scholarly journals Long-term aerosol optical depth datasets over China retrieved from satellite data

2011 ◽  
Vol 4 (6) ◽  
pp. 6643-6678 ◽  
Author(s):  
Y. Xue ◽  
H. Xu ◽  
Y. Li ◽  
L. Yang ◽  
L. Mei ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Dust Storms ◽  
The Tibetan Plateau ◽  
Hysplit Model ◽  
The North ◽  
Moderate Resolution ◽  
Dust Weather ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Nine years of daily aerosol optical depth (AOD) measurements have been derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data using the Synergetic Retrieval of Aerosol Properties (SRAP) method over China for the period from August 2002 to August 2011, comprising AODs at 470, 550, and 660 nm. Then, the variation over China over the nine years was determined from the derived AOD data. Preliminary daily results show the agreement between the Aerosol Robotic Network (AERONET) AOD data and the derived AOD data. From 1219 daily collocations, representing mutually cloud-free conditions, we find that more than 54% of SRAP-MODIS retrieved AOD values comparing with AERONET-observed values within an expected error envelop of 20%. From 222 monthly averaged collocations, representing mutually cloud-free conditions, we find that more than 63% of SRAP-MODIS retrieved AOD values comparing with AERONET-observed values within an expected error envelop of 15% and more than 70% within an expected error envelop of 20%. In addition, the long-term SRAP AOD dataset has been implemented in analysing case studies involving dust storms, haze and the characteristics of AOD variation over China over the past nine years. It was found that areas in China with high AOD values generally appear in the Inner Mongolia, the North China Plain, Tarim Basin, the Sichuan Basin, the Tibetan Plateau and the middle and lower reaches of the Yangtze River and area with low AOD values generally appear in the Fujian Province, the Yungui Plateau, and northeast plain. The seasonal averaged AOD results indicate that AOD values generally reach their maximum in spring and their minimum in winter. The yearly mean and monthly mean SRAP AOD were also used to study the spatial and temporal aerosol distributions over China. The results indicate that the AOD over China exhibited no obvious change. Monthly averaged AOD in August in Beijing experienced one decreasing processes from 2006 to 2010, especially after 2007. The monthly mean AOD decreased from 0.46 in 2007 to 0.29 in 2010. SRAP AODs were used to study one haze case and dust case. Combining AOD data from the SRAP AOD dataset and HYSPLIT model can forecast the transport of haze. SRAP AOD data are also sensitive enough to reflect the occurrence and intensity of dust weather. Thus, the SRAP AOD dataset can be used to precisely reflect the spatial distribution, concentration distribution and transmission path of dust.

scholarly journals Assessment of the Representativeness of MODIS Aerosol Optical Depth Products at Different Temporal Scales Using Global AERONET Measurements

2020 ◽  
Vol 12 (14) ◽  
pp. 2330
Author(s):  
Yan Tong ◽  
Lian Feng ◽  
Kun Sun ◽  
Jing Tang
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Radiative Forcing ◽  
Large Scale ◽  
Data Availability ◽  
Temporal Scales ◽  
Modis Aod ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Assessments of long-term changes of air quality and global radiative forcing at a large scale heavily rely on satellite aerosol optical depth (AOD) datasets, particularly their temporal binning products. Although some attempts focusing on the validation of long-term satellite AOD have been conducted, there is still a lack of comprehensive quantification and understanding of the representativeness of satellite AOD at different temporal binning scales. Here, we evaluated the performances of the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products at various temporal scales by comparing the MODIS AOD datasets from both the Terra and Aqua satellites with the entire global AErosol RObotic NETwork (AERONET) observation archive between 2000 and 2017. The uncertainty levels of the MODIS hourly and daily AOD products were similarly high, indicating that MODIS AOD retrievals could be used to represent daily aerosol conditions. The MODIS data showed the reduced quality when integrated from the daily to monthly scale, where the relative mean bias (RMB) changed from 1.09 to 1.21 for MODIS Terra and from 1.04 to 1.17 for MODIS Aqua, respectively. The limitation of valid data availability within a month appeared to be the primary reason for the increased uncertainties in the monthly binning products, and the monthly data associated uncertainties could be reduced when the number of valid AOD retrievals reached 15 times in one month. At all three temporal scales, the uncertainty levels of satellite AOD products decreased with increasing AOD values. The results of this study could provide crucial information for satellite AOD users to better understand the reliability of different temporal AOD binning products and associated uncertainties in their derived long-term trends.

scholarly journals A New Satellite-Based Global Climatology of Dust Aerosol Optical Depth

2020 ◽  
Vol 59 (1) ◽  
pp. 83-102 ◽  
Author(s):  
Kara K. Voss ◽  
Amato T. Evan
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Dust Aerosol ◽  
Aral Sea ◽  
Optical Measurements ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

AbstractBy mass, dust is the largest contributor to global aerosol burden, yet long-term observational records of dust, particularly over the ocean, are limited. Here, two nearly global observational datasets of dust aerosol optical depth τd are created primarily on the basis of optical measurements of the aerosol column from 1) the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite spanning from 2001 to 2018 and 2) the Advanced Very High Resolution Radiometer (AVHRR) from 1981 to 2018. The quality of the new data is assessed by comparison with existing dust datasets that are spatially more limited. Between 2001 and 2018, τd decreased over Asia and increased significantly over the Sahara, Middle East, and parts of eastern Europe, with the largest increase found over the Aral Sea where emissive playa surfaces have been exposed. These daily, observational, and nearly global records of dust will allow for improvement in understanding the role of dust in climate variability.

scholarly journals Towards a long-term global aerosol optical depth record: applying a consistent aerosol retrieval algorithm to MODIS and VIIRS-observed reflectance

2015 ◽  
Vol 8 (10) ◽  
pp. 4083-4110 ◽  
Author(s):  
R. C. Levy ◽  
L. A. Munchak ◽  
S. Mattoo ◽  
F. Patadia ◽  
L. A. Remer ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Infrared Imaging ◽  
Quantitative Information ◽  
Environmental Data ◽  
Retrieval Algorithm ◽  
Climate Data ◽  
Data Record ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. To answer fundamental questions about aerosols in our changing climate, we must quantify both the current state of aerosols and how they are changing. Although NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have provided quantitative information about global aerosol optical depth (AOD) for more than a decade, this period is still too short to create an aerosol climate data record (CDR). The Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on the Suomi-NPP satellite in late 2011, with additional copies planned for future satellites. Can the MODIS aerosol data record be continued with VIIRS to create a consistent CDR? When compared to ground-based AERONET data, the VIIRS Environmental Data Record (V_EDR) has similar validation statistics as the MODIS Collection 6 (M_C6) product. However, the V_EDR and M_C6 are offset in regards to global AOD magnitudes, and tend to provide different maps of 0.55 μm AOD and 0.55/0.86 μm-based Ångström Exponent (AE). One reason is that the retrieval algorithms are different. Using the Intermediate File Format (IFF) for both MODIS and VIIRS data, we have tested whether we can apply a single MODIS-like (ML) dark-target algorithm on both sensors that leads to product convergence. Except for catering the radiative transfer and aerosol lookup tables to each sensor's specific wavelength bands, the ML algorithm is the same for both. We run the ML algorithm on both sensors between March 2012 and May 2014, and compare monthly mean AOD time series with each other and with M_C6 and V_EDR products. Focusing on the March–April–May (MAM) 2013 period, we compared additional statistics that include global and gridded 1° × 1° AOD and AE, histograms, sampling frequencies, and collocations with ground-based AERONET. Over land, use of the ML algorithm clearly reduces the differences between the MODIS and VIIRS-based AOD. However, although global offsets are near zero, some regional biases remain, especially in cloud fields and over brighter surface targets. Over ocean, use of the ML algorithm actually increases the offset between VIIRS and MODIS-based AOD (to ~ 0.025), while reducing the differences between AE. We characterize algorithm retrievability through statistics of retrieval fraction. In spite of differences between retrieved AOD magnitudes, the ML algorithm will lead to similar decisions about "whether to retrieve" on each sensor. Finally, we discuss how issues of calibration, as well as instrument spatial resolution may be contributing to the statistics and the ability to create a consistent MODIS → VIIRS aerosol CDR.

scholarly journals Dynamics of Muddy Rain of 15 June 2018 in Nepal

Atmosphere ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 529
Author(s):  
Ashok Kumar Pokharel ◽  
Tianli Xu ◽  
Xiaobo Liu ◽  
Binod Dawadi
Keyword(s):  
Dust Emission ◽  
Dust Storms ◽  
Strong Wind ◽  
Hysplit Model ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Upper Level ◽  
Terra Satellite ◽  
Negative Buoyancy

It has been revealed from the Modern-Era Retrospective analysis for Research and Applications MERRA analyses, Moderate Resolution Imaging Spectroradiometer MODIS/Terra satellite imageries, Naval Aerosol Analysis and Prediction System NAAPS model outputs, Cloud –Aerosol Lidar and Infrared Pathfinder Satellite Observations CALIPSO imageries, Hybrid Single Particle Lagrangian Integrated Trajectory HYSPLIT model trajectories, atmospheric soundings, and observational records of dust emission that there were multiple dust storms in the far western parts of India from 12 to 15 June 2018 due to thunderstorms. This led to the lifting of the dust from the surface. The entry of dust into the upper air was caused by the generation of a significant amount of turbulent kinetic energy as a function of strong wind shear generated by the negative buoyancy of the cooled air aloft and the convective buoyancy in the lower planetary boundary layer. Elevated dust reached a significant vertical height and was advected towards the northern/northwestern/northeastern parts of India. In the meantime, this dust was carried by northwesterly winds associated with the jets in the upper level, which advected dust towards the skies over Nepal where rainfall was occurring at that time. Consequently, this led to the muddy rain in Nepal.

scholarly journals An Effective and Efficient Enhanced Fixed Rank Smoothing Method for the Spatiotemporal Fusion of Multiple-Satellite Aerosol Optical Depth Products

2020 ◽  
Vol 12 (7) ◽  
pp. 1102
Author(s):  
Bin Zou ◽  
Ning Liu ◽  
Wei Wang ◽  
Huihui Feng ◽  
Xiangping Liu ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Mainland China ◽  
Bayesian Maximum Entropy ◽  
Time Cost ◽  
Long Time ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Fixed Rank

Current reported spatiotemporal solutions for fusing multisensor aerosol optical depth (AOD) products used to recover gaps either suffer from unacceptable accuracy levels, i.e., fixed rank smooth (FRS), or high time costs, i.e., Bayesian maximum entropy (BME). This problem is generally more serious when dealing with multiple AOD products in a long time series or over large geographic areas. This study proposes a new, effective, and efficient enhanced FRS method (FRS-EE) to fuse satellite AOD products with uncertainty constraints. AOD products used in the fusion experiment include Moderate Resolution Imaging SpectroRadiometer (MODIS) DB/DT_DB_Combined AOD and Multiangle Imaging SpectroRadiometer (MISR) AOD across mainland China from 2016 to 2017. Results show that the average completeness of original, initial FRS fused, and FRS-EE fused AODs with uncertainty constraints are 22.80%, 95.18%, and 65.84%, respectively. Although the correlation coefficient (R = 0.77), root mean square error (RMSE = 0.30), and mean bias (Bias = 0.023) of the initial FRS fused AODs are relatively lower than those of original AODs compared to Aerosol Robotic Network (AERONET) AOD records, the accuracy of FRS-EE fused AODs, which are R = 0.88, RMSE = 0.20, and Bias = 0.022, is obviously improved. More importantly, in regions with fully missing original AODs, the accuracy of FRS-EE fused AODs is close to that of original AODs in regions with valid retrievals. Meanwhile, the time cost of FRS-EE for AOD fusion was only 2.91 h; obviously lower than the 30.46 months taken for BME.

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 Comparison of PMCAMx aerosol optical depth predictions over Europe with AERONET and MODIS measurements

2016 ◽  
Vol 9 (11) ◽  
pp. 4257-4272
Author(s):  
Antigoni Panagiotopoulou ◽  
Panagiotis Charalampidis ◽  
Christos Fountoukis ◽  
Christodoulos Pilinis ◽  
Spyros N. Pandis
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Transport Model ◽  
Chemical Transport Model ◽  
Secondary Aerosol ◽  
The Balkans ◽  
Modis Aod ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
High Dust

Abstract. The ability of chemical transport model (CTM) PMCAMx to reproduce aerosol optical depth (AOD) measurements by the Aerosol Robotic Network (AERONET) and the Moderate Resolution Imaging Spectroradiometer (MODIS) over Europe during the photochemically active period of May 2008 (EUCAARI campaign) is evaluated. Periods with high dust or sea-salt levels are excluded, so the analysis focuses on the ability of the model to simulate the mostly secondary aerosol and its interactions with water. PMCAMx reproduces the monthly mean MODIS and AERONET AOD values over the Iberian Peninsula, the British Isles, central Europe, and Russia with a fractional bias of less than 15 % and a fractional error of less than 30 %. However, the model overestimates the AOD over northern Europe, most probably due to an overestimation of organic aerosol and sulfates. At the other end, PMCAMx underestimates the monthly mean MODIS AOD over the Balkans, the Mediterranean, and the South Atlantic. These errors appear to be related to an underestimation of sulfates. Sensitivity tests indicate that the evaluation results of the monthly mean AODs are quite sensitive to the relative humidity (RH) fields used by PMCAMx, but are not sensitive to the simulated size distribution and the black carbon mixing state. The screening of the satellite retrievals for periods with high dust (or coarse particles in general) concentrations as well as the combination of the MODIS and AERONET datasets lead to more robust conclusions about the ability of the model to simulate the secondary aerosol components that dominate the AOD during this period.

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

2020 ◽  
Vol 12 (12) ◽  
pp. 1985 ◽  
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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