scholarly journals Correcting for trace gas absorption when retrieving aerosol optical depth from satellite observations of reflected shortwave radiation

2018 ◽  
Vol 11 (6) ◽  
pp. 3205-3219 ◽  
Author(s):  
Falguni Patadia ◽  
Robert C. Levy ◽  
Shana Mattoo

Abstract. Retrieving aerosol optical depth (AOD) from top-of-atmosphere (TOA) satellite-measured radiance requires separating the aerosol signal from the total observed signal. Total TOA radiance includes signal from the underlying surface and from atmospheric constituents such as aerosols, clouds and gases. Multispectral retrieval algorithms, such as the dark-target (DT) algorithm that operates upon the Moderate Resolution Imaging Spectroradiometer (MODIS, on board Terra and Aqua satellites) and Visible Infrared Imaging Radiometer Suite (VIIRS, on board Suomi-NPP) sensors, use wavelength bands in “window” regions. However, while small, the gas absorptions in these bands are non-negligible and require correction. In this paper, we use the High-resolution TRANsmission (HITRAN) database and Line-By-Line Radiative Transfer Model (LBLRTM) to derive consistent gas corrections for both MODIS and VIIRS wavelength bands. Absorptions from H2O, CO2 and O3 are considered, as well as other trace gases. Even though MODIS and VIIRS bands are “similar”, they are different enough that applying MODIS-specific gas corrections to VIIRS observations results in an underestimate of global mean AOD (by 0.01), but with much larger regional AOD biases of up to 0.07. As recent studies have been attempting to create a long-term data record by joining multiple satellite data sets, including MODIS and VIIRS, the consistency of gas correction has become even more crucial.

2018 ◽  
Author(s):  
Falguni Patadia ◽  
Robert Levy ◽  
Shana Mattoo

Abstract. Retrieving aerosol optical depth (AOD) from top-of-atmosphere (TOA) satellite-measured radiance requires separating the aerosol signal from the total observed signal. Total TOA radiance includes signal from underlying surface and from atmospheric constituents such as aerosols, clouds and gases. Multispectral retrieval algorithms, such as the dark-target (DT) algorithm that operates upon Moderate Resolution Imaging Spectroradiometer (MODIS, onboard Terra and Aqua satellites) and Visible Infrared Imaging Radiometer Suite (VIIRS, onboard Suomi-NPP) sensors, use wavelength bands in “window” regions. However, while small, the gas absorptions in these bands are non-negligible and require correction. In this paper we use High-resolution TRANsmission (HITRAN) database and Line-by-Line Radiative Transfer Model (LBLRTM) to derive consistent gas corrections for both MODIS and VIIRS wavelength bands. Absorptions from H2O, CO2 and O3 are considered, as well as other trace gases. Even though MODIS and VIIRS bands are “similar”, they are different enough that applying MODIS specific gas corrections to VIIRS observations results in an underestimate of global mean AOD (by 0.01), but with much larger regional AOD biases up to 0.07. As recent studies are attempting to create a long-term data record by joining multiple satellite datasets, including MODIS and VIIRS, the consistency of gas correction becomes even more crucial.


2015 ◽  
Vol 8 (10) ◽  
pp. 4083-4110 ◽  
Author(s):  
R. C. Levy ◽  
L. A. Munchak ◽  
S. Mattoo ◽  
F. Patadia ◽  
L. A. Remer ◽  
...  

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.


2018 ◽  
Author(s):  
Pawan Gupta ◽  
Lorraine A. Remer ◽  
Robert C. Levy ◽  
Shana Mattoo

Abstract. The two MODerate Resolution Imaging Spectroradiometer (MODIS) sensors, aboard Earth Observing Satellites (EOS) Terra and Aqua, have been making aerosol observations for more than 15 years. From these observations, the MODIS dark target (DT) aerosol retrieval algorithm provides aerosol optical depth (AOD) products, globally over both land and ocean. In addition to the standard resolution product (10 × 10 km2), the MODIS collection 6 (C006) data release included a higher resolution (3 × 3 km2). Other than accommodations for the two different resolutions, the 10 km, and 3 km DT algorithms are basically the same. In this study, we perform global validation of the higher resolution AOD over global land by comparing against AERONET measurements. The MODIS-AERONET collocated data sets consist of 161,410 high-confidence AOD pairs from 2000 to 2015 for MODIS Terra and 2003 to 2015 for MODIS-Aqua. We find that 62.5 % and 68.4 % of AODs retrieved from MODIS-Terra and MODIS-Aqua, respectively, fall within previously published expected error bounds of ±(0.05 + 0.2*AOD), with a high correlation (R = 0.87). The scatter is not random but exhibits a mean positive bias of ~ 0.06 for Terra and ~ 0.03 for Aqua. These biases for the 3 km product are approximately 0.03 larger than the biases found in similar validations of the 10 km product. The validation results for the 3 km product did not have a relationship to aerosol loading (i.e. true AOD) but did exhibit dependence on quality flags, region, viewing geometry, and aerosol spatial variability. Time series of global MODIS-AERONET differences show that validation is not static, but has changed over the course of both sensors' lifetimes, with MODIS-Terra showing more change over time. The likely cause of the change of validation over time is sensor degradation, but changes in the distribution of AERONET stations and differences in the global aerosol system itself could be contributing to the temporal variability of validation.


2013 ◽  
Vol 30 (5) ◽  
pp. 929-941 ◽  
Author(s):  
Hao Zhang ◽  
Bing Zhang ◽  
Dongmei Chen ◽  
Junsheng Li ◽  
Guangning Zhao

Abstract Beer’s attenuation law is the basis for the retrieval of aerosol optical depth (AOD) from sunphotometer data. However, the filter band function causes uncertainty during the retrieval of AOD from sunphotometer data, particularly for channels covering spectral regions of strong gas absorption. In this work, the uncertainty in AOD retrieval due to the filter band function is systematically analyzed by employing fine spectral absorption cross sections obtained from the Molecular Spectroscopy and Chemical Kinetics Group and the line-by-line radiative transfer model (LBLRTM). The uncertainty in AOD retrieval includes the uncertainty due to the wings of the filter band function in the ultraviolet (UV) region and errors in the optical depth calculation for Rayleigh scattering and absorption of O3, NO2, H2O, CH4, and CO2. The results showed that 1) the uncertainty of AOD retrieval by this method, which is called the approximate AOD retrieval method, might become large when the filter band function is not well designed, particularly in the UV region; 2) in the case of a large zenith observation condition, the errors will be nonnegligible if the Rayleigh scattering optical depth is calculated at a central wavelength without including filter band function; 3) the band-weighted absorption coefficients of O3 and NO2 remain nearly constant when the gas amounts change, except in the case of questionably designed band filters; and 4) these weak-absorption optical depths for H2O, CH4, and CO2 cannot be ignored in the 1020- or 1640-nm channels, where an optical depth error of 0.01−0.02 may be introduced.


2018 ◽  
Vol 11 (5) ◽  
pp. 3145-3159 ◽  
Author(s):  
Pawan Gupta ◽  
Lorraine A. Remer ◽  
Robert C. Levy ◽  
Shana Mattoo

Abstract. In addition to the standard resolution product (10 km), the MODerate resolution Imaging Spectroradiometer (MODIS) Collection 6 (C006) data release included a higher resolution (3 km). Other than accommodations for the two different resolutions, the 10 and 3 km Dark Target (DT) algorithms are basically the same. In this study, we perform global validation of the higher-resolution aerosol optical depth (AOD) over global land by comparing against AErosol RObotic NETwork (AERONET) measurements. The MODIS–AERONET collocated data sets consist of 161 410 high-confidence AOD pairs from 2000 to 2015 for Terra MODIS and 2003 to 2015 for Aqua MODIS. We find that 62.5 and 68.4 % of AODs retrieved from Terra MODIS and Aqua MODIS, respectively, fall within previously published expected error bounds of ±(0.05 + 0.2 × AOD), with a high correlation (R= 0.87). The scatter is not random, but exhibits a mean positive bias of ∼ 0.06 for Terra and ∼ 0.03 for Aqua. These biases for the 3 km product are approximately 0.03 larger than the biases found in similar validations of the 10 km product. The validation results for the 3 km product did not have a relationship to aerosol loading (i.e., true AOD), but did exhibit dependence on quality flags, region, viewing geometry, and aerosol spatial variability. Time series of global MODIS–AERONET differences show that validation is not static, but has changed over the course of both sensors' lifetimes, with Terra MODIS showing more change over time. The likely cause of the change of validation over time is sensor degradation, but changes in the distribution of AERONET stations and differences in the global aerosol system itself could be contributing to the temporal variability of validation.


2015 ◽  
Vol 8 (7) ◽  
pp. 6877-6947 ◽  
Author(s):  
R. C. Levy ◽  
L. A. Munchak ◽  
S. Mattoo ◽  
F. Patadia ◽  
L. A. Remer ◽  
...  

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 Ångstrom 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 retrievibility 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.


2020 ◽  
Vol 12 (7) ◽  
pp. 1102
Author(s):  
Bin Zou ◽  
Ning Liu ◽  
Wei Wang ◽  
Huihui Feng ◽  
Xiangping Liu ◽  
...  

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.


2013 ◽  
Vol 52 (1) ◽  
pp. 186-196 ◽  
Author(s):  
Benjamin H. Cole ◽  
Ping Yang ◽  
Bryan A. Baum ◽  
Jerome Riedi ◽  
Laurent C.-Labonnote ◽  
...  

AbstractInsufficient knowledge of the habit distribution and the degree of surface roughness of ice crystals within ice clouds is a source of uncertainty in the forward light scattering and radiative transfer simulations of ice clouds used in downstream applications. The Moderate Resolution Imaging Spectroradiometer (MODIS) collection-5 ice microphysical model presumes a mixture of various ice crystal shapes with smooth facets, except for the compact aggregate of columns for which a severely rough condition is assumed. When compared with Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) polarized reflection data, simulations of polarized reflectance using smooth particles show a poor fit to the measurements, whereas very rough-faceted particles provide an improved fit to the polarized reflectance. In this study a new microphysical model based on a mixture of nine different ice crystal habits with severely roughened facets is developed. Simulated polarized reflectance using the new ice habit distribution is calculated using a vector adding–doubling radiative transfer model, and the simulations closely agree with the polarized reflectance observed by PARASOL. The new general habit mixture is also tested using a spherical albedo differences analysis, and surface roughening is found to improve the consistency of multiangular observations. These results are consistent with previous studies that have used polarized reflection data. It is suggested that an ice model incorporating an ensemble of different habits with severely roughened surfaces would potentially be an adequate choice for global ice cloud retrievals.


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