scholarly journals Estimation of aerosol optical depth over Dehradun (India) using simple model for atmospheric radiative transfer in multiple scattering approximation

2014 ◽  
Vol XL-8 ◽  
pp. 271-276
Author(s):  
M. Mehta
Keyword(s):  
Simple Model ◽  
Radiative Transfer ◽  
Multiple Scattering ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Land Surface ◽  
Accurate Estimate ◽  
Scattering Approximation ◽  
Modis Aod ◽  
Atmospheric Radiative Transfer

Aerosol optical depth retrieval over land surface using remote sensing employs the use of radiative transfer simulations and/or simultaneous measurements of atmospheric parameters at the time of satellite pass. Also, an accurate estimate of land surface parameters is also required in order to separate the atmospheric component from the land surface reflectance reaching at-sensor. In addition to empirical and semi-empirical approaches, amongst the most widely used methods to retrieve the aerosol properties from satellite measurements are radiative transfer codes used in either forward or inverse modes. As most of them are computationally complex, henceforth, efforts are made to formulate approximate models. In this study, we have tried to estimate aerosol optical depth using one such established physically based model, namely, SMART (Simple Model for Atmospheric Radiative Transfer) code in multiple scattering approximation for aerosols over first band (0.52–0.59 μm) of RESOURCESAT-AWiFS sensor. The aim of the analysis was to find out an approach to decouple aerosol effects from Top of atmosphere signals recorded by AWiFS sensor using multiple scattering approximations for aerosols. The model is first calibrated for aerosol asymmetry parameter for one dataset each of summer and winter seasons respectively and subsequently validated for 4 different datasets (2 summer and 2 winter) against the MODIS atmosphere product for aerosol optical depth. The results show that the difference between simulated vs. MODIS AOD fall within MODIS expected errors for the aerosol product.

scholarly journals Improving GOES Advanced Baseline Imager (ABI) Aerosol Optical Depth (AOD) Retrievals using an Empirical Bias Correction Algorithm

2020 ◽  
Author(s):  
Hai Zhang ◽  
Shobha Kondragunta ◽  
Istvan Laszlo ◽  
Mi Zhou
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Bias Correction ◽  
Land Surface ◽  
Infrared Imaging ◽  
Retrieval Algorithm ◽  
Correction Algorithm ◽  
Time Step ◽  
Modis Aod ◽  
Moderate Resolution Imaging Spectroradiometer

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 multi-band algorithm similar to those of polar-orbiting satellites’ sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). Therefore, ABI AOD is expected to have accuracy and precision comparable to MODIS AOD and VIIRS AOD. However, this work demonstrates that the current version of GOES-16 (GOES-East) ABI AOD has diurnally varying biases due to errors in the land surface reflectance relationship between the bands used in the ABI AOD retrieval algorithm, which vary with respect to the Sun-satellite geometry. To reduce these biases, an empirical bias correction algorithm has been developed based on the lowest observed ABI AOD of an adjacent 30-day 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 August 6 to December 31, 2018 are used to validate the bias correction algorithm. For the top 2 qualities ABI AOD, after bias correction, the correlation between 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 uncorrected high-quality ABI AOD. Thus, by using the top 2 qualities of ABI AOD in conjunction with the bias correction algorithm, the area coverage of ABI AOD is substantially increased without loss of data accuracy.

scholarly journals Effective aerosol optical depth from pyranometer measurements of surface solar radiation (global radiation) at Thessaloniki, Greece

2012 ◽  
Vol 12 (12) ◽  
pp. 33265-33289
Author(s):  
A. V. Lindfors ◽  
N. Kouremeti ◽  
A. Arola ◽  
S. Kazadzis ◽  
A. F. Bais ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Atmospheric Aerosol ◽  
Global Radiation ◽  
Single Scattering ◽  
Surface Radiation ◽  
Surface Solar Radiation ◽  
Long Time ◽  
Source Of Information

Abstract. Pyranometer measurements of the solar surface radiation (SSR) are available at many locations worldwide, often as long time series covering several decades into the past. These data constitute a potential source of information on the atmospheric aerosol load. Here, we present a method for estimating the aerosol optical depth (AOD) using pyranometer measurements of the SSR together with total water vapor column information. The method, which is based on radiative transfer simulations, was developed and tested using recent data from Thessaloniki, Greece. The effective AOD calculated using this method was found to agree well with co-located AERONET measurements, exhibiting a correlation coefficient of 0.9 with 2/3 of the data found within ±20% or ±0.05 of the AERONET AOD. This is similar to the performance of current satellite aerosol methods. Differences in the AOD as compared to AERONET can be explained by variations in the aerosol properties of the atmosphere that are not accounted for in the idealized settings used in the radiative transfer simulations, such as variations in the single scattering albedo and Ångström exponent. Furthermore, the method is sensitive to calibration offsets between the radiative transfer simulations and the pyranometer SSR. The method provides an opportunity of extending our knowledge of the atmospheric aerosol load to locations and times not covered by dedicated aerosol measurements.

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.

scholarly journals Using the OMI Aerosol Index and Absorption Aerosol Optical Depth to evaluate the NASA MERRA Aerosol Reanalysis

2014 ◽  
Vol 14 (23) ◽  
pp. 32177-32231 ◽  
Author(s):  
V. Buchard ◽  
A. M. da Silva ◽  
P. R. Colarco ◽  
A. Darmenov ◽  
C. A. Randles ◽  
...  
Keyword(s):  
Optical Properties ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Biomass Burning ◽  
Saharan Dust ◽  
Aerosol Layer ◽  
Anthropogenic Aerosols ◽  
Aerosol Absorption ◽  
Modis Aod ◽  
Aerosol Index

Abstract. A radiative transfer interface has been developed to simulate the UV Aerosol Index (AI) from the NASA Goddard Earth Observing System version 5 (GEOS-5) aerosol assimilated fields. The purpose of this work is to use the AI and Aerosol Absorption Optical Depth (AAOD) derived from the Ozone Monitoring Instrument (OMI) measurements as independent validation for the Modern Era Retrospective analysis for Research and Applications Aerosol Reanalysis (MERRAero). MERRAero is based on a version of the GEOS-5 model that is radiatively coupled to the Goddard Chemistry, Aerosol, Radiation, and Transport (GOCART) aerosol module and includes assimilation of Aerosol Optical Depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Since AI is dependent on aerosol concentration, optical properties and altitude of the aerosol layer, we make use of complementary observations to fully diagnose the model, including AOD from the Multi-angle Imaging SpectroRadiometer (MISR), aerosol retrievals from the Aerosol Robotic Network (AERONET) and attenuated backscatter coefficients from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission to ascertain potential misplacement of plume height by the model. By sampling dust, biomass burning and pollution events in 2007 we have compared model produced AI and AAOD with the corresponding OMI products, identifying regions where the model representation of absorbing aerosols was deficient. As a result of this study over the Saharan dust region, we have obtained a new set of dust aerosol optical properties that retains consistency with the MODIS AOD data that were assimilated, while resulting in better agreement with aerosol absorption measurements from OMI. The analysis conducted over the South African and South American biomass burning regions indicates that revising the spectrally-dependent aerosol absorption properties in the near-UV region improves the modeled-observed AI comparisons. Finally, during a period where the Asian region was mainly dominated by anthropogenic aerosols, we have performed a qualitative analysis in which the specification of anthropogenic emissions in GEOS-5 is adjusted to provide insight into discrepancies observed in AI comparisons.

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