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 The value of satellite observations in the analysis and short-range prediction of Asian dust

2019 ◽  
Vol 19 (2) ◽  
pp. 987-998 ◽  
Author(s):  
Angela Benedetti ◽  
Francesca Di Giuseppe ◽  
Luke Jones ◽  
Vincent-Henri Peuch ◽  
Samuel Rémy ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Data Assimilation ◽  
Optical Depth ◽  
Short Range ◽  
Asian Dust ◽  
Satellite Observations ◽  
Global Model ◽  
Deep Blue ◽  
Dust Events

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 1-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 the aerosol load over China. Data used were the Moderate Resolution Imaging Spectroradiometer (MODIS) Dark Target and the Deep Blue aerosol optical depth (AOD). 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 of the availability of independent AOD data from two established ground-based networks (AERONET, Aerosol Robotic Network, and CARSNET, China Aerosol Remote Sensing Network), which could be used to evaluate experiments. Particulate matter (PM) data from the China Environmental Protection Agency 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 short-range 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 than the unconstrained experiment, generally showing smaller normalized mean 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 into the forecast up to 48 h. The performance of the global model in terms of particulate matter does not show the same degree of skill as the performance in terms of optical depth. Despite this, the global model is able to capture some regional pollution patterns. This indicates that the global model analyses may be used as boundary conditions for regional air quality models at higher resolution, enhancing their performance in situations in which part of the pollution may have originated from large-scale mechanisms. While assimilation is not a substitute for model development and characterization of the emission sources, results indicate that it can play a role in delivering improved monitoring of Asian dust optical depth.

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.

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>

Assessing the impact of Chinese FY-3/MERSI AOD data assimilation on air quality forecasts: Sand dust events in northeast China

2019 ◽  
Vol 205 ◽  
pp. 78-89 ◽  
Author(s):  
Yansong Bao ◽  
Liuhua Zhu ◽  
Qin Guan ◽  
Yuanhong Guan ◽  
Qifeng Lu ◽  
...  
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
Northeast China ◽  
Dust Events ◽  
The Impact ◽  
Sand Dust

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 Simplified aerosol modeling for variational data assimilation

2009 ◽  
Vol 2 (2) ◽  
pp. 213-229 ◽  
Author(s):  
N. Huneeus ◽  
O. Boucher ◽  
F. Chevallier
Keyword(s):  
Data Assimilation ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
General Circulation ◽  
Variational Data Assimilation ◽  
Sensitivity Analyses ◽  
Simplified Model ◽  
Desert Dust ◽  
Coarse Mode ◽  
Fine Mode

Abstract. We have developed a simplified aerosol model together with its tangent linear and adjoint versions for the ultimate aim of optimizing global aerosol and aerosol precursor emission using variational data assimilation. The model was derived from the general circulation model LMDz; it groups together the 24 aerosol species simulated in LMDz into 4 species, namely gaseous precursors, fine mode aerosols, coarse mode desert dust and coarse mode sea salt. The emissions have been kept as in the original model. Modifications, however, were introduced in the computation of aerosol optical depth and in the processes of sedimentation, dry and wet deposition and sulphur chemistry to ensure consistency with the new set of species and their composition. The simplified model successfully manages to reproduce the main features of the aerosol distribution in LMDz. The largest differences in aerosol load are observed for fine mode aerosols and gaseous precursors. Differences between the original and simplified models are mainly associated to the new deposition and sedimentation velocities consistent with the definition of species in the simplified model and the simplification of the sulphur chemistry. Furthermore, simulated aerosol optical depth remains within the variability of monthly AERONET observations for all aerosol types and all sites throughout most of the year. Largest differences are observed over sites with strong desert dust influence. In terms of the daily aerosol variability, the model is less able to reproduce the observed variability from the AERONET data with larger discrepancies in stations affected by industrial aerosols. The simplified model however, closely follows the daily simulation from LMDz. Sensitivity analyses with the tangent linear version show that the simplified sulphur chemistry is the dominant process responsible for the strong non-linearity of the model.

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 Critical evaluation of the MODIS Deep Blue aerosol optical depth product for data assimilation over North Africa

2012 ◽  
Vol 5 (5) ◽  
pp. 7815-7865 ◽  
Author(s):  
Y. Shi ◽  
J. Zhang ◽  
J. S. Reid ◽  
E. J. Hyer ◽  
N. C. Hsu
Keyword(s):  
Quality Assurance ◽  
Data Assimilation ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
North Africa ◽  
Critical Evaluation ◽  
Deep Blue ◽  
Moderate Resolution ◽  
Level 3 ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. A total of eight years of Terra (2000–2007) and Aqua (2002–2009) Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) collection 5.1 (c5.1) data were examined for their potential usage in aerosol assimilation. Uncertainties in the DB Aerosol Optical Depth (AOD) were identified and studied. Empirical corrections and quality assurance procedures were developed for North Africa and the Arabian Peninsula. After applying quality assurance and quality check procedures, the Root-Mean-Square-Error (RMSE) in the MODIS Terra and Aqua AOD are reduced by 18.1 and 18.2% to 0.16 and 0.17, respectively, with respect to AERONET data. These procedures were also applied to two months of DB collection 6 (c6) AOD data and reductions in RMSE were found, indicating that the algorithms developed for c5.1 data are applicable to c6 data to some extent. A new quality-assured DB level 3 AOD product was developed for future implementations in both aerosol data assimilation and climate related applications.

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