quality and poses risk to human health. In this study we use GEOS-Chem (a global transport model) to examine the impact of Sahara dust on air quality and the consequent health effect in Europe for the year 2016–2017. The simualtion is conducted in a nested model with the native resolution of 0.25° × 0.3125° (Latitude × Logitude) over Europe (32.75°N–61.25°N, 15°W–40°E). The simulation on a global scale with a coarse horizontal resolution of 2° × 2.5° is also conducted to provide the boundary condition for the nested-grid simulation as well as aerosol optical depth (AOD) over the Sahara desert for model evaluation.</p><p>The model performance is evaluated by comparisons with surface observations including aerosol optical depth (AOD) from AERONET, and PM<sub>2.5</sub> and PM<sub>10</sub> concentrations from numerous air quality monitoring stations in European countries. Overall, the model well reproduces observed surface PM concentrations over most European countries with some underestimation in southern Europe. In addition, model AOD is highly correlated with AERONET data over both Sahara and European region.</p><p>The spatial distribution of dust concentrations, frequency of dust episodes, as well as the exposure and health effects are studied. The concentrations of Sahara dust decrease from 5–20 μg m<sup>-3</sup> in south to 0.5–1.0 μg m<sup>-3</sup> in north of Europe. Spain and Italy are most heavily influenced by Sahara dust in terms of both concentration levels and frequencies of occurrence. Strong dust episodes (>50 μg m<sup>-3</sup>) occur predominately in Southern Spain and Italy with frequency of 2–5%, while light dust episodes (>1 μg m<sup>-3</sup>) are often detected (5–30%) in Central and Western Europe.</p><p>The population-weighted dust concentrations are higher in Southern European countries (3.3–7.9 μg m<sup>-3</sup>) and lower in Western European countries (0.5–0.6 μg m<sup>-3</sup>). The health effects of exposure to dust is evaluated based on population attributable fraction (PAF). We use the relative risk (RR) value of 1.04 (95% confidence intervals: 1.00 – 1.09) per 10 µg m<sup>-3 </sup>of dust exposure based on the main model of Beelen et al. (2014). We estimate a total of 41884 (95% CI: 2110–81658) deaths per year attributed to the exposure to dust in the 13 European countries studied. Due to high contribution to PM<sub>10</sub> in Spain, Italy and Portugal, dust accounts for 44%, 27% and 22% of the total number of deaths linked to PM<sub>10</sub> exposure, respectively.</p>

scholarly journals Impact of Atmospheric and Aerosol Optical Depth Observations on Aerosol Initial Conditions in a strongly-coupled data assimilation system

2019 ◽  
Author(s):  
Milija Zupanski ◽  
Anton Kliewer ◽  
Ting-Chi Wu ◽  
Karina Apodaca ◽  
Qijing Bian ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Initial Conditions ◽  
Positive Impact ◽  
Storm Event ◽  
Strongly Coupled ◽  
Atmospheric Observations ◽  
Coupled Data Assimilation ◽  
The Impact

Abstract. Strongly coupled data assimilation frameworks provide a mechanism for including additional information about aerosols through the coupling between aerosol and atmospheric variables, effectively utilizing atmospheric observations to change the aerosol analysis. Here, we investigate the impact of these observations on aerosol using the Maximum Likelihood Ensemble Filter (MLEF) algorithm with Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) which includes the Godard Chemistry Aerosol Radiation and Transport (GOCART) module. We apply this methodology to a dust storm event over the Arabian Peninsula and examine in detail the error covariance and in particular the impact of atmospheric observations on improving the aerosol initial conditions. The assimilated observations include conventional atmospheric observations and Aerosol Optical Depth (AOD) retrievals. Results indicate a positive impact of using strongly coupled data assimilation and atmospheric observations on the aerosol initial conditions, quantified using Degrees of Freedom for Signal.

scholarly journals Constraining chemical transport PM<sub>2.5</sub> modeling outputs using surface monitor measurements and satellite retrievals: application over the San Joaquin Valley

2018 ◽  
Vol 18 (17) ◽  
pp. 12891-12913 ◽  
Author(s):  
Mariel D. Friberg ◽  
Ralph A. Kahn ◽  
James A. Limbacher ◽  
K. Wyat Appel ◽  
James A. Mulholland
Keyword(s):  
Air Quality ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Cross Validation ◽  
Regional Scale ◽  
Chemical Transport ◽  
Near Surface ◽  
Air Mass ◽  
Uncertainty Estimates ◽  
The Impact

Abstract. Advances in satellite retrieval of aerosol type can improve the accuracy of near-surface air quality characterization by providing broad regional context and decreasing metric uncertainties and errors. The frequent, spatially extensive and radiometrically consistent instantaneous constraints can be especially useful in areas away from ground monitors and progressively downwind of emission sources. We present a physical approach to constraining regional-scale estimates of PM2.5, its major chemical component species estimates, and related uncertainty estimates of chemical transport model (CTM; e.g., the Community Multi-scale Air Quality Model) outputs. This approach uses ground-based monitors where available, combined with aerosol optical depth and qualitative constraints on aerosol size, shape, and light-absorption properties from the Multi-angle Imaging SpectroRadiometer (MISR) on the NASA Earth Observing System's Terra satellite. The CTM complements these data by providing complete spatial and temporal coverage. Unlike widely used approaches that train statistical regression models, the technique developed here leverages CTM physical constraints such as the conservation of aerosol mass and meteorological consistency, independent of observations. The CTM also aids in identifying relationships between observed species concentrations and emission sources.Aerosol air mass types over populated regions of central California are characterized using satellite data acquired during the 2013 San Joaquin field deployment of the NASA Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) project. We investigate the optimal application of incorporating 275 m horizontal-resolution aerosol air-mass-type maps and total-column aerosol optical depth from the MISR Research Aerosol retrieval algorithm (RA) into regional-scale CTM output. The impact on surface PM2.5 fields progressively downwind of large single sources is evaluated using contemporaneous surface observations. Spatiotemporal R2 and RMSE values for the model, constrained by both satellite and surface monitor measurements based on 10-fold cross-validation, are 0.79 and 0.33 for PM2.5, 0.88 and 0.65 for NO3−, 0.78 and 0.23 for SO42−, 1.00 and 1.01 for NH4+, 0.73 and 0.23 for OC, and 0.31 and 0.65 for EC, respectively. Regional cross-validation temporal and spatiotemporal R2 results for the satellite-based PM2.5 improve by 30 % and 13 %, respectively, in comparison to unconstrained CTM simulations and provide finer spatial resolution. SO42− cross-validation values showed the largest spatial and spatiotemporal R2 improvement, with a 43 % increase. Assessing this physical technique in a well-instrumented region opens the possibility of applying it globally, especially over areas where surface air quality measurements are scarce or entirely absent.

scholarly journals Constraining Chemical Transport PM<sub>2.5</sub> Modeling Using Surface Monitor Measurements and Satellite Retrievals: Application over the San Joaquin Valley

2018 ◽  
Author(s):  
Mariel D. Friberg ◽  
Ralph A. Kahn ◽  
James A. Limbacher ◽  
K. Wyat Appel ◽  
James A. Mulholland
Keyword(s):  
Air Quality ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Satellite Data ◽  
Cross Validation ◽  
Regional Scale ◽  
Chemical Transport ◽  
Emission Sources ◽  
Near Surface ◽  
The Impact

Abstract. Advances in satellite retrieval of aerosol type can improve the accuracy of near-surface air quality characterization, by providing broad regional context. In addition to aerosol optical depth, qualitative constraints on aerosol size, shape, and single-scattering albedo provided by multi-angle instruments, such as the Multi-angle Imaging SpectroRadiometer (MISR) on the NASA Earth Observing System’s Terra satellite, can provide frequent, spatially extensive, instantaneous constraints on chemical transport models (CTMs), which can be especially useful in areas away from ground monitors and progressively downwind of emission sources. CTMs (e.g. the Community Multi-scale Air Quality Modeling System) complement such data by providing complete spatial and temporal coverage, offering additional physical constraints (e.g., conservation of aerosol mass, meteorological consistency) independent of observations, and aid in identifying relationships between observed species concentrations and emission sources. Incorporating satellite aerosol information in the development of PM2.5 concentration metrics can lead to a decrease in metric uncertainties and errors. This work focuses on the degree to which regional-scale satellite and CTM data can be combined to improve surface estimates of PM2.5, its major chemical component species estimates, and related estimates of uncertainty. Aerosol airmass types over populated regions of Southern California are characterized using satellite data acquired during the 2013 San Joaquin field deployment of the NASA DISCOVER-AQ project. Using the MISR Research Aerosol retrieval algorithm (RA), we investigate and evaluate the optimal application of incorporating 275 m horizontal-resolution aerosol airmass-type maps and total-column aerosol optical depth into a 2 km resolution, regional-scale CTM output, to obtain constrained fields of surface PM2.5. Contemporaneous surface observations are used to evaluate the results. The impact of incorporating MISR aerosol data on the ability to characterize air quality progressively downwind of large single sources is discussed. The spatiotemporal R2 values for the model, constrained by both satellite and surface-monitor measurements based on 10 % withholding, are 0.79 for PM2.5, 0.88 for NO3−, 0.78 for SO42−, 1.00 for NH4+, 0.73 for OC, and 0.31 for EC. Regional cross-validation temporal and spatiotemporal R2 results for the satellite-based PM2.5 improve by 30 % and 13 %, respectively, in comparison to CTM simulations, and provide finer spatial resolution. SO42− cross-validation values showed the largest spatial and spatiotemporal R2 improvement with a 43 % increase. Assessing this technique in a well-instrumented region opens the possibility of using the satellite data to apply the technique globally.

scholarly journals The Impact of the Direct Effect of Aerosols on Meteorology and Air Quality Using Aerosol Optical Depth Assimilation During the KORUS‐AQ Campaign

2019 ◽  
Vol 124 (14) ◽  
pp. 8303-8319 ◽  
Author(s):  
Jia Jung ◽  
Amir H. Souri ◽  
David C. Wong ◽  
Sojin Lee ◽  
Wonbae Jeon ◽  
...  
Keyword(s):  
Air Quality ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Direct Effect ◽  
The Impact

scholarly journals The impact of aerosol optical depth assimilation on aerosol forecasts and radiative effects during a wild fire event over the United States

2014 ◽  
Vol 7 (3) ◽  
pp. 3851-3866
Author(s):  
D. Chen ◽  
Z. Liu ◽  
C. S. Schwartz ◽  
H.-C. Lin ◽  
J. D. Cetola ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
The United States ◽  
Aerosol Layer ◽  
Radiative Effects ◽  
Fire Event ◽  
Fire Emissions ◽  
Wild Fire ◽  
The Impact

Abstract. The Gridpoint Statistical Interpolation three-dimensional variational data assimilation (DA) system coupled with the Weather Research and Forecasting/Chemistry (WRF/Chem) model was utilized to improve aerosol forecasts and study aerosol direct and semi-direct radiative feedbacks during a US wild fire event. Assimilation of MODIS total 550 nm aerosol optical depth (AOD) retrievals clearly improved WRF/Chem forecasts of surface PM2.5 and organic carbon (OC) compared to the corresponding forecasts without aerosol data assimilation. The scattering aerosols in the fire downwind region typically cooled layers both above and below the aerosol layer and suppressed convection and clouds, which led to an average 2% precipitation decease during the fire week. This study demonstrated that even with no input of fire emissions, AOD DA improved the aerosol forecasts and allowed a more realistic model simulation of aerosol radiative effects.

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