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

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.

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

10.5194/acp-2018-78 ◽

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.

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

Atmospheric Environment ◽

10.1016/j.atmosenv.2019.02.026 ◽

2019 ◽

Vol 205 ◽

pp. 78-89 ◽

Cited By ~ 2

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

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Contrasting synoptic weather patterns between non-dust high particulate matter events and Asian dust events in Seoul, South Korea

Atmospheric Environment ◽

10.1016/j.atmosenv.2019.116864 ◽

2019 ◽

Vol 214 ◽

pp. 116864 ◽

Cited By ~ 4

Author(s):

Myung-Il Jung ◽

Seok-Woo Son ◽

Hyun Cheol Kim ◽

Sang-Woo Kim ◽

Rokjin J. Park ◽

...

Keyword(s):

Particulate Matter ◽

South Korea ◽

Asian Dust ◽

Weather Patterns ◽

Synoptic Weather ◽

Dust Events

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Spatial analysis of aerosol optical depth obtained by air quality modelling and SEVIRI satellite observations over Portugal

Atmospheric Pollution Research ◽

10.1016/j.apr.2018.07.011 ◽

2019 ◽

Vol 10 (1) ◽

pp. 234-243

Author(s):

A.P. Fernandes ◽

M. Riffler ◽

J. Ferreira ◽

S. Wunderle ◽

C. Borrego ◽

...

Keyword(s):

Air Quality ◽

Spatial Analysis ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Satellite Observations ◽

Air Quality Modelling

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A low-cost monitor for measurement of fine particulate matter and aerosol optical depth – Part 2: Citizen-science pilot campaign in northern Colorado

Atmospheric Measurement Techniques ◽

10.5194/amt-12-6385-2019 ◽

2019 ◽

Vol 12 (12) ◽

pp. 6385-6399 ◽

Cited By ~ 4

Author(s):

Bonne Ford ◽

Jeffrey R. Pierce ◽

Eric Wendt ◽

Marilee Long ◽

Shantanu Jathar ◽

...

Keyword(s):

Particulate Matter ◽

Air Quality ◽

Aerosol Optical Depth ◽

Citizen Science ◽

Optical Depth ◽

Spatial Scales ◽

Fine Particulate Matter ◽

Optical Measurements ◽

Fine Particulate ◽

Northern Colorado

Abstract. A pilot field campaign was conducted in the fall and winter of 2017 in northern Colorado to test the deployment of the Aerosol Mass and Optical Depth (AMOD) instrument as part of the Citizen-Enabled Aerosol Measurements for Satellites (CEAMS) network. Citizen scientists were recruited to set up the device to take filter and optical measurements of aerosols in their backyards. The goal of the network is to provide more surface particulate matter and aerosol optical depth (AOD) measurements to increase the spatial and temporal resolution of ratios of fine particulate matter (PM2.5) to AOD and to improve satellite-based estimates of air quality. Participants collected 65 filters and 160 multi-wavelength AOD measurements, from which 109 successful PM2.5 : AOD ratios were calculated. We show that PM2.5, AOD, and their ratio (PM2.5 : AOD) often vary substantially over relatively short spatial scales; this spatial variation is not typically resolved by satellite- and model-based PM2.5 exposure estimates. The success of the pilot campaign suggests that citizen-science networks are a viable means for providing new insight into surface air quality. We also discuss lessons learned and AMOD design modifications, which will be used in future wider deployments of the CEAMS network.

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

Atmospheric Chemistry and Physics ◽

10.5194/acp-20-6015-2020 ◽

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.

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Extreme Aerosol Events at Mesa Verde, Colorado: Implications for Air Quality Management

Atmosphere ◽

10.3390/atmos12091140 ◽

2021 ◽

Vol 12 (9) ◽

pp. 1140

Author(s):

Marisa E. Gonzalez ◽

Jeri G. Garfield ◽

Andrea F. Corral ◽

Eva-Lou Edwards ◽

Kira Zeider ◽

...

Keyword(s):

Particulate Matter ◽

Air Quality ◽

Extreme Events ◽

Airborne Particulate Matter ◽

Asian Dust ◽

Weekly Cycle ◽

Aerosol Model ◽

Anthropogenic Pollutants ◽

Mesa Verde ◽

Aerosol Analysis

A significant concern for public health and visibility is airborne particulate matter, especially during extreme events. Of most relevance for health, air quality, and climate is the role of fine aerosol particles, specifically particulate matter with aerodynamic diameters less than or equal to 2.5 micrometers (PM2.5). The purpose of this study was to examine PM2.5 extreme events between 1989 and 2018 at Mesa Verde, Colorado using Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring data. Extreme events were identified as those with PM2.5 on a given day exceeding the 90th percentile value for that given month. We examine the weekly, monthly, and interannual trends in the number of extreme events at Mesa Verde, in addition to identifying the sources of the extreme events with the aid of the Navy Aerosol Analysis and Prediction (NAAPS) aerosol model. Four sources were used in the classification scheme: Asian dust, non-Asian dust, smoke, and “other”. Our results show that extreme PM2.5 events in the spring are driven mostly by the dust categories, whereas summertime events are influenced largely by smoke. The colder winter months have more influence from “other” sources that are thought to be largely anthropogenic in nature. No weekly cycle was observed for the number of events due to each source; however, interannual analysis shows that the relative amount of dust and smoke events compared to “other” events have increased in the last decade, especially smoke since 2008. The results of this work indicate that, to minimize and mitigate the effects of extreme PM2.5 events in the southwestern Colorado area, it is important to focus mainly on smoke and dust forecasting in the spring and summer months. Wintertime extreme events may be easier to regulate as they derive more from anthropogenic pollutants accumulating in shallow boundary layers in stagnant conditions.

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Global Distribution of Column Satellite Aerosol Optical Depth to Surface PM2.5 Relationships

Remote Sensing ◽

10.3390/rs12121985 ◽

2020 ◽

Vol 12 (12) ◽

pp. 1985 ◽

Cited By ~ 1

Author(s):

Sundar Christopher ◽

Pawan Gupta

Keyword(s):

Particulate Matter ◽

Air Quality ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Large Population ◽

Correlation Coefficients ◽

Large Spread ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer

Using a combined Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) mid-visible aerosol optical depth (AOD) product at 0.1 × 0.1-degree spatial resolution and collocated surface PM2.5 (particulate matter with aerodynamic diameter smaller than 2.5 μm) monitors, we provide a global five-year (2015–2019) assessment of the spatial and seasonal AOD–PM2.5 relationships of slope, intercepts, and correlation coefficients. Only data from ground monitors accessible through an open air-quality portal that are available to the worldwide community for air quality research and decision making are used in this study. These statistics that are reported 1 × 1-degree resolution are important since satellite AOD is often used in conjunction with spatially limited surface PM2.5 monitors to estimate global distributions of surface particulate matter concentrations. Results indicate that more than 3000 ground monitors are now available for PM2.5 studies. While there is a large spread in correlation coefficients between AOD and PM2.5, globally, averaged over all seasons, the correlation coefficient is 0.55 with a unit AOD producing 54 μgm−3 of PM2.5 (Slope) with an intercept of 8 μgm−3. While the number of surface PM2.5 measurements has increased by a factor of 10 over the last decade, a concerted effort is still needed to continue to increase these monitors in areas that have no surface monitors, especially in large population centers that will further leverage the strengths of satellite data.

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Satellite-based observations of ground-level fine particulate matter and comparison to a regional air quality model

10.5194/egusphere-egu2020-10342 ◽

2020 ◽

Author(s):

Jana Handschuh ◽

Frank Baier ◽

Thilo Erbertseder ◽

Martijn Schaap

Keyword(s):

Particulate Matter ◽

Air Quality ◽

Air Pollutants ◽

Fine Particulate Matter ◽

Ground Level ◽

In Situ Measurements ◽

Satellite Observations ◽

Optical Parameters ◽

Fine Particulate

Particulate matter and other air pollutants have become an increasing burden on the environment and human health. Especially in metropolitan and high-traffic areas, air quality is often remarkably reduced. For a better understanding of the air quality in specific areas, which is of great environment-political interest, data with high resolution in space and time is required. The combination of satellite observations and chemistry-transport-modelling has proven to give a good database for assessments and analyses of air pollution. In contrast to sample in-situ measurements, satellite observations provide area-wide coverage &#8203;&#8203;of measurements and thus the possibility for an almost gapless mapping of actual air pollutants. For a high temporal resolution, chemistry-transport-models are needed, which calculate concentrations of specific pollutants in continuous time steps. Satellite observations can thus be used to improve model performances.There are no direct satellite-measurements of fine particulate matter (PM2.5) but ground-level concentrations of PM2.5 can be derived from optical parameters such as aerosol optical depth (AOD). A wide range of methods for the determination of PM2.5 concentrations from AOD measurements has been developed so far, but it is still a big challenge. In this study a semi-empirical approach based on the physical relationships between meteorological and optical parameters was applied to determine a first-guess of ground-level PM2.5 concentrations for the year 2018 and the larger Germany region. Therefor AOD observations of MODIS (Moderate Resolution Imaging Spectroradiometer) aboard the NASA Aqua satellite were used in a spatial resolution of 3km. First results showed an overestimation of ground-level aerosols and quiet low correlations with in-situ station measurements from the European Environmental Agency (EEA). To improve the results, correction factors were calculated using the coefficients of linear regression between satellite-based and in-situ measured particulate matter concentrations. Spatial and seasonal dependencies were taken into account with it. Correlations between satellite and in-situ measurements could be improved applying this method.The MODIS 3km AOD product was found to be a good base for area-wide calculations of ground-level PM2.5 concentrations. First comparisons to the calculated PM2.5 concentrations from chemistry-transport-model POLYPHEMUS/DLR showed significant differences though. Satellite observations will now be used to improve the general model performance, first by helping to find and understand regional and temporal dependencies in the differences. As part of the German project S-VELD funded by the Federal Ministry of Transport and Digital Infrastructure BMVI, it will help for example to adjust the derivation of particle emissions within the model.

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Modelling and assimilation of lidar signals over Greater Paris during the MEGAPOLI summer campaign

Atmospheric Chemistry and Physics ◽

10.5194/acp-14-3511-2014 ◽

2014 ◽

Vol 14 (7) ◽

pp. 3511-3532 ◽

Cited By ~ 19

Author(s):

Y. Wang ◽

K. N. Sartelet ◽

M. Bocquet ◽

P. Chazette

Keyword(s):

Particulate Matter ◽

Air Quality ◽

Data Assimilation ◽

Transport Model ◽

Interpolation Method ◽

Optimal Interpolation ◽

Atmospheric Conditions ◽

Model Aerosol ◽

Paris Area ◽

Lidar Observations

Abstract. In this study, we investigate the ability of the chemistry transport model (CTM) Polair3D of the air quality modelling platform Polyphemus to simulate lidar backscattered profiles from model aerosol concentration outputs. This investigation is an important preprocessing stage of data assimilation (validation of the observation operator). To do so, simulated lidar signals are compared to hourly lidar observations performed during the MEGAPOLI (Megacities: Emissions, urban, regional and Global Atmospheric POLlution and climate effects, and Integrated tools for assessment and mitigation) summer experiment in July 2009, when a ground-based mobile lidar was deployed around Paris on-board a van. The comparison is performed for six different measurement days, 1, 4, 16, 21, 26 and 29 July 2009, corresponding to different levels of pollution and different atmospheric conditions. Overall, Polyphemus well reproduces the vertical distribution of lidar signals and their temporal variability, especially for 1, 16, 26 and 29 July 2009. Discrepancies on 4 and 21 July 2009 are due to high-altitude aerosol layers, which are not well modelled. In the second part of this study, two new algorithms for assimilating lidar observations based on the optimal interpolation method are presented. One algorithm analyses PM10 (particulate matter with diameter less than 10 μm) concentrations. Another analyses PM2.5 (particulate matter with diameter less than 2.5 μm) and PM2.5–10 (particulate matter with a diameter higher than 2.5 μm and lower than 10 μm) concentrations separately. The aerosol simulations without and with lidar data assimilation (DA) are evaluated using the Airparif (a regional operational network in charge of air quality survey around the Paris area) database to demonstrate the feasibility and usefulness of assimilating lidar profiles for aerosol forecasts. The evaluation shows that lidar DA is more efficient at correcting PM10 than PM2.5, probably because PM2.5 is better modelled than PM10. Furthermore, the algorithm which analyses both PM2.5and PM2.5–10 provides the best scores for PM10. The averaged root-mean-square error (RMSE) of PM10 is 11.63 μg m−3 with DA (PM2.5 and PM2.5–10), compared to 13.69 μg m−3 with DA (PM10) and 17.74 μg m−3 without DA on 1 July 2009. The averaged RMSE of PM10 is 4.73 μg m−3 with DA (PM2.5 and PM2.5–10), against 6.08 μg m−3 with DA (PM10) and 6.67 μg m−3 without DA on 26 July 2009.

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