Analysis of spatial and temporal variability of aerosol optical depth over China using MODIS combined Dark Target and Deep Blue product

2018 ◽  
Vol 137 (3-4) ◽  
pp. 2271-2288 ◽  
Author(s):  
Mikalai Filonchyk ◽  
Haowen Yan ◽  
Zhongrong Zhang
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Temporal Variability ◽  
Spatial And Temporal Variability ◽  
Deep Blue

scholarly journals Aerosol optical depth in the European Brewer Network

2017 ◽  
Author(s):  
Javier López-Solano ◽  
Alberto Redondas ◽  
Thomas Carlund ◽  
Juan J. Rodriguez-Franco ◽  
Henri Diémoz ◽  
...  
Keyword(s):  
Real Time ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Temporal Variability ◽  
Input Data ◽  
Spatial And Temporal Variability ◽  
Ultraviolet Range ◽  
Using Data ◽  
Better Than

Abstract. The high spatial and temporal variability of aerosols make networks capable of measuring their properties in near real time of high scientific interest. In this work we present and discuss results of an aerosol optical depth algorithm to be used in the European Brewer Network, which provides data in near real time of more than 30 spectrophotometers located from Tamanrasset (Algeria) to Kangerlussuaq (Greenland). Using data from the Brewer Intercomparison Campaigns in the years 2013 and 2015, and the period in between, plus comparisons with Cimel sunphotometers and UVPFR instruments, we check the precision, stability, and uncertainty of the Brewer AOD in the ultraviolet range from 300 to 320 nm. Our results show a precision better than 0.01, an uncertainty of less than 0.05, and a stability similar to that of the ozone measurements for well-maintained instruments. We also discuss future improvements to our algorithm with respect to the input data, their processing, and the characterization of the Brewer instruments for the measurement of aerosols.

scholarly journals Airborne observation during KORUS-AQ show aerosol optical depth are more spatially self-consistent than aerosol intensive properties

2022 ◽  
Author(s):  
Samuel E. LeBlanc ◽  
Michal Segal-Rozenhaimer ◽  
Jens Redemann ◽  
Connor J. Flynn ◽  
Roy R. Johnson ◽  
...  
Keyword(s):  
Air Quality ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Temporal Variability ◽  
Spatial Scales ◽  
Aerosol Emission ◽  
Fine Mode ◽  
Predominant Factor ◽  
Spatio Temporal ◽  
Microphysical Processes

Abstract. Aerosol particles can be emitted, transported, removed, or transformed, leading to aerosol variability at scales impacting the climate (days to years and over hundreds of kilometers) or the air quality (hours to days and from meters to hundreds of kilometers). We present the temporal and spatial scales of changes in AOD (Aerosol Optical Depth), and aerosol size (using Angstrom Exponent; AE, and Fine-Mode-Fraction; FMF) over Korea during the 2016 KORUS-AQ (KORea-US Air Quality) atmospheric experiment. We use measurements and retrievals of aerosol optical properties from airborne instruments for remote sensing (4STAR; Spectrometers for Sky-Scanning Sun Tracking Atmospheric Research) and in situ (LARGE; NASA Langley Aerosol Research Group Experiment) on board the NASA DC-8, geostationary satellite (GOCI; Geostationary Ocean Color Imager; Yonsei aerosol retrieval (YAER) version 2) and reanalysis (MERRA-2; Modern-Era Retrospective Analysis for Research and Applications, version 2). Measurements from 4STAR when flying below 500 m, show an average AOD at 501 nm of 0.43 and an average AE of 1.15 with large standard deviation (0.32 and 0.26 for AOD and AE respectively) likely due to mixing of different aerosol types (fine and coarse mode). The majority of AODs due to fine mode aerosol is observed at altitudes lower than 2 km. Even though there are large variations, for 18 out of the 20 flight days, the column AOD measurements by 4STAR along the NASA DC-8 flight trajectories matches the south-Korean regional average derived from GOCI. We also observed that, contrary to prevalent understanding, AE and FMF are more spatially variable than AOD during KORUS-AQ, even when accounting for potential sampling biases by using Monte Carlo resampling. Averaging between measurements and model for the entire KORUS-AQ period, a reduction in correlation by 15 % is 65.0 km for AOD and shorter at 22.7 km for AE. While there are observational and model differences, the predominant factor influencing spatial-temporal homogeneity is the meteorological period. High spatio-temporal variability occur during the dynamic period (25–31 May), and low spatio-temporal variability occur during blocking Rex pattern (01–07 June). The changes in spatial variability scales between AOD and FMF/AE, while inter-related, indicate that microphysical processes that impact mostly the dominant aerosol size, like aerosol particle formation, growth, and coagulation, vary at shorter scales than the aerosol concentration processes that mostly impact AOD, like aerosol emission, transport, and removal.

scholarly journals Retrieval of aerosol optical depth over land based on a time series technique using MSG/SERIVI data

2012 ◽  
Vol 12 (2) ◽  
pp. 4031-4071 ◽  
Author(s):  
L. Mei ◽  
Y. Xue ◽  
G. de Leeuw ◽  
T. Holzer-Popp ◽  
J. Guang ◽  
...  
Keyword(s):  
Time Series ◽  
Detailed Analysis ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Optimal Solution ◽  
Single Scattering ◽  
Asymmetry Factor ◽  
Surface Reflectance ◽  
Deep Blue ◽  
Aerosol Type

Abstract. A novel approach for the joint retrieval of aerosol optical depth (AOD) and surface reflectance, using Meteosat Second Generation – Spinning Enhanced Visible and Infrared Imagers (MSG/SEVIRI) observations in two solar channels, is presented. The retrieval is based on a time series (TS) technique, which makes use of the two visible bands at 0.6 μm and 0.8 μm in three orderly scan times (15 min interval between two scans) to retrieve the AOD over land. Using the radiative transfer equation for plane-parallel atmospheres two coupled differential equations for the upward and downward fluxes are derived. The boundary conditions for the upward and downward fluxes at the top and at the bottom of the atmosphere are used in these equations to provide an analytic solution for the surface reflectance. To derive these fluxes, the aerosol single scattering albedo (SSA) and asymmetry factor are required to provide a solution. These are provided from a set of six pre-defined aerosol types with the SSA and asymmetry factor (g). We assume one aerosol type for a grid of 1° × 1° and the surface reflectance changes little between two consequent scans. A k approximation was used in the inversion to find the best solution of atmospheric properties and surface reflectance. The algorithm makes use of numerical minimisation routines to obtain the optimal solution of atmospheric properties and surface reflectance by selection of the most suitable aerosol type from pre-defined sets. Also, it is assumed that the surface reflectance is little influenced by aerosol scattering at 1.6 μm and therefore the ratio of surface reflectances in the solar band for two consequent scans can be well-approximated by the ratio of the reflectances at 1.6 μm. A further assumption is that the surface reflectance varies only slightly over a period of 30 min. A detailed analysis of the retrieval results show that it is suitable for AOD retrieval over land. Six Aerosol Robotic Network (AERONET) sites with different surface types were used for detailed analysis and 42 other AERONET sites were used for validation. From 445 collocations representing stable and homogeneous aerosol type, we found that >75% of MSG-retrieved AOD values compared to AERONET observed values with an error envelope of ±0.05 ± 0.15τ and a high correlation (R > 0.86). The AOD datasets derived using the TS method with SEVIRI data was also compared with collocated AOD products derived from the NASA TERRA and AQUA MODIS data using the dark dense vegetation (DDV) method and the Deep Blue algorithms. Using the TS method, AOD could be retrieved for more pixels than with the NASA Deep Blue algorithm. The AOD values derived compare favourably.

scholarly journals Implications of MODIS bowtie distortion on aerosol optical depth retrievals, and techniques for mitigation

2015 ◽  
Vol 8 (8) ◽  
pp. 8727-8752 ◽  
Author(s):  
A. M. Sayer ◽  
N. C. Hsu ◽  
C. Bettenhausen
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Geographical Location ◽  
Pixel Size ◽  
Shape Distortion ◽  
Deep Blue ◽  
L2 Processing ◽  
Simple Step ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. The scan geometry of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors results in a pixel shape distortion known as the "bowtie effect". Specifically, sensor pixels near the edge of the swath are elongated along-track and across-track compared to pixels near the centre of the swath, resulting in an increase of pixel area by up to a factor of ~ 9, and additionally pixels acquired from consecutive scans overlap. The Deep Blue and Dark Target aerosol optical depth (AOD) retrieval algorithms aggregate sensor pixels and provide level 2 (L2) AOD at a nominal horizontal pixel size of 10 km, but the bowtie distortion means that they also suffer from this size increase and overlap. This means that the spatial characteristics of the data vary as a function of satellite viewing zenith angle (VZA) and, for VZA > 30°, corresponding to approximately 50 % of the data, are areally enlarged by a factor of 50 % or more compared to this nominal pixel area, and are not spatially independent of each other. This has implications for retrieval uncertainty and aggregated statistics, causing a narrowing of AOD distributions near the edge of the swath, as well as for data comparability from the application of similar algorithms to sensors without this level of bowtie distortion. Additionally, the pixel overlap is not obvious to users of the L2 aerosol products because only pixel centres, not boundaries, are provided within the L2 products. A two-step procedure is proposed to mitigate the effects of this distortion on the MODIS aerosol products. The first (simple) step involves changing the order in which pixels are aggregated in L2 processing to reflect geographical location rather than scan order, which removes the bulk of the overlap between L2 pixels, and slows the rate of growth of L2 pixel size vs. VZA. This can be achieved without significant changes to existing MODIS processing algorithms. The second step involves additionally changing the number of sensor pixels aggregated across-track as a function of VZA, which preserves L2 pixel size at around 10 km × 10 km across the whole swath, but would require algorithmic quality assurance tests to be re-evaluated. Both of these steps also improve the extent to which the pixel locations a user would infer from the L2 data products represent the actual spatial extent of the L2 pixels.

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 Modeling a typical winter-time dust event over the Arabian Peninsula and the Red Sea

2013 ◽  
Vol 13 (4) ◽  
pp. 1999-2014 ◽  
Author(s):  
S. Kalenderski ◽  
G. Stenchikov ◽  
C. Zhao
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Red Sea ◽  
Arabian Peninsula ◽  
Dust Particles ◽  
Spatial And Temporal Variability ◽  
Dust Event ◽  
Meteorological Model ◽  
Dust Generation ◽  
Winter Time

Abstract. We used WRF-Chem, a regional meteorological model coupled with an aerosol-chemistry component, to simulate various aspects of the dust phenomena over the Arabian Peninsula and Red Sea during a typical winter-time dust event that occurred in January 2009. The model predicted that the total amount of emitted dust was 18.3 Tg for the entire dust outburst period and that the two maximum daily rates were ~2.4 Tg day−1 and ~1.5 Tg day−1, corresponding to two periods with the highest aerosol optical depth that were well captured by ground- and satellite-based observations. The model predicted that the dust plume was thick, extensive, and mixed in a deep boundary layer at an altitude of 3–4 km. Its spatial distribution was modeled to be consistent with typical spatial patterns of dust emissions. We utilized MODIS-Aqua and Solar Village AERONET measurements of the aerosol optical depth (AOD) to evaluate the radiative impact of aerosols. Our results clearly indicated that the presence of dust particles in the atmosphere caused a significant reduction in the amount of solar radiation reaching the surface during the dust event. We also found that dust aerosols have significant impact on the energy and nutrient balances of the Red Sea. Our results showed that the simulated cooling under the dust plume reached 100 W m−2, which could have profound effects on both the sea surface temperature and circulation. Further analysis of dust generation and its spatial and temporal variability is extremely important for future projections and for better understanding of the climate and ecological history of the Red Sea.

scholarly journals Aerosol Optical Depth from MODIS satellite data above the Pierre Auger Observatory

2019 ◽  
Vol 197 ◽  
pp. 02011
Author(s):  
Nataliia Borodai
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Satellite System ◽  
Horizontal Resolution ◽  
Pierre Auger Observatory ◽  
Fluorescence Detector ◽  
Deep Blue ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Aerosol optical depth can be retrieved from measurements performed by Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument. The MODIS satellite system includes two polar satellites, Terra and Aqua. Each of them flies over the Pierre Auger Observatory once a day, providing two measurements of aerosols per day and covering the whole area of the Observatory. MODIS aerosol data products have been generated by three dedicated algorithms over bright and dark land and over ocean surface. We choose the Deep Blue algorithm data to investigate the distribution of aerosols over the Observatory, as this algorithm is the most appropriate one for semi-arid land of the Pierre Auger Observatory. This data algorithm allows us to obtain aerosol optical depth values for the investigated region, and to build cloud-free aerosol maps with a horizontal resolution 0.1°×0.1°. Since a suffcient number of measurements was obtained only for Loma Amarilla and Coihueco fluorescence detector (FD) sites of the Pierre Auger Observatory, a more detailed analysis of aerosol distributions is provided for these sites. Aerosols over these FD sites are generally distributed in a similar way each year, but some anomalies are also observed. These anomalies in aerosol distributions appear mainly due to some transient events, such as volcanic ash clouds, fires etc. We conclude that the Deep Blue MODIS algorithm provides more realistic aerosol optical depth values than other available algorithms.

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