scholarly journals Operational Retrieval of aerosol optical depth over Indian subcontinent and Indian Ocean using INSAT-3D/Imager product validation

2014 ◽  
Vol XL-8 ◽  
pp. 277-282 ◽  
Author(s):  
M. K. Mishra ◽  
G. Rastogi ◽  
P. Chauhan
Keyword(s):  
Indian Ocean ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Linear Correlation ◽  
Indian Subcontinent ◽  
Correlation Coefficients ◽  
Retrieval Algorithm ◽  
Multiple Channel ◽  
Geo Satellite ◽  
Ocean Region

Aerosol optical depth (AOD) over Indian subcontinent and Indian Ocean region is derived operationally for the first time from the geostationary earth orbit (GEO) satellite INSAT-3D Imager data at 0.65 μm wavelength. Single visible channel algorithm based on clear sky composites gives larger retrieval error in AOD than other multiple channel algorithms due to errors in estimating surface reflectance and atmospheric property. However, since MIR channel signal is insensitive to the presence of most aerosols, therefore in present study, AOD retrieval algorithm employs both visible (centred at 0.65 μm) and mid-infrared (MIR) band (centred at 3.9 μm) measurements, and allows us to monitor transport of aerosols at higher temporal resolution. Comparisons made between INSAT-3D derived AOD (τ<sub>I</sub>) and MODIS derived AOD (τ<sub>M</sub>) co-located in space (at 1&deg; resolution) and time during January, February and March (JFM) 2014 encompasses 1165, 1052 and 900 pixels, respectively. Good agreement found between τ<sub>I</sub> and τ<sub>M</sub> during JFM 2014 with linear correlation coefficients (R) of 0.87, 0.81 and 0.76, respectively. The extensive validation made during JFM 2014 encompasses 215 co-located AOD in space and time derived by INSAT 3D (τ<sub>I</sub>) and 10 sun-photometers (τ<sub>A</sub>) that includes 9 AERONET (Aerosol Robotic Network) and 1 handheld sun-photometer site. INSAT-3D derived AOD i.e. τ<sub>I</sub>, is found within the retrieval errors of τ<sub>I</sub> = ±0.07 ±0.15τ<sub>A</sub> with linear correlation coefficient (R) of 0.90 and root mean square error equal (RMSE) to 0.06. Present work shows that INSAT-3D aerosol products can be used quantitatively in many applications with caution for possible residual clouds, snow/ice, and water contamination.

scholarly journals Aerosol Optical Depth (AOD) retrieval using simultaneous GOES-East and GOES-West reflected radiances over the Western US

2012 ◽  
Vol 5 (5) ◽  
pp. 7945-7981
Author(s):  
H. Zhang ◽  
R. M. Hoff ◽  
S. Kondragunta ◽  
I. Laszlo ◽  
A. Lyapustin
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Atmospheric Correction ◽  
Correlation Coefficients ◽  
Western United States ◽  
Retrieval Algorithm ◽  
Hybrid Technique ◽  
Retrieval Accuracy ◽  
Western Us ◽  
Data Coverage

Abstract. Aerosol Optical Depth (AOD) in the Western United States is observed independently by both the GOES-East and GOES-West imagers. The GASP (GOES Aerosol/Smoke Product) aerosol optical depth retrieval algorithm treats each satellite as a unique sensor and thus NOAA obtains two separate aerosol optical depth values at the same time for the same location. The TOA radiances and the associated derived optical depths can be quite different due to the different viewing geometries with large difference in solar-scattering angles. In order to fully exploit the simultaneous observations and generate consistent AOD retrievals from the two satellites, the authors develop a new aerosol optical depth retrieval algorithm that uses data from both satellites. The algorithm uses combined GOES-East and GOES-West visible channel TOA reflectance and daily average AOD from GOES Multi-Angle Implementation of Atmospheric Correction (GOES-MAIAC) on clear days (AOD less than 0.3), when diurnal variation of AOD is low, to retrieve surface BRDF. The known BRDF shape is applied on subsequent days to retrieve BRDF and AOD. The algorithm is validated at three AERONET sites over the Western US. The AOD retrieval accuracy from the hybrid technique using the two satellites is similar to that from one satellite over UCSB and Railroad Valley. Improvement of the accuracy is observed at Boulder. The correlation coefficients between the GOES AOD and AERONET AOD are in the range of 0.67 to 0.81 over the three sites. The hybrid algorithm has more data coverage compared to the single satellite retrievals over surfaces with high reflectance. The number of coincidences with AERONET observations increases from the use of two-single satellite algorithms by 5–80% for the three sites. With the application of the new algorithm, consistent AOD retrievals and better retrieval coverages can be obtained using the data from the two GOES satellite imagers.

scholarly journals A multi-angle aerosol optical depth retrieval algorithm for geostationary satellite data over the United States

2011 ◽  
Vol 11 (23) ◽  
pp. 11977-11991 ◽  
Author(s):  
H. Zhang ◽  
A. Lyapustin ◽  
Y. Wang ◽  
S. Kondragunta ◽  
I. Laszlo ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Atmospheric Correction ◽  
Correlation Coefficients ◽  
The United States ◽  
Retrieval Algorithm ◽  
High Temporal Resolution ◽  
Surface Reflectance ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Polar Orbiting Satellite

Abstract. Aerosol optical depth (AOD) retrievals from geostationary satellites have high temporal resolution compared to the polar orbiting satellites and thus enable us to monitor aerosol motion. However, current Geostationary Operational Environmental Satellites (GOES) have only one visible channel for retrieving aerosols and hence the retrieval accuracy is lower than those from the multichannel polar-orbiting satellite instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS). The operational GOES AOD retrieval algorithm (GOES Aerosol/Smoke Product, GASP) uses 28-day composite images from the visible channel to derive surface reflectance, which can produce large uncertainties. In this work, we develop a new AOD retrieval algorithm for the GOES imager by applying a modified Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. The algorithm assumes the surface Bidirectional Reflectance Distribution Function (BRDF) in the channel 1 of GOES is proportional to seasonal average MODIS BRDF in the 2.1 μm channel. The ratios between them are derived through time series analysis of the GOES visible channel images. The results of AOD and surface reflectance retrievals are evaluated through comparisons against those from Aerosol Robotic Network (AERONET), GASP, and MODIS. The AOD retrievals from the new algorithm demonstrate good agreement with AERONET retrievals at several sites across the US with correlation coefficients ranges from 0.71 to 0.85 at five out of six sites. At the two western sites Railroad Valley and UCSB, the MAIAC AOD retrievals have correlations of 0.8 and 0.85 with AERONET AOD, and are more accurate than GASP retrievals, which have correlations of 0.7 and 0.74 with AERONET AOD. At the three eastern sites, the correlations with AERONET AOD are from 0.71 to 0.81, comparable to the GASP retrievals. In the western US where surface reflectance is higher than 0.15, the new algorithm also produces larger AOD retrieval coverage than both GASP and MODIS.

scholarly journals Aerosol optical depth (AOD) retrieval using simultaneous GOES-East and GOES-West reflected radiances over the western United States

2013 ◽  
Vol 6 (2) ◽  
pp. 471-486 ◽  
Author(s):  
H. Zhang ◽  
R. M. Hoff ◽  
S. Kondragunta ◽  
I. Laszlo ◽  
A. Lyapustin
Keyword(s):  
United States ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
High Surface ◽  
Atmospheric Correction ◽  
Correlation Coefficients ◽  
Western United States ◽  
Retrieval Algorithm ◽  
Hybrid Technique ◽  
Single Observation

Abstract. Aerosol optical depth (AOD) in the western United States is observed independently by both the (Geostationary Operational Environmental Satellites) GOES-East and GOES-West imagers. The GASP (GOES Aerosol/Smoke Product) aerosol optical depth retrieval algorithm treats each satellite as a unique sensor and thus obtains two separate aerosol optical depth values at the same time for the same location. The TOA (the top of the atmosphere) radiances and the associated derived optical depths can be quite different due to the different viewing geometries with large difference in solar-scattering angles. In order to fully exploit the simultaneous observations and generate consistent AOD retrievals from the two satellites, the authors develop a new "hybrid" aerosol optical depth retrieval algorithm that uses data from both satellites. The algorithm uses both GOES-East and GOES-West visible channel TOA reflectance and daily average AOD from GOES Multi-Angle Implementation of Atmospheric Correction (GOES-MAIAC) on low AOD days (AOD less than 0.3), when diurnal variation of AOD is low, to retrieve surface BRDF (Bidirectional Reflectance Distribution Function). The known BRDF shape is applied on subsequent days to retrieve BRDF and AOD. The algorithm is validated at three AERONET sites over the western US. The AOD retrieval accuracy from the "hybrid" technique using the two satellites is similar to that from one satellite over UCSB (University of California Santa Barbara) and Railroad Valley, Nevada. Improvement of the accuracy is observed at Boulder, Colorado. The correlation coefficients between the GOES AOD and AERONET AOD are in the range of 0.67 to 0.81. More than 74% of AOD retrievals are within the error of ±(0.05 + 0.15 τ) compared to AERONET AOD. The hybrid algorithm has more data coverage compared to the single satellite retrievals over surfaces with high surface reflectance. For single observation areas the number of valid AOD data increases from the use of two-single satellite algorithms by 5–80% for the three sites. With the application of the new algorithm, consistent AOD retrievals and better retrieval coverages can be obtained using the data from the two GOES satellite imagers.

Validation of JAXA Himawari-8 Aerosol Optical Depth Products over China with AERONET and CARSNET Observations

2020 ◽  
Author(s):  
Ling Gao ◽  
Chengcai Li ◽  
Lin Chen ◽  
Jun Li ◽  
Huizheng Che
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Southern China ◽  
Correlation Coefficients ◽  
Northern China ◽  
Temporal Variations ◽  
Retrieval Algorithm ◽  
High Temporal Resolution ◽  
Surface Reflectance ◽  
Modis Aod

&lt;p&gt;The performance of JAXA Himawari-8 Advanced Himawari Imager (AHI) aerosol optical depth (AOD) products over China is evaluated with ground-based AErosol&amp;#160;RObotic&amp;#160;NETwork (AERONET) and Sun-Sky Radiometer Observation Network (CARSNET) observations as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products. Considering the quality and quantity of valid data, the study was limited to AOD products from AHI with a Quality Assurance Flag (QA_Flag) of &amp;#8220;good&amp;#8221; and &amp;#8220;very good.&amp;#8221; The spatial distribution of the AHI AOD product is similar to that of the MODIS AOD product. The AOD correlation between AHI and MODIS is better in the morning than in the afternoon after March, however, using MODIS AOD as a reference resulted in underestimation in the morning and overestimation in the afternoon. The bias is also larger in spring and autumn than in summer and winter. Validation with sun-photometer observations indicates good correlation between AHI AOD and ground-based observations with correlation coefficients larger than 0.75 (N&gt;1000) when barren and sparsely vegetated surfaces are excluded. At 02:30 UTC, 53% of the collocated AHI AOD observations fall in the expected error (EE) range and at 5:30 UTC, 59.3% fall above the EE. The AHI AOD overestimation was apparent at the Northern China stations in April and after October, whereas the underestimation was apparent in southern China throughout the year. The temporal variations of AHI and AERONET AOD also show that the overestimation occurred in the afternoon and underestimation occurred in the morning.&lt;/p&gt;&lt;p&gt;The assumption that the solar geometries were nearly identical and the surface reflectance unchanged for a month causes the surface reflectance underestimation and leads to the AOD overestimation for barren surfaces in autumn and winter. Because background aerosols were neglected, the surface reflectance was overestimated, leading to AOD underestimation in vegetated surfaces.&lt;/p&gt;&lt;p&gt;Overall, the JAXA AOD provides a reliable and high temporal resolution aerosol product for environmental and climate research and the aerosol retrieval algorithm requires improvement.&lt;/p&gt;

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 9-year spatial and temporal evolution of desert dust aerosols over South-East Asia as revealed by CALIOP

2017 ◽  
Author(s):  
Emmanouil Proestakis ◽  
Vassilis Amiridis ◽  
Eleni Marinou ◽  
Aristeidis K. Georgoulias ◽  
Stavros Solomos ◽  
...  
Keyword(s):  
East Asia ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Indian Subcontinent ◽  
Dust Aerosol ◽  
Observation Data ◽  
South East Asia ◽  
Desert Dust ◽  
Dust Aerosols ◽  
Se Asia

Abstract. We present a 3-D climatology of the desert dust distribution over South-East Asia derived using CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) data. To distinguish desert dust from total aerosol load we apply a methodology developed in the framework of EARLINET (European Aerosol Research Lidar Network), the particle linear depolarization ratio and updated lidar ratio values suitable for Asian dust, on multiyear CALIPSO observations (01/2007–12/2015). The resulting dust product provides information on the horizontal and vertical distribution of dust aerosols over SE (South-East) Asia along with the seasonal transition of dust transport pathways. Persistent high D_AOD (Dust Aerosol Optical Depth) values, of the order of 0.6, are present over the arid and semi-arid desert regions. Dust aerosol transport (range, height and intensity) is subject to high seasonality, with highest values observed during spring for northern China (Taklimakan/Gobi deserts) and during summer over the Indian subcontinent (Thar Desert). Additionally we decompose the CALIPSO AOD (Aerosol Optical Depth) into dust and non-dust aerosol components to reveal the non-dust AOD over the highly industrialized and densely populated regions of SE Asia, where the non-dust aerosols yield AOD values of the order of 0.5. Furthermore, the CALIPSO-based short-term AOD and D_AOD time series and trends between 01/2007 and 12/2015 are calculated over SE Asia and over selected sub-regions. Positive trends are observed over northwest and east China and the Indian subcontinent, whereas over southeast China are mostly negative. The calculated AOD trends agree well with the trends derived from Aqua/MODIS (Moderate Resolution Imaging Spectroradiometer), although significant differences are observed over specific regions.

scholarly journals Improving GOES Advanced Baseline Imager (ABI) aerosol optical depth (AOD) retrievals using an empirical bias correction algorithm

2020 ◽  
Vol 13 (11) ◽  
pp. 5955-5975
Author(s):  
Hai Zhang ◽  
Shobha Kondragunta ◽  
Istvan Laszlo ◽  
Mi Zhou
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Bias Correction ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Infrared Imaging ◽  
Retrieval Algorithm ◽  
Correction Algorithm ◽  
Time Step

Abstract. The Advanced Baseline Imager (ABI) on board the Geostationary Operational Environmental Satellite-R (GOES-R) series enables retrieval of aerosol optical depth (AOD) from geostationary satellites using a multiband algorithm similar to those of polar-orbiting satellites' sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). However, this work demonstrates that the current version of GOES-16 (GOES-East) ABI AOD has diurnally varying biases due to limitations in the land surface reflectance relationships between the 0.47 µm band and the 2.2 µm band and between the 0.64 µm band and 2.2 µm band used in the ABI AOD retrieval algorithm, which vary with the Sun–satellite geometry and NDVI (normalized difference vegetation index). To reduce these biases, an empirical bias correction algorithm has been developed based on the lowest observed ABI AOD of an adjacent 30 d period and the background AOD at each time step and at each pixel. The bias correction algorithm improves the performance of ABI AOD compared to AErosol RObotic NETwork (AERONET) AOD, especially for the high and medium (top 2) quality ABI AOD. AOD data for the period 6 August to 31 December 2018 are used to evaluate the bias correction algorithm. After bias correction, the correlation between the top 2 quality ABI AOD and AERONET AOD improves from 0.87 to 0.91, the mean bias improves from 0.04 to 0.00, and root-mean-square error (RMSE) improves from 0.09 to 0.05. These results for the bias-corrected top 2 qualities ABI AOD are comparable to those of the corrected high-quality ABI AOD. By using the top 2 qualities of ABI AOD in conjunction with the bias correction algorithm, the areal coverage of ABI AOD is increased by about 100 % without loss of data accuracy.

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