Validation, inter-comparison, and usage recommendation of six latest VIIRS and MODIS aerosol products over the ocean and land on the global and regional scales

MODIS and VIIRS aerosol products have been used extensively by the scientific community. Products in operation include MODIS Dark Target (DT), Deep Blue (DB), and Multi-Angle Implementation of Atmospheric Correction (MAIAC) and VIIRS DT, DB, and NOAA Environmental Data Record products. This study comprehensively validated and inter-compared aerosol optical depth (AOD) and Ångstrom exponent (AE) over land and the ocean of these six products (seven different algorithms) on regional and global scales using AErosol RObotic NETwork (AERONET) and Maritime Aerosol Network (MAN) observations. In particular, we used AERONET inversions to classify AOD and AE biases into different scenarios (depending on absorption and particle size) to obtain retrieval error characteristics. The spatial patterns of the products and their differences were also analyzed. Collectively, although six satellite AODs are in good agreement with ground observations, VIIRS DB (land and ocean) and MODIS MAIAC (land only) AODs show better validation metrics globally and better performance in 8/10 world regions. Therefore, they are more recommended for usage. Although land AE retrievals are not capable of quantitative application at both instantaneous and monthly scales, their spatial patterns show qualitative potential. Ocean AE shows a relatively high correlation coefficient with ground measurements (>0.75), meeting the fraction of expected accuracy (> 0.70). Error characteristic analyses emphasize the importance of aerosol particle size and absorption-scattering properties for land retrieval, indicating that improving the representation of aerosol types is necessary. This study is expected to facilitate the usage selection of operating VIIRS and MODIS products and their algorithm improvements.

Assessment of CALIOP-Derived CCN Concentrations by In Situ Surface Measurements

The satellite-based cloud condensation nuclei (CCN) proxies used to quantify the aerosol-cloud interactions (ACIs) are column integrated and do not guarantee the vertical co-location of aerosols and clouds. This has encouraged the use of height-resolved measurements of spaceborne lidars for ACI studies and led to advancements in lidar-based CCN retrieval algorithms. In this study, we present a comparison between the number concentration of CCN (nCCN) derived from ground-based in situ and spaceborne lidar cloud-aerosol lidar with orthogonal polarization (CALIOP) measurements. On analysing their monthly time series, we found that about 88% of CALIOP nCCN estimates remained within a factor of 1.5 of the in situ measurements. Overall, the CALIOP estimates of monthly nCCN were in good agreement with the in situ measurements with a normalized mean error of 71%, normalized mean bias of 39% and correlation coefficient of 0.68. Based on our comparison results, we point out the necessary measures that should be considered for global nCCN retrieval. Our results show the competence of CALIOP in compiling a global height- and type-resolved nCCN dataset for use in ACI studies.

A Coupled Evaluation of Operational MODIS and Model Aerosol Products for Maritime Environments Using Sun Photometry: Evaluation of the Fine and Coarse Mode

Although satellite retrievals and data assimilation have progressed to where there is a good skill for monitoring maritime Aerosol Optical Depth (AOD), there remains uncertainty in achieving further degrees of freedom, such as distinguishing fine and coarse mode dominated species in maritime environments (e.g., coarse mode sea salt and dust versus fine mode terrestrial anthropogenic emissions, biomass burning, and maritime secondary production). For the years 2016 through 2019, we performed an analysis of 550 nm total AOD550, fine mode AOD (FAOD550; also known as FM AOD in the literature), coarse mode AOD (CAOD550), and fine mode fraction (η550) between Moderate Resolution Spectral Imaging Radiometer (MODIS) V6.1 MOD/MYD04 dark target aerosol retrievals and the International Cooperative for Aerosol Prediction (ICAP) core four multi-model consensus (C4C) of analyses/short term forecasts that assimilate total MODIS AOD550. Differences were adjudicated by the global shipboard Maritime Aerosol Network (MAN) and selected island AERONET sun photometer observations with the application of the spectral deconvolution algorithm (SDA). Through a series of conditional and regional analyses, we found divergence included regions of terrestrial influence and latitudinal dependencies in the remote oceans. Notably, MODIS and the C4C and its members, while having good correlations overall, have a persistent +0.04 to +0.02 biases relative to MAN and AERONET for typical AOD550 values (84th% < 0.28), with the C4C underestimating significant events thereafter. Second, high biases in AOD550 are largely associated with the attribution of the fine mode in satellites and models alike. Thus, both MODIS and C4C members are systematically overestimating AOD550 and FAOD550 but perform better in characterizing the CAOD550. Third, for MODIS, findings are consistent with previous reports of a high bias in the retrieved Ångström Exponent, and we diagnosed both the optical model and cloud masking as likely causal factors for the AOD550 and FAOD550 high bias, whereas for the C4C, it is likely from secondary overproduction and perhaps numerical diffusion. Fourth, while there is no wind-speed-dependent bias for surface winds <12 m s−1, the C4C and MODIS AOD550s also overestimate CAOD550 and FAOD550, respectively, for wind speeds above 12 m/s. Finally, sampling bias inherent in MAN, as well as other circumstantial evidence, suggests biases in MODIS are likely even larger than what was diagnosed here. We conclude with a discussion on how MODIS and the C4C products have their own strengths and challenges for a given climate application and discuss needed research.

Retrieval and Validation of AOD from Himawari-8 Data over Bohai Rim Region, China

The geostationary satellite Himawari-8, possessing the Advanced Himawari Imager (AHI), which features 16 spectral bands from the visible to infrared range, is suitable for aerosol observations. In this study, a new algorithm is introduced to retrieve aerosol optical depth (AOD) over land at a resolution of 2 km from the AHI level 1 data. Considering the anisotropic effects of complex surface structures over land, Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) model parameters product (MCD19A3) is used to calculate the surface reflectance for Himawari-8’s view angle and band. In addition, daily BRDF model parameters are calculated in areas with dense vegetation, considering the rapid variation of surface reflectance caused by vegetation growth. Moreover, aerosol models are constructed based on long duration Aerosol Robotic Network (AERONET) single scattering albedo (SSA) values to stand for aerosol types in the retrieval algorithm. The new algorithm is applied to AHI images over Bohai Rim region from 2018 and is evaluated using the newest AERONET version 3 AOD measurements and the latest MODIS collection 6.1 AOD products. The AOD retrievals from the new algorithm show good agreement with the AERONET AOD measurements, with a correlation coefficient of 0.93 and root mean square error (RMSE) of 0.12. In addition, the new algorithm increases AOD retrievals and retrieval accuracy compared to the Japan Aerospace Exploration Agency (JAXA) aerosol products. The algorithm shows stable performance during different seasons and times, which makes it possible for use in climate or diurnal aerosol variation studies.

Primary Evaluation of the GCOM-C Aerosol Products at 380 nm Using Ground-Based Sky Radiometer Observations

The Global Change Observation Mission-Climate (GCOM-C) is currently the only satellite sensor providing aerosol optical thickness (AOT) in the ultraviolet (UV) region during the morning overpass time. The observations in the UV region are important to detect the presence of absorbing aerosols in the atmosphere. The recently available GCOM-C dataset of AOT at 380 nm for January to September 2019 were evaluated using ground-based SKYNET sky radiometer measurements at Chiba, Japan (35.62° N, 140.10° E) and Phimai, central Thailand (15.18° N, 102.56° E), representing urban and rural sites, respectively. AOT retrieved from sky radiometer observations in Chiba and Phimai was compared with coincident AERONET and multi-axis differential optical absorption spectroscopy (MAX-DOAS) AOT values, respectively. Under clear sky conditions, the datasets showed good agreement. The sky radiometer and GCOM-C AOT values showed a positive correlation (R) of ~0.73 for both sites, and agreement between the datasets was mostly within ±0.2 (the number of coincident points at both sites was less than 50 for the coincidence criterion of ≤30 km). At Chiba, greater differences in the AOT values were primarily related to cloud screening in the datasets. The mean bias error (MBE) (GCOM-C – sky radiometer) for the Chiba site was −0.02 for a coincidence criterion of ≤10 km. For a similar coincidence criterion, the MBE values were higher for observations at the Phimai site. This difference was potentially related to the strong influence of biomass burning during the dry season (Jan–Apr). The diurnal variations in AOT, inferred from the combination of GCOM-C and ozone monitoring instrument (OMI) observations, showed good agreement with the sky radiometer data, despite the differences in the absolute AOT values. Over Phimai, the AOT diurnal variations from the satellite and sky radiometer observations were different, likely due to the large differences in the AOT values during the dry season.

Evaluation of Himawari-8/AHI, MERRA-2, and CAMS Aerosol Products over China

Reliable aerosol optical depth (AOD) data with high spatial and temporal resolutions are needed for research on air pollution in China. AOD products from the Advanced Himawari Imager (AHI) onboard the geostationary Himawari-8 satellite and reanalysis datasets make it possible to capture diurnal variations of aerosol loadings. However, due to the different retrieval methods, their applicability may vary with different space and time. Thus, in this study, taking the measured AOD at the Aerosol Robotic NETwork (AERONET) stations as the gold standard, the performance of the latest AHI hourly AOD product (i.e., L3 AOD) was evaluated and then compared with that of two reanalysis AOD datasets offered by Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS), respectively, covering from July 2015 to December 2017 over China. For all the matchups, AHI AOD shows the highest robustness with a high correlation (R) of 0.82, low root-mean-square error (RMSE) of 0.23, and moderate mean absolute relative error (MARE) of 0.56. Although MERRA-2 and CAMS products both have lower R values (0.74, 0.72) and higher RMSE (0.28, 0.26), the former is slightly better than the latter. Accuracy of AOD products could be mainly affected by the pollution level and less affected by particle size distribution. Comparisons among these AOD products imply that AHI AOD is more reliable in regions with high pollution levels, such as central and eastern China, while in the northern and western part, MERRA-2 AOD seems more satisfying. The performance of all the three AOD products presents a significant diurnal variety, as indicated by the highest accuracy in the morning for AHI and at noon for reanalysis data. Moreover, due to various pollution distribution patterns and meteorological conditions, there are distinct seasonal characteristics in the performance of AOD products for different regions.

Evaluation of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) Aerosol Algorithm for Himawari-8 Data

Himawari-8, operated by the Japan Meteorological Agency (JMA), is a new generation geostationary satellite that provides remote sensing data to retrieve atmospheric aerosol optical depth (AOD) at high spatial (1 km) and high temporal (10 min) resolutions. The Geostationary- National Aeronautics and Space Administration (NASA) Earth exchange (GeoNEX) project recently adapted the multiangle implementation of atmospheric correction (MAIAC) algorithm, originally developed for joint retrieval of AOD and surface anisotropic reflectance with the moderate resolution imaging spectroradiometer (MODIS) data, to generate Earth monitoring products from the latest geostationary satellites including Himawari-8. This study evaluated the GeoNEX Himawari-8 ~1 km MAIAC AOD retrieved over all the aerosol robotic network (AERONET) sites between 6°N–30°N and 91°E–127°E. The corresponding JMA Himawari-8 AOD products were also evaluated for comparison. We only used cloud-free and the best quality satellite AOD retrievals and compiled a total of 16,532 MAIAC-AERONET and 21,737 JMA-AERONET contemporaneous pairs of AOD values for 2017. Statistical analyses showed that both MAIAC and JMA data are highly correlated with AERONET AOD, with the correlation coefficient (R) of ~0.77, and the root mean squared error (RMSE) of ~0.16. The absolute bias of MAIAC AOD (0.02 overestimation) appears smaller than that of the JMA AOD (0.05 underestimation). In comparison with the JMA data, the time series of MAIAC AOD were more consistent with AERONET AOD values and better capture the diurnal variations of the latter. The dependence of MAIAC AOD bias on scattering angles is also discussed.

Assessment of Himawari-8 AHI Aerosol Optical Depth Over Land

This study conducted the first comprehensive assessment of the aerosol optical depth (AOD) product retrieved from the observations by the Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite. The AHI Level 3 AOD (Version 3.0) was evaluated using the collocated Aerosol Robotic Network (AERONET) level 2.0 direct sun AOD measurements over the last three years (May 2016–December 2018) at 58 selected AERONET sites. A comprehensive comparison between AHI and AERONET AOD was carried out, which yielded a correlation coefficient (R) of 0.82, a slope of 0.69, and a root mean square error (RMSE) of 0.16. The results indicate a good agreement between AHI and AERONET AOD, while revealing that the AHI aerosol retrieval algorithm tends to underestimate the atmospheric aerosol load. In addition, the expected uncertainty of AHI Level 3 AOD (Version 3.0) is ± (0.1 + 0.3 × AOD). Furthermore, the performance of the AHI aerosol retrieval algorithm exhibits regional variation. The best performance is reported over East Asia (R 0.86), followed by Southeast Asia (R 0.79) and Australia (R 0.35). The monthly and seasonal comparisons between AHI and AERONET show that the best performance is found in summer (R 0.93), followed by autumn (R 0.84), winter (R 0.82), and spring (R 0.76). The worst performance was observed in March (R 0.75), while the best performance appeared in June (R 0.94). The variation in the annual mean AHI AOD on the scale of hours demonstrates that AHI can perform continuous (no less than ten hours) aerosol monitoring.

A Short Note on the Potential of Utilization of Spectral AERONET-Derived Depolarization Ratios for Aerosol Classification

We herein present the spectral linear particle depolarization ratios (δp) from an Aerosol Robotics NETwork (AERONET) sun/sky radiometer with respect to the aerosol type. AERONET observation sites, which are representative of each aerosol type, were selected for our study. The observation data were filtered using the Ångström exponent (Å), fine-mode fraction (FMF) and single scattering albedo (ω) to ensure that the obtained values of δp were representative of each aerosol condition. We report the spectral δp values provided in the recently released AERONET version 3 inversion product for observation of the following aerosol types: dust, polluted dust, smoke, non-absorbing, moderately-absorbing and high-absorbing pollution. The AERONET-derived δp values were generally within the range of the δp values measured from lidar observations for each aerosol type. In addition, it was found that the spectral variation of δp differed according to the aerosol type. From the obtained results, we concluded that our findings provide potential insight into the identification and classification of aerosol types using remote sensing techniques.

Satellite Ocean Aerosol Retrieval (SOAR) algorithm extension to S-NPP VIIRS as part of the 'Deep Blue' aerosol project

The Suomi National Polar-Orbiting Partnership (S-NPP) satellite, launched in late 2011, carries the Visible Infrared Imaging Radiometer Suite (VIIRS) and several other instruments. VIIRS has similar characteristics to prior satellite sensors used for aerosol optical depth (AOD) retrieval, allowing the continuation of space-based aerosol data records. The Deep Blue algorithm has previously been applied to retrieve AOD from Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectro-radiometer (MODIS) measurements over land. The SeaWiFS Deep Blue data set also included a SeaWiFS Ocean Aerosol Retrieval (SOAR) algorithm to cover water surfaces. As part of NASA's VIIRS data processing, Deep Blue is being applied to VIIRS data over land, and SOAR has been adapted from SeaWiFS to VIIRS for use over water surfaces. This study describes SOAR as applied in version 1 of NASA's S-NPP VIIRS Deep Blue data product suite. Several advances have been made since the SeaWiFS application, as well as changes to make use of the broader spectral range of VIIRS. A preliminary validation against Maritime Aerosol Network (MAN) measurements suggests a typical uncertainty on retrieved 550nm AOD of order ±(0.03+10%), comparable to existing SeaWiFS/MODIS aerosol data products. Retrieved Ångström exponent and fine mode AOD fraction are also well-correlated with MAN data, with small biases and uncertainty similar to or better than SeaWiFS/MODIS products.

The CALIPSO Version 4 Automated Aerosol Classification and Lidar Ratio Selection Algorithm

The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) version 4.10 (V4) level 2 aerosol data products, released in November 2016, include substantial improvements to the aerosol subtyping and lidar ratio selection algorithms. These improvements are described along with resulting changes in aerosol optical depth (AOD). The most fundamental change in V4 level 2 aerosol products is a new algorithm to identify aerosol subtypes in the stratosphere. Four aerosol subtypes are introduced for the stratospheric aerosols: polar stratospheric aerosol (PSA), volcanic ash, sulfate/other, and smoke. The tropospheric aerosol subtyping algorithm was also improved by adding the following enhancements: (1) all aerosol subtypes are now allowed over polar regions, whereas the version 3 (V3) algorithm allowed only clean continental and polluted continental aerosols; (2) a new "dusty marine" aerosol subtype is introduced, representing mixtures of dust and marine aerosols near the ocean surface; and (3) the "polluted continental" and "smoke" subtypes have been renamed "polluted continental/smoke" and "elevated smoke", respectively. V4 also revises the lidar ratios for clean marine, dust, clean continental, and elevated smoke subtypes. As a consequence of the V4 updates, the mean 532 nm AOD retrieved by CALIOP has increased by 0.044 (0.036) or 52 % (40 %) for nighttime (daytime). Lidar ratio revisions are the most influential factor for AOD changes from V3 to V4, especially for cloud-free skies. Preliminary validation studies show that the AOD discrepancies between CALIOP and AERONET/MODIS (ocean) are reduced in V4 compared to V3.

The CALIPSO Version 4 Automated Aerosol Classification and Lidar Ratio Selection Algorithm

The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) version 4.10 (V4) level 2 aerosol data products, released in November 2016, include substantial improvements to the aerosol subtyping and lidar ratio selection algorithms. These improvements are described along with resulting changes in aerosol optical depth (AOD). The most fundamental change in V4 level 2 aerosol products is a new algorithm to identify aerosol subtypes in the stratosphere. Four aerosol subtypes are introduced for the stratospheric aerosols: polar stratospheric aerosol (PSA), volcanic ash, sulfate/other, and smoke. The tropospheric aerosol subtyping algorithm was also improved by adding the following enhancements: (1) all aerosol subtypes are now allowed over polar regions, whereas the version 3 (V3) algorithm allowed only clean continental and polluted continental aerosols; (2) a new "dusty marine" aerosol subtype is introduced, representing mixtures of dust and marine aerosols near the ocean surface; and (3) the "polluted continental" and "smoke" subtypes have been renamed "polluted continental/smoke" and "elevated smoke", respectively. V4 also revises the lidar ratios for clean marine, dust, clean continental, and elevated smoke subtypes. As a consequence of the V4 updates, the mean 532 nm AOD retrieved by CALIOP has increased by 0.044 (0.036) or 52 % (40 %) for nighttime (daytime). Lidar ratio revisions are the most influential factor for AOD changes from V3 to V4, especially for cloud-free skies. Preliminary validation studies show that the AOD discrepancies between CALIOP and AERONET/MODIS (ocean) are reduced in V4 compared to V3.

Minimum aerosol layer detection sensitivities and their subsequent impacts on aerosol optical thickness retrievals in CALIPSO level 2 data products

Due to instrument sensitivities and algorithm detection limits, level 2 (L2) Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) 532nm aerosol extinction profile retrievals are often populated with retrieval fill values (RFVs), which indicate the absence of detectable levels of aerosol within the profile. In this study, using 4 years (2007-2008 and 2010-2011) of CALIOP version 3 L2 aerosol data, the occurrence frequency of daytime CALIOP profiles containing all RFVs (all-RFV profiles) is studied. In the CALIOP data products, the aerosol optical thickness (AOT) of any all-RFV profile is reported as being zero, which may introduce a bias in CALIOP-based AOT climatologies. For this study, we derive revised estimates of AOT for all-RFV profiles using collocated Moderate Resolution Imaging Spectroradiometer (MODIS) Dark Target (DT) and, where available, AErosol RObotic NEtwork (AERONET) data. Globally, all-RFV profiles comprise roughly 71% of all daytime CALIOP L2 aerosol profiles (i.e., including completely attenuated profiles), accounting for nearly half (45 %) of all daytime cloud-free L2 aerosol profiles. The mean collocated MODIS DT (AERONET) 550 nm AOT is found to be near 0.06 (0.08) for CALIOP all-RFV profiles. We further estimate a global mean aerosol extinction profile, a so-called "noise floor", for CALIOP all-RFV profiles. The global mean CALIOP AOT is then recomputed by replacing RFV values with the derived noise-floor values for both all-RFV and non-all-RFV profiles. This process yields an improvement in the agreement of CALIOP and MODIS over-ocean AOT.

Evaluation of NASA Deep Blue/SOAR aerosol retrieval algorithms applied to AVHRR measurements

The Deep Blue (DB) and Satellite Ocean Aerosol Retrieval (SOAR) algorithms have previously been applied to observations from sen-sors like the Moderate Resolution Imaging Spectroradiometers (MODIS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) to provide records of mid-visible aerosol optical depth (AOD) and related quantities over land and ocean surfaces respectively. Recently, DB and SOAR have also been applied to Ad-vanced Very High Resolution Radiometer (AVHRR) observations from several platforms (NOAA11, NOAA14, and NOAA18), to demonstrate the potential for extending the DB and SOAR AOD records. This study provides an evaluation of the initial version (V001) of the resulting AVHRR-based AOD data set, including validation against Aerosol Robotic Network (AERONET) and ship-borne observations, and comparison against both other AVHRR AOD Research (GESTAR), Universities Space Research Association. records and MODIS/SeaWiFS products at select long-term AERONET sites. Although it is difficult to distil error characteristics into a simple expression, the results suggest that one standard deviation confidence intervals on retrieved AOD of ±(0.03+15%) over water and ±(0.05+25%) over land represent the typical level of uncertainty, with a tendency towards negative biases in high-AOD conditions, caused by a combination of algorithmic assumptions and sensor calibration issues. Most of the available validation data are for NOAA18 AVHRR, although performance appears to be similar for the NOAA11 and NOAA14 sensors as well.

Cross-calibration of S-NPP VIIRS moderate resolution reflective solar bands against MODIS Aqua over dark water scenes

The Visible Infrared Imaging Radiometer Suite (VIIRS) is being used to continue the record of Earth Science observations and data products produced routinely from National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. However, the absolute calibration of VIIRS's reflected solar bands is thought to be biased, leading to offsets in derived data products such as aerosol optical depth (AOD) as compared to when similar algorithms are applied to different sensors. This study presents a cross-calibration of these VIIRS bands against MODIS Aqua over dark water scenes, finding corrections to the NASA VIIRS Level 1 (version 2) reflectances between approximately +1 % and -7 % (dependent on band) are needed to bring the two into alignment (after accounting for expected differences resulting from different band spectral response functions), and indications of relative trending of up to ^0.35 % per year in some bands. The derived calibration gain corrections are also applied to the VIIRS reflectance and then used in an AOD retrieval, and are shown to decrease the bias and total error in AOD across the midvisible spectral region compared to the standard VIIRS NASA reflectance calibration. The resulting AOD bias characteristics are similar to those of NASA MODIS AOD data products, which is encouraging in terms of multisensor data continuity.