Jaehwa Lee

Retrieving near-global aerosol loading over land and ocean from AVHRR

The spaceborne Advanced Very High Resolution Radiometer (AVHRR) sensor data record is approaching 40 years, providing a crucial asset for studying long-term trends of aerosol properties regionally and globally. However, due to limitations of its channels’ information content, aerosol optical depth (AOD) data from AVHRR over land are still largely lacking. In this paper, we describe a new physics-based algorithm to retrieve aerosol loading over both land and ocean from AVHRR for the first time. The over-land algorithm is an extension of our Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue algorithm, while a simplified version of our Satellite Ocean Aerosol Retrieval (SOAR) algorithm is used over ocean. We compare retrieved AVHRR AOD with that from MODIS on a daily and seasonal basis, and find in general good agreement between the two. For the satellites with equatorial crossing times within two hours of solar noon, the spatial coverage of the AVHRR aerosol product is comparable to that of MODIS, except over very bright arid regions (such as the Sahara), where the underlying surface reflectance at 630 nm reaches the critical surface reflectance. Based upon comparisons of the AVHRR AOD against Aerosol Robotic Network (AERONET) data, preliminary results indicate that the expected error confidence interval envelope is around ±(0.03+15%) over ocean and ±(0.05+25%) over land for this first version of the AVHRR aerosol products. Consequently, these new AVHRR aerosol products can contribute important building blocks for constructing a consistent long-term data record for climate studies.

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 VIIRSs 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.

Retrieving the height of smoke and dust aerosols by synergistic use of VIIRS, OMPS, and CALIOP observations

This study extends the application of the previously developed Aerosol Single-scattering albedo and layer Height Estimation (ASHE) algorithm, which was originally applied to smoke aerosols only, to both smoke and dust aerosols by including nonspherical dust properties in the retrieval process. The main purpose of the algorithm is to derive aerosol height information over wide areas using aerosol products from multiple satellite sensors simultaneously: aerosol optical depth (AOD) and Angstrom exponent from the Visible Infrared Imaging Radiometer Suite (VIIRS), UV aerosol index from the Ozone Mapping and Profiler Suite (OMPS), and total backscatter coefficient profile from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The case studies suggest that the ASHE algorithm performs well for both smoke and dust aerosols, showing root-mean-square error of the retrieved aerosol height as compared to CALIOP observations from 0.58 to 1.31 km and mean bias from −0.70 to 1.13 km. In addition, the algorithm shows the ability to retrieve single-scattering albedo to within 0.03 of Aerosol Robotic Network inversion data for moderate to thick aerosol loadings (AOD of ~1.0). For typical single-layered aerosol cases, the estimated uncertainty in the retrieved height ranges from 1.20 to 1.80 km over land and from 1.15 to 1.58 km over ocean when favorable conditions are met. Larger errors are observed for multilayered aerosol events, due to the limited sensitivities of the passive sensors to such cases.

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.

Validation of aerosol type classification from satellite remote sensing

Inter-comparison of various satellite data is performed for the purpose of validation of aerosol type classification algorithm from satellite remote sensing, so called, MODIS-OMI algorithm (MOA hereafter). Infrared Optical Depth Index (IODI), correlation coefficient between carbon monoxide (CO) column density and black carbon (BC) aerosol optical thickness (AOT), and aerosol types from 4-channel algorithm and CALIOP measurements are used to validate dust, BC, and aerosol type from MOA, respectively. The agreement of dust pixels between IODI and MOA ranges 0.1 to 0.6 with respect to AOT constraint, and it is inferred that IODI is less sensitive to optically thin dust layer. Increase of the correlation coefficient between AOT and CO column density when BC pixels are taken into account supports the performance of MOA to detect BC aerosol. The agreement of aerosol types from MOA and 4CA showed reasonable consistency, and the difference can be described by different absorptivity test and retrieval accuracy of AE. Intercomparison of aerosol types between MOA and CALIOP measurements represented reasonable consistency when AOT greater than 0.5, and height dependence of MOA is inferred from consistency analysis with respect to aerosol layer height from CALIOP measurements. Inter-comparisons among different satellite data showed feasible future for validating aerosol type classification algorithm from satellite remote sensing.

Validation of Aerosol Type Classification from Satellite Remote Sensing

Inter‐comparison of various satellite data is performed for the purpose of validation of aerosol type classification algorithm from satellite remote sensing, so called, MODIS‐OMI algorithm (MOA hereafter). Infrared Optical Depth Index (IODI), correlation coefficient between carbon monoxide (CO) column density and black carbon (BC) aerosol optical thickness (AOT), and aerosol types from 4‐channel algorithm and CALIOP measurements are used to validate dust, BC, and aerosol type from MOA, respectively. The agreement of dust pixels between IODI and MOA ranges 0.1 to 0.6 with respect to AOT constraint, and it is inferred that IODI is less sensitive to optically thin dust layer. Increase of the correlation coefficient between AOT and CO column density when BC pixels are taken into account supports the performance of MOA to detect BC aerosol. The agreement of aerosol types from MOA and 4 CA showed reasonable consistency, and the difference can be described by different absorptivity test and retrieval accuracy of...

Intercomparison of Shortwave Radiative Transfer Models for Aerosol-laden Atmospheres

Intercomparison among the three radiative transfer models (RTMs) which have been used in the studies for COMS, was carried out on the condition of aerosol-laden atmospheres. Also the role of aerosols in the atmospheric radiation budget was analyzed. The results (hereafter referred to as H15) from Halthore et al.`s study (2005) were used as a benchmark to examine the models. Aerosol Radiative Forcing (ARF) values from the three RTMs, calculated under two conditions of Aerosol Optical Thickness (AOT

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 NASAs 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 NASAs 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.

Aerosol monitoring by the Geostationary Ocean Color Imager

Atmospheric aerosols have important physical and chemical effects on Earth’s climate and on the radiation reaching its surface. The environment and climate of East Asia, in particular, are affected by the large amounts of aerosols in the region, which are of several types with different effects. Numerous studies have retrieved aerosol optical properties from multiple visible (VIS)/near-IR (NIR) channels, such as the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Medium-spectral Resolution Imaging Spectrometer (MERIS). However, all of these sensors, aboard low Earth orbit satellites, have provided aerosol information only once a day. The Geostationary Ocean Color Imager (GOCI) is the first multi-channel VIS/NIR ocean color sensor operating in geostationary orbit. It is located onboard the Communication, Ocean, and Meteorological Satellite (COMS) launched on 27 June 2010 to observe ocean color around the Korean Peninsula. The GOCI has eight spectral channels at 412, 443, 490, 555, 660, 680, 745, and 865nm, with spatial coverage of 2500km 2500km centered at 36N and 130E and a resolution of 500m.1 By taking advantage of a geostationary platform, GOCI can provide hourly spectral images that can be used for continuous monitoring of aerosols as well as ocean color over cloud-free areas. Aerosol optical properties can be retrieved from the reflectance measured by the GOCI. In this article, we introduce an aerosol retrieval algorithm that has been developed for the GOCI.2 The algorithm retrieves the aerosol optical depth (AOD), fine-mode fraction (FMF), and Figure 1. An example of a lookup table (LUT) computed by using the radiative transfer model rstar5b developed at the University of Tokyo for different aerosol loadings (AOD at 550nm) and FMF, assuming a combination of non-absorbing fine-mode and dust aerosols. TOA: Time of arrival.

Wavelength Dependency of Aerosol Optical Properties in Seoul

In this study, wavelength dependency of aerosol was investigated using the correlation between Single scattering albedo (SSA) and Angstrom exponent (AE), the time series of SSA difference in accordance with wavelength, and the relationship between SSA and AE ratio which defined AE at longer wavelength pair over AE at shorter wavelength pair. All used data in this study are daily mean values. The positive correlation between SSA and AE becomes clear as the wavelength pair of AE becomes longer, however negative correlation with AE at shorter wavelength pair also cannot be neglected. Accordingly, the correlation is well negative between SSA at longer wavelength (LW‐SSA) and AE at shorter wavelength pair (SW‐AE), opposite to the well positive correlation between SSA at shorter wavelength (SW‐SSA) and AE at longer wavelength pair (LW‐AE). SSA also shows the interesting relationship with AE ratio. In this study, AE ratio is defined as AE at 675–1020 nm over AE at 340–675 nm. Their relation shows the quadratic c...