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All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

An Optimal-Estimation-Based Aerosol Retrieval Algorithm Using OMI Near-UV Observations

An optimal-estimation(OE)-based aerosol retrieval algorithm using the OMI (Ozone Monitoring Instrument) near-ultraviolet observation was developed in this study. The OE-based algorithm has the merit of providing useful estimates of errors simultaneously with the inversion products. Furthermore, instead of using the traditional lookup tables for inversion, it performs online radiative transfer calculations with the VLIDORT (linearized pseudo-spherical vector discrete ordinate radiative transfer code) to eliminate interpolation errors and improve stability. The measurements and inversion products of the Distributed Regional Aerosol Gridded Observation Network campaign in northeast Asia (DRAGON NE-Asia 2012) were used to validate the retrieved aerosol optical thickness (AOT) and single scattering albedo (SSA). The retrieved AOT and SSA at 388 nm have a correlation with the Aerosol Robotic Network (AERONET) products that is comparable to or better than the correlation with the operational product during the campaign. The OEbased estimated error represented the variance of actual biases of AOT at 388 nm between the retrieval and AERONET measurements better than the operational error estimates. The forward model parameter errors were analyzed separately for both AOT and SSA retrievals. The surface reflectance at 388 nm, the imaginary part of the refractive index at 354 nm, and the number fine-mode fraction (FMF) were found to be the most important parameters affecting the retrieval accuracy of AOT, while FMF was the most important parameter for the SSA retrieval. The additional information provided with the retrievals, including the estimated error and degrees of freedom, is expected to be valuable for relevant studies. Detailed advantages of using the OE method were described and discussed in this paper.

Air Pollution↗
NASA NTRS2000

Validation of MODIS Aerosol Retrievals during PRIDE

The Puerto Rico Dust Experiment (PRIDE) was held in Roosevelt Roads, Puerto Rico from June 26 to July 24, 2000. It was intended to study the radiative and microphysical properties of Saharan dust transported into Puerto Rico. PRIDE had the unique distinction of being the first major field experiment to allow direct comparison of aerosol retrievals from MODIS (MODerate Imaging Spectro-radiometer - aboard the Terra satellite) with data from a variety of ground, shipboard and air-based instruments. Over the ocean the MODIS algorithm retrieves optical depth as well as information about the aerosol's size. During PRIDE, MODIS passed over Roosevelt Roads approximately once per day during daylight hours. Due to sunglint and clouds over Puerto Rico, aerosol retrievals can be made from only about half the MODIS scenes. In this study we try to "validate" our aerosol retrievals by comparing to measurements taken by sun-photometers from multiple platforms, including: Cimel (AERONET) from the ground, Microtops (handheld) from ground and ship, and the NASA-Ames sunphotometer from the air.

Levy, R.↗
NASA NTRS2013

Enhanced Deep Blue Aerosol Retrieval Algorithm: The Second Generation

The aerosol products retrieved using the MODIS collection 5.1 Deep Blue algorithm have provided useful information about aerosol properties over bright-reflecting land surfaces, such as desert, semi-arid, and urban regions. However, many components of the C5.1 retrieval algorithm needed to be improved; for example, the use of a static surface database to estimate surface reflectances. This is particularly important over regions of mixed vegetated and non- vegetated surfaces, which may undergo strong seasonal changes in land cover. In order to address this issue, we develop a hybrid approach, which takes advantage of the combination of pre-calculated surface reflectance database and normalized difference vegetation index in determining the surface reflectance for aerosol retrievals. As a result, the spatial coverage of aerosol data generated by the enhanced Deep Blue algorithm has been extended from the arid and semi-arid regions to the entire land areas.

Deep Blue↗
NASA NTRS2016

Extending "Deep Blue" Aerosol Retrieval Coverage to Cases of Absorbing Aerosols Above Clouds: Sensitivity Analysis and First Case Studies

Cases of absorbing aerosols above clouds (AACs), such as smoke or mineral dust, are omitted from most routinely processed space-based aerosol optical depth (AOD) data products, including those from the Moderate Resolution Imaging Spectroradiometer (MODIS). This study presents a sensitivity analysis and preliminary algorithm to retrieve above-cloud AOD and liquid cloud optical depth (COD) for AAC cases from MODIS or similar sensors, for incorporation into a future version of the "Deep Blue" AOD data product. Detailed retrieval simulations suggest that these sensors should be able to determine AAC AOD with a typical level of uncertainty approximately 25-50 percent (with lower uncertainties for more strongly absorbing aerosol types) and COD with an uncertainty approximately10-20 percent, if an appropriate aerosol optical model is known beforehand. Errors are larger, particularly if the aerosols are only weakly absorbing, if the aerosol optical properties are not known, and the appropriate model to use must also be retrieved. Actual retrieval errors are also compared to uncertainty envelopes obtained through the optimal estimation (OE) technique; OE-based uncertainties are found to be generally reasonable for COD but larger than actual retrieval errors for AOD, due in part to difficulties in quantifying the degree of spectral correlation of forward model error. The algorithm is also applied to two MODIS scenes (one smoke and one dust) for which near-coincident NASA Ames Airborne Tracking Sun photometer (AATS) data were available to use as a ground truth AOD data source, and found to be in good agreement, demonstrating the validity of the technique with real observations.

aerosols above clouds↗
NASA NTRS2019

Can OMPS and VIIRS Be Used as a Single Virtual Sensor for Aerosol Retrieval?

Satellite earth observation of aerosols using a single sensor have been done for more than three decades. The retrieved global distribution of column integrated aerosol optical depth (AOD) has been refined with higher accuracy and finer resolutions. However, the other aerosol properties, such as aerosol absorption and aerosol height, haven not been mapped as extensively using passive sensors. The detectors sensitive to these properties (UV-based sensors such as Aura-OMI) lack the adequate visible channels to tease out the absorption, height and loading contributions. In addition, their spatial resolution is prone to frequent cloud contamination impacting aerosol loading retrievals using only these spectral ranges. However, the deployment of the VIIRS and OMPS-NM sensors on the same Suomi NPP platform provides a first opportunity to carry out retrievals of aerosol single scattering albedo (SSA) and AOD with little cloud contamination using an expanded spectral range from the UV to the near-IR wavelengths. After collocated VIIRS pixel within the large OMPS footprint during the high resolution OMPS observing mode, we compared the radiance from OMPS and VIIRS to ensure the calibration from individual sensors are comparable. Then a look up table (LUT) was created for a smoke case over central Africa. The LUT is generated using 10 channels from 0.357 to 2.1 micron with 44 combinations of aerosol models and 4 different aerosol heights. With CALIOP and AERONET observation nearby, the possibility of using OMPS and VIIRS radiance as a single virtual sensor to simultaneously and consistently retrieve AOD, SSA, and aerosol height is explored.

Shi, Yingxi↗
NASA NTRS2018

Correcting for Trace Gas Absorption When Retrieving Aerosol Optical Depth from Satellite Observations of Reflected Shortwave Radiation

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.

Satellite Remote Sensing↗
NASA NTRS2024

Sensitivity studies of nighttime top-of-atmosphere radiances from artificial light sources using a 3-D radiative transfer model for nighttime aerosol retrievals

By accounting for surface-based light source emissions and top-of-atmosphere (TOA) downward lunar fluxes, we adapted the spherical harmonics discrete ordinate method (SHDOM) 3-dimensional (3-D) radiative transfer model (RTM) to simulate nighttime 3-D TOA radiances as observed from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) on board the Suomi-NPP satellite platform. Used previously for daytime 3-D applications, these new SHDOM enhancements allow for the study of the impacts of various observing conditions and aerosol properties on simulated VIIRS-DNB TOA radiances. Observations over Dakar, Senegal, selected for its bright city lights and a large range of aerosol optical depth (AOD), were investigated for potential applications and opportunities for using observed radiances containing VIIRS-DNB “bright pixels” from artificial light sources to conduct aerosol retrievals. We found that using the standard deviation (SD) of such bright pixels provided a more stable quantity for nighttime AOD retrievals than direct retrievals from TOA radiances. Further, both the mean TOA radiance and SD of TOA radiances over artificial sources are significantly impacted by satellite viewing angles. Light domes, the enhanced radiances adjacent to artificial light sources, are strong functions of aerosol properties and especially aerosol vertical distribution, which may be further utilized for retrieving aerosol layer height in future studies. Through inter-comparison with both day- and nighttime Aerosol Robotic Network (AERONET) data, the feasibility of retrieving nighttime AODs using 3-D RTM SHDOM over artificial light sources was demonstrated. Our study shows strong potential for using artificial light sources for nighttime AOD retrievals, while also highlighting larger uncertainties in quantifying surface light source emissions. This study underscores the need for surface light emission source characterizations as a key boundary condition, which is a complex task that requires enhanced input data and further research. We demonstrate how quality-controlled nighttime light data from NASA’s Black Marble product suite could serve as a primary input into estimations of surface light source emissions for nighttime aerosol retrievals.

Jianglong Zhang↗
NASA NTRS2011

Comparative Analysis of Aerosol Retrievals from MODIS, OMI and MISR Over Sahara Region

MODIS is a wide field-of-view sensor providing daily global observations of the Earth. Currently, global MODIS aerosol retrievals over land are performed with the main Dark Target algorithm complimented with the Deep Blue (DB) Algorithm over bright deserts. The Dark Target algorithm relies on surface parameterization which relates reflectance in MODIS visible bands with the 2.1 micrometer region, whereas the Deep Blue algorithm uses an ancillary angular distribution model of surface reflectance developed from the time series of clear-sky MODIS observations. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm has been developed for MODIS. MAIAC uses a time series and an image based processing to perform simultaneous retrievals of aerosol properties and surface bidirectional reflectance. It is a generic algorithm which works over both dark vegetative surfaces and bright deserts and performs retrievals at 1 km resolution. In this work, we will provide a comparative analysis of DB, MAIAC, MISR and OMI aerosol products over bright deserts of northern Africa.

Lyapustin, A.↗
NASA NTRS2016

Aerosol Optical Properties Derived from the DRAGON-NE Asia Campaign, and Implications for a Single-Channel Algorithm to Retrieve Aerosol Optical Depth in Spring from Meteorological Imager (MI) On-Board the Communication, Ocean, and Meteorological Satellite (COMS)

An aerosol model optimized for northeast Asia is updated with the inversion data from the Distributed Regional Aerosol Gridded Observation Networks (DRAGON)-northeast (NE) Asia campaign which was conducted during spring from March to May 2012. This updated aerosol model was then applied to a single visible channel algorithm to retrieve aerosol optical depth (AOD) from a Meteorological Imager (MI) on-board the geostationary meteorological satellite, Communication, Ocean, and Meteorological Satellite (COMS). This model plays an important role in retrieving accurate AOD from a single visible channel measurement. For the single-channel retrieval, sensitivity tests showed that perturbations by 4 % (0.926 +/- 0.04) in the assumed single scattering albedo (SSA) can result in the retrieval error in AOD by over 20 %. Since the measured reflectance at the top of the atmosphere depends on both AOD and SSA, the overestimation of assumed SSA in the aerosol model leads to an underestimation of AOD. Based on the AErosol RObotic NETwork (AERONET) inversion data sets obtained over East Asia before 2011, seasonally analyzed aerosol optical properties (AOPs) were categorized by SSAs at 675 nm of 0.92 +/- 0.035 for spring (March, April, and May). After the DRAGON-NE Asia campaign in 2012, the SSA during spring showed a slight increase to 0.93 +/- 0.035. In terms of the volume size distribution, the mode radius of coarse particles was increased from 2.08 +/- 0.40 to 2.14 +/- 0.40. While the original aerosol model consists of volume size distribution and refractive indices obtained before 2011, the new model is constructed by using a total data set after the DRAGON-NE Asia campaign. The large volume of data in high spatial resolution from this intensive campaign can be used to improve the representative aerosol model for East Asia. Accordingly, the new AOD data sets retrieved from a single-channel algorithm, which uses a precalculated look-up table (LUT) with the new aerosol model, show an improved correlation with the measured AOD during the DRAGON-NE Asia campaign. The correlation between the new AOD and AERONET value shows a regression slope of 1.00, while the comparison of the original AOD data retrieved using the original aerosol model shows a slope of 1.08. The change of y-offset is not significant, and the correlation coefficients for the comparisons of the original and new AOD are 0.87 and 0.85, respectively. The tendency of the original aerosol model to overestimate the retrieved AOD is significantly improved by using the SSA values in addition to size distribution and refractive index obtained using the new model.

Kim, M.↗
NASA NTRS2012

Improved Cloud and Snow Screening in MAIAC Aerosol Retrievals Using Spectral and Spatial Analysis

An improved cloud/snow screening technique in the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm is described. It is implemented as part of MAIAC aerosol retrievals based on analysis of spectral residuals and spatial variability. Comparisons with AERONET aerosol observations and a large-scale MODIS data analysis show strong suppression of aerosol optical thickness outliers due to unresolved clouds and snow. At the same time, the developed filter does not reduce the aerosol retrieval capability at high 1 km resolution in strongly inhomogeneous environments, such as near centers of the active fires. Despite significant improvement, the optical depth outliers in high spatial resolution data are and will remain the problem to be addressed by the application-dependent specialized filtering techniques.

Lyapustin, A.↗
NASA NTRS1999

Current Status of Aerosol Retrievals from TOMS

Properties of atmospheric aerosols over all land and water surfaces are retrieved from TOMS measurements of backscattered radiances. The TOMS technique, uses observations at two wavelengths. In the near ultraviolet (330-380 nm) range, where the effects of gaseous absorption are negligible. The retrieved properties are optical depth and a measure of aerosol absorptivity, generally expressed as single scattering albedo. The main sources of error of the TOMS aerosol products are sub-pixel cloud contamination and uncertainty on the height above the surface of UV-absorbing aerosol layers. The first error source is related to the large footprint (50 x 50 km at nadir) of the sensor, and the lack of detection capability of sub-pixel size clouds. The uncertainty associated with the height of the absorbing aerosol layers, on the other hand, is related to the pressure dependence of the molecular scattering process, which is the basis of the near-UV method of absorbing aerosol detection. The detection of non-absorbing aerosols is not sensitive to aerosol layer height. We will report on the ongoing work to overcome both of these difficulties. Coincident measurements of high spatial resolution thermal infrared radiances are used to address the cloud contamination issue. Mostly clear scenes for aerosol retrieval are selected by examining the spatial homogeneity of the IR radiance measurements within a TOMS pixel. The approach to reduce the uncertainty associated with the height of the aerosol layer by making use of a chemical transport model will also be discussed.

Torres, O.↗
NASA NTRS1999

Aerosol Retrievals Using Channel 1 and 2 AVHRR Data

The effect of tropospheric aerosols on global climate via the direct and indirect radiative forcings is one of the largest remaining uncertainties in climate change studies. Current assessments of the direct aerosol radiative effect mainly focus on sulfate aerosols. It has become clear, however, that other aerosol types like soil dust and smoke from biomass burning are also likely to be important climate forcing factors. The magnitude and even the sign of the climate forcing caused by these aerosol types is still unknown. General circulation models (GCMs) can be used to estimate the climatic effect of the direct radiative forcing by tropospheric and stratospheric aerosols. Aerosol optical properties are already parameterized in the Goddard Institute for Space Studies GCM. Once the global distribution of aerosol properties (optical thickness, size distribution, and chemical composition) is available, the calculation of the direct aerosol forcing is rather straighfforward. However, estimates of the indirect aerosol effect require additional knowledge of the physics and chemistry of aerosol-cloud interactions which are still poorly understood. One of the main objectives of the Global Aerosol Climatology Project, established in 1998 as a joint initiative of NASA's Radiation Science Program and GEWEX, is to infer the global distribution of aerosols, their properties, and their seasonal and interannual variations for the full period of available satellite data. This will be accomplished primarily through a systematic application of multichannel aerosol retrieval algorithms to existing satellite data and advanced 3-dimensional aerosol chemistry/transport models. In this paper we outline the methodology of analyzing channel 1 and 2 AVHRR radiance data over the oceans and describe preliminary retrieval results.

Mishchenko, Michael I.↗
NASA NTRS2003

Environmental Snapshots for Satellite Multi-Angle Aerosol Retrieval Validation During the ACE-Asia Field Campaign

On five occasions spanning the ACE-Asia field experiment in spring 2001, the multi-angle imaging MISR instrument, flying aboard the NASA Earth Observing System s Terra satellite, took quarter-kilometer data over a 400-km-wide swath, coincident with high-quality observations by multiple instruments on two or more participating surface and airborne platforms. The cases capture a range of clean, polluted, and dusty aerosol conditions. They represent some of the best opportunities during ACE- Asia for comparative studies among intensive and extensive aerosol observations in their environmental context. We inter-compare related measurements and discuss the implications of apparent discrepancies for each case, at a level of detail appropriate to the analysis of satellite observations. With a three-stage optical modeling process, we synthesize data from multiple sources into layer-by-layer snapshots that summarize what we know about the state of the atmosphere and surface at key locations during each event, to be used for satellite vicarious calibration and aerosol retrieval validation. Aerosols within a few kilometers of the surface were composed primarily of pollution and Asian dust mixtures, as expected. Accumulation and coarse-mode particle size distributions varied little among the events studied, but column aerosol optical depth changed by more than a factor of four, and the near-surface proportion of dust ranged from about 25% to 50%. The amount of absorbing material in the sub-micron fraction was highest when near-surface winds crossed Beijing and the Korean Peninsula, and was considerably lower for all other cases. Ambiguities remain in segregating size distributions by composition; having simultaneous single scattering albedo measurements at more than a single wavelength would significantly reduce the resulting optical model uncertainties, as would integral constraints from surface and atmospheric radiative flux observations. The consistency of component particle micro-physical properties among the five events, even in this relatively complex aerosol environment, suggests that global, satellite-derived maps of aerosol-air-mass-type extent, combined with targeted in situ measurements, can provide a detailed global picture of aerosol behavior. Further joint satellite and in situ analysis is needed to assess the spatial variability of both intensive and extensive aerosol properties within aerosol air masses in two spatial dimensions.

Kahn, Ralph↗
NASA NTRS2011

Using Satellite Aerosol Retrievals to Monitor Surface Particulate Air Quality

The MODIS and MISR aerosol products were designed nearly two decades ago for the purpose of climate applications. Since launch of Terra in 1999, these two sensors have provided global, quantitative information about column-integrated aerosol properties, including aerosol optical depth (AOD) and relative aerosol type parameters (such as Angstrom exponent). Although primarily designed for climate, the air quality (AQ) community quickly recognized that passive satellite products could be used for particulate air quality monitoring and forecasting. However, AOD and particulate matter (PM) concentrations have different units, and represent aerosol conditions in different layers of the atmosphere. Also, due to low visible contrast over brighter surface conditions, satellite-derived aerosol retrievals tend to have larger uncertainty in urban or populated regions. Nonetheless, the AQ community has made significant progress in relating column-integrated AOD at ambient relative humidity (RH) to surface PM concentrations at dried RH. Knowledge of aerosol optical and microphysical properties, ambient meteorological conditions, and especially vertical profile, are critical for physically relating AOD and PM. To make urban-scale maps of PM, we also must account for spatial variability. Since surface PM may vary on a finer spatial scale than the resolution of standard MODIS (10 km) and MISR (17km) products, we test higher-resolution versions of MODIS (3km) and MISR (1km research mode) retrievals. The recent (July 2011) DISCOVER-AQ campaign in the mid-Atlantic offers a comprehensive network of sun photometers (DRAGON) and other data that we use for validating the higher resolution satellite data. In the future, we expect that the wealth of aircraft and ground-based measurements, collected during DISCOVER-AQ, will help us quantitatively link remote sensed and ground-based measurements in the urban region.

Levy, Robert C.↗
NASA NTRS2020

Dust Aerosol Retrieval Over the Oceans With the MODIS/VIIRS Dark‐Target Algorithm: 1. Dust Detection

To prepare for implementation of a new aerosol retrieval specifically designed for dust aerosol over ocean in the operational Dark-Target (DT) algorithms for the Moderate-resolution Imaging Spectrometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite sensors, we focus on the challenge of detecting dust. We first survey the literature on existing dust detection algorithms and then develop an innovative algorithm that combines near-UV (deep blue), visible, and thermal infrared (TIR) wavelength spectral tests. The new detection algorithm is applied to Terra and Aqua MODIS granules and compared with other dust detection possibilities from existing MODIS products. Quantitative evaluation of the new dust detection algorithm is conducted using both a collocated AERONET-MODIS data set and collocated Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO)-MODIS data set. From comparison with both AERONET and CALIOP measurements, we estimate the new dust detection algorithm detects about 30% of weakly dusty pixels and more than 80% of heavily dusty pixels, with false detections in the range of 1–2%. The very low false detection rate is particularly noteworthy in comparison with existing literature. Compared with the dust flag currently available as part of the MODIS cloud mask product (MOD35/MYD35), and dust classification based on commonly used thresholds with aerosol optical depth (AOD) and Angstrom exponent (AE), the new dust detection algorithm finds more dusty pixels and fewer false detections.

spectral dust detection↗
NASA NTRS2020

Dust Aerosol Retrieval Over the Oceans With the MODIS/VIIRS Dark Target Algorithm: 2. Nonspherical Dust Model

The Dark‐target (DT) aerosol algorithm retrieves spectral Aerosol Optical Depth (AOD) and other aerosol properties from Moderate‐resolution Imaging Spectrometer (MODIS) reflectance observations. Over the ocean, the DT algorithm is known to contain scattering‐angle‐dependent biases in its retrievals of AOD, Angstrom Exponent (AE), and Fine Mode Fraction (FMF) for dust aerosols. Following a two‐step strategy to improve the DT retrieval of dust over ocean, for which the first step is to identify dusty pixels (reported in “Part 1”), in this “Part 2,” we report on construction of a new dust model lookup table (LUT) and the strategy for applying it within the existing DT algorithm. In particular, we evaluate different characterizations of dust optical properties from a variety of frameworks and databases, and compare them with the current DT retrieval assumptions. Substituting the standard operational LUT with a spheroid dust model with identified dusty pixels shows significant improvement when compared with collocated AERONET‐identified dusty pixels. Specifically, the application of the new dust model to dusty pixels reduces their AOD bias from 0.06 to 0.02 while improving the fraction of retrievals within expected error from 64% to 82%. At the same time, the overall bias in AE is reduced from 0.13 to 0.06, and the scattering‐angle‐dependent AE bias is largely eliminated. In testing on two full months of data (April and July), the new retrieval will reduce the monthly mean AOD by up to 0.1 and 0.2 in the north Atlantic and Arabian seas, respectively. The average AE and FMF are also reduced in these dust heavy regions

dust aerosol retrieval↗
NASA NTRSDate not supplied

Investigation of the Potential for Lidar & Polarimeter Combined Aerosol Retrievals Using Various Instrument Combinations

As a scientific community, we are motivated to achieve better observational capabilities for aerosols, to reduce uncertainties about aerosol’s role in climate. Encouraged by the National Academies Decadal Survey, many are looking to the synergy of lidar and polarimeter as a means of achieving improved measurements. Polarimetric observations of total-column radiances have excellent sensitivity to aerosol size distribution and absorption properties. Active lidar measurements have fewer wavelengths and view angles and therefore more limited sensitivity to aerosol microphysical properties such as complex refractive index. Yet the advantage of vertically resolved profiles is game-changing, especially given that models contain significant uncertainty about the vertical distribution of aerosol and that polarimeter and other passive retrievals must make assumptions about vertical distribution which can impact performance. To optimize the synergy, a joint polarimeter + lidar mission requires a joint retrieval. However, the exact instruments that will be launched on a future satellite are unknown, and the capabilities of polarimeters and lidars cover a large range. In this study, we perform information content analysis for a range of nominal instrument combinations. The methodology is ideal for assessing the impact of measurements and a priori information (assumptions and constraints) with respect to the desired retrieval products, and for comparing measurement systems. It has advantages that make it useful in advance of or in parallel with retrieval development, such as producing quantitatively useful comparisons even for retrieval scenarios that are significantly underdetermined and therefore impossible to retrieve without significant assumptions, and it is not impacted by retrieval-specific sources of error such as incomplete or incorrect convergence which can complicate the interpretation of a retrieval performance analysis. Here, we will derive the expected uncertainty in retrieved aerosol concentration, effective radius, fine mode fraction and single scattering albedo, for a combination of a polarimeter with various grades of lidar, from an elastic backscatter lidar, similar to the pathfinder CALIOP on the CALIPSO satellite, to a multi-wavelength high spectral resolution lidar.

Sharon P Burton↗
NASA NTRS2018

Optimal Estimation-Based Algorithm to Retrieve Aerosol Optical Properties for GEMS Measurements Over Asia

The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled to be in orbit in 2019 onboard the GEO-KOMPSAT 2B satellite and will continuously monitor air quality over Asia. The GEMS will make measurements in the UV spectrum (300-500 nm) with 0.6 nm resolution. In this study, an algorithm is developed to retrieve aerosol optical properties from UV-visible measurements for the future satellite instrument and is tested using 3 years of existing OMI L1B data. This algorithm provides aerosol optical depth (AOD), single scattering albedo (SSA) and aerosol layer height (ALH) using an optimized estimation method. The retrieved AOD shows good correlation with Aerosol Robotic Network (AERONET) AOD with correlation coefficients of 0.83, 0.73 and 0.80 for heavy-absorbing fine (HAF) particles, dust and non-absorbing (NA) particles, respectively. However, regression tests indicate underestimation and overestimation of HAF and NA AOD, respectively. In comparison with AOD from the OMI/Aura Near-UV Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13 km x 24 km V003 (OMAERUV) algorithm, the retrieved AOD has a correlation coefficient of 0.86 and linear regression equation, AOD(sub GEMS) = 1.18AOD(sub OMAERUV) + 0.09. An uncertainty test based on a reference method, which estimates retrieval error by applying the algorithm to simulated radiance data, revealed that assumptions in the spectral dependency of aerosol absorptivity in the UV cause significant errors in aerosol property retrieval, particularly the SSA retrieval. Consequently, retrieved SSAs did not show good correlation with AERONET values. The ALH results were qualitatively compared with the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) products and were found to be well correlated for highly absorbing aerosols. The difference between the attenuated-backscatter-weighted height from CALIOP and retrieved ALH were mostly closed to zero when the retrieved AOD is higher than 0.8 and SSA is lower than 0.93. Although retrieval accuracy was not significantly improved, the simultaneous consistent retrieval of AOD, SSA and ALH alone demonstrates the value of this stand-alone algorithm, given their nature for error using other methods. The use of these properties as input parameters for the air mass factor calculation is expected to improve the retrieval of other trace gases over Asia.

Geostationary Environment Monitoring Spectrometer ↗