Recent Progress in the NASA 'e-deep Blue' Algorithm for Remote Sensing of Aerosol Optical Properties
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Sayer, Andrew M.↗
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Swelling of Transported Smoke from Savanna fires over the Southeast Atlantic Ocean
We use the recently released Version 4 (V4) lidar data products from CALIPSO to study the smoke plumes transported from Southern African biomass burning areas. The significant improvements in CALIPSO V4 Level 1 calibration and the V4 Level 2 aerosol subtyping algorithms, the latter being particularly relevant to biomass burning smoke over this area, lead to a better representation of their optical properties. For the first time, we show evidence of smoke particles increasing in size, evidenced in their particulate color ratios, as they are transported over the South Atlantic Ocean from the source regions over Southern Africa. This is likely due to hygroscopic swelling of the smoke particles and is reflected in the higher relative humidity in the middle troposphere for profiles with smoke. This finding may have implications for radiative forcing estimates over this area and is relevant to the ORACLES field mission that is currently underway.
Kar, J.↗
Differences in the Vertical and Microphysical Evolution of Volcanic and Pyrocumulonimbus Stratospheric Aerosol Plumes as Observed by CALIOP and CATS Satellite Lidar
For some time, volcanic eruptions have been thought to be the only significant direct injectors of aerosols in the stratosphere. However, recent fire seasons have featured fire events resulting in large volcanic-sized pyrocumulonimbus plumes of smoke aerosols reaching many kilometers into the lower stratosphere. To understand and model the effects of these pyrocumulonimbus events on stratospheric composition and climate, a natural analogy lies with better studied volcanic events; however, differences in plume composition may limit this comparison. Using satellite lidar from both CATS and CALIOP, we show that the stratospheric aerosol plumes from the record-setting Pacific Northwest pyrocumulonimbus event of 2017 and the Calbuco volcanic eruption of 2015 evolve differently both vertically and microphysically. Specifically, depolarization ratios indicate that this pyrocumulonimbus event’s aerosol particles became more irregularly shaped over time in contrast to volcanic aerosols which become more spherical over time. Accounting for these changes in aerosol properties may be significant in assessing the effects of pyrocumulonimbus events on the Earth’s radiative balance and aid in refining stratospheric aerosol typing algorithms to differentiate volcanic from pyrocumulonimbus plumes.
Christian, Kenneth↗
CALIOP Stratospheric Aerosol Typing Performance of Smoke from the 2019-2020 Australian Bushfire Event
A series of pyroCb events occurring in southeast Australia in late December 2019 and early January 2020 injected a large amount of smoke into the stratosphere. CALIOP observations provided critical measurements of the plume in the stratosphere which gave clues about its microphysical properties. An upcoming revised version of the CALIOP level 2 data products (V4.5) will feature improved aerosol typing in the stratosphere. Above the tropopause, the level 2 products discriminate between smoke, volcanic ash, and volcanic sulfate. Accuracy of aerosol typing is critical for automated surveys characterizing distributions of anthropogenic versus natural aerosol and is essential for accurate aerosol extinction retrievals from elastic backscatter lidars such as CALIOP. This poster assesses the performance of the prototype V4.5 CALIOP stratospheric aerosol typing algorithm for the Australian bushfire event.
Jason Tackett↗
Breaking Barriers: Integrating Geo-Leo Aerosol Data with an Open-Source Approach
The scientific community is still examining the novel data from geostationary satellite observations and evaluating methods for effectively fusing the polar observations with various spatial and temporal resolutions. However, the merged data will present a significant ""Big Data"" challenge, including processing, storage, data discoverability, accessibility, and migration within cloud computing environments. We have developed an open-source package to fuse aerosol optical depths (AOD) products from six satellite sensors in the past four years (2019~2023), and this presentation will update our recent progress. Using this Python-based package, we produced a level 3 global (AOD) product in a quarter-degree spatial resolution every half-hour, fusing the Level 2 AOD data with the Dark Target aerosol retrieval algorithm from six satellites: three geostationary (GOES-16/17 and Himawari-8) with high temporal resolution, and three polar orbiting (TERRA/MODIS, AQUA/MODIS, and SNPP-VIIRS) with global coverage. By integrating these observations, the diurnal cycle of global AOD in this fused product can be characterized at local, regional, and global scales. Furthermore, we are committed to openness and transparency by providing our package and its associated functionalities as open-source. Our dedication to adhering to the FAIR, CARE, and TRUST principles ensures that our users can rely on the integrity and ethical standards of our work. For instance of Interoperability, this package fuses remote sensing products on demand into desired temporal and spatial domains. It can be run in a central processing unit (CPU) or a Graphics processing unit (GPU) mode. This package will empower researchers and practitioners to use satellite and sensor data efficiently in various applications and research.
Xiaohua Pan↗
Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements
The Aerosol Robotic Network (AERONET) has provided highly accurate, ground-truth measurements of the aerosol optical depth (AOD) using Cimel Electronique Sun–sky radiometers for more than 25 years. In Version 2 (V2) of the AERONET database, the near-real-time AOD was semiautomatically quality controlled utilizing mainly cloud-screening methodology, while additional AOD data contaminated by clouds or affected by instrument anomalies were removed manually before attaining quality-assured status (Level 2.0). The large growth in the number of AERONET sites over the past 25 years resulted in significant burden to the manual quality control of millions of measurements in a consistent manner. The AERONET Version 3 (V3) algorithm provides fully automatic cloud screening and instrument anomaly quality controls. All of these new algorithm updates apply to near-real-time data as well as post-field-deployment processed data, and AERONET reprocessed the database in 2018. A full algorithm redevelopment provided the opportunity to improve data inputs and corrections such as unique filter-specific temperature characterizations for all visible and near-infrared wavelengths, updated gaseous and water vapor absorption coefficients, and ancillary data sets. The Level 2.0 AOD quality-assured data set is now available within a month after post-field calibration, reducing the lag time from up to several months. Near-real-time estimated uncertainty is determined using data qualified as V3 Level 2.0 AOD and considering the difference between the AOD computed with the pre-field calibration and AOD computed with pre-field and post-field calibration. This assessment provides a near-real-time uncertainty estimate for which average differences of AOD suggest a +0.02 bias and one sigma uncertainty of 0.02, spectrally, but the bias and uncertainty can be significantly larger for specific instrument deployments. Long-term monthly averages analyzed for the entire V3 and V2 databases produced average differences (V3–V2) of +0.002 with a ±0.02 SD (standard deviation), yet monthly averages calculated using time-matched observations in both databases were analyzed to compute an average difference of −0.002 with a ±0.004 SD. The high statistical agreement in multiyear monthly averaged AOD validates the advanced automatic data quality control algorithms and suggests that migrating research to the V3 database will corroborate most V2 research conclusions and likely lead to more accurate results in some cases.
David M. Giles↗
TPSAS-NF1676L-20108-DND
This presentation describes several enhancements planned for the version 4 aerosol subtyping and lidar ratio selection algorithms of the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The CALIOP subtyping algorithm determines the most likely aerosol type from CALIOP measurements (attenuated backscatter, estimated particulate depolarization ratios de, layer altitude), and surface type. The aerosol type, so determined, is associated with a lidar ratio (LR) from a discrete set of values. In the version 3 algorithms, there are 6 pairs of 532 and 1064 nm lidar ratios. Some of these lidar ratios will be updated in the version 4 algorithms. In particular, the dust and polluted dust will be adjusted to reflect the latest measurements and model studies of these types. The algorithms are being updated to eliminate the occasional confusion between smoke and clean marine aerosols seen in version 3 by modifications to the elevated layer flag definitions that are used to determine the presence of smoke aerosols over the ocean. In the subtyping algorithms pure dust is determined by high estimated particulate depolarization ratios [de > 0.20]. Mixtures of dust and other aerosol types are determined by intermediate values of the estimated depolarization ratio [0.075< de <0.2]. The version 3 algorithms are limited to mixtures of dust and smoke, the so-called polluted dust aerosol type. To differentiate between mixtures of dust and smoke, and dust and marine aerosols, a new aerosol type will be added in the version 4 data products. In the revised classification algorithms, polluted dust will still defined as dust + smoke/pollution but in the marine boundary layer instances of moderate depolarization will be typed as dusty marine aerosols with a lower lidar ratio [LR = 35 sr] than polluted dust [currently LR = 55 sr]. The dusty marine type introduced in version 4 is modeled as a mixture of dust + marine aerosol. In the v3 algorithms the frequency of dust and polluted dust aerosols at daytime is higher than at nighttime. We present possible reasons for this and present the v4 distributions resulting from both improved background slope corrections of the daytime depolarization ratios and changes to the daytime thresholds for the polluted dust and dusty marine types of version 4. To gauge the impact of the enhancements, we contrast the following between versions 3 and 4: aerosol type, parameter distributions of each type, layer heights of maximum frequency, and distributions of smoke in biomass burning regions. To illustrate specific impacts the presentation shows case studies of version 3 and version 4 vertical feature masks of the aerosol subtypes, where appropriate, for the above enhancements.
A Omar↗
Simultaneous Aerosol and Ocean Polarimeter Products Using Coupled Atmosphere-Ocean Vector Radiative Transfer and Neural Networks: The PACE-MAPP Algorithm
We describe the PACE-MAPP algorithm that simultaneously retrieves aerosol and ocean optical parameters using multiangle and multi-channel polarimeter measurements from the SPEXone, Hyper-Angular Rainbow Polarimeter 2 (HARP2), and Ocean Color Instrument (OCI) instruments onboard the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) observing system PACE-MAPP is adapted from the Research Scanning Polarimeter (RSP) Microphysical Aerosol Properties from Polarimetry (RSP-MAPP) algorithm. A key feature of the MAPP family of algorithms is the use of a coupled vector radiative transfer model such that the atmosphere and ocean are always considered together as one system. Consequently, conservation of energy ensures that negative water-leaving radiances do not occur. PACE-MAPP uses optimal estimation to simultaneously characterize the optical and microphysical properties of aerosol and ocean constituents, find the optimal solution, and reliably account for the uncertainties of each parameter. This coupled approach, together with multiangle, multi-channel polarimeter measurements, will enable retrievals of aerosol and water properties across the Earth’s oceans. The PACE-MAPP algorithm provides aerosol and ocean products for both the open ocean and coastal areas and is designed to be accurate, modular, and efficient by using fast neural networks that replace the time-consuming vector radiative transfer calculations. We provide an overview of the PACE-MAPP framework and also describe its modular components including its aerosol and hydrosol models, ocean bio-optical models, and thin cirrus model.
Snorre Stamnes↗
Aerosol Abundances and Optical Characteristics in the Pacific Basin Free Troposphere
During NASA's Global Backscatter Experiment (GLOBE) mission flights in November 1989 and May 1990, a DC-8 research aircraft probed the Pacific Basin free troposphere for about 90 flight hours in each month between +72 and -62 degrees latitude, +130 and -120 degrees longitude, and up to 39,000 feet pressure altitudes. Aerosols were sampled continuously in situ by optical particle counters to measure concentration and particle size, and during 48 10-min intervals during each mission by wire impactors for concentration, size, composition, phase and shape analyses. The optical particle counters cover a particle diameter range between 0.3 and 20 microns; wire impactors extend the range down to 0.03 microns. Results of particle number, size, shape, together with the assumption of a refractive index corresponding to (NH4)2SO4 to account for the prevalence of aerosol sulfur, were utilized in a Mie algorithm to calculate aerosol extinction and backscatter for a range of wavelengths (0.385 less than lambda less than 10.64 microns). Computations for 22 randomly selected size distributions yield coefficients of extinction E(0.525) = (2.03 +/- 1.20) x 10(exp -4) km(exp -1) and backscatter beta(0.525) = (6.45 +/- 3.49) x 10(exp -6) km(exp -1) sr(exp -1) in the visible, and E(10.64) = (8.13 +/- 6.47) x 10(exp -6) km(exp -1) and beta(10.64) = (9.98 +/- 10.69) x 10(exp -8) km(exp -1) sr(exp -1) in the infrared, respectively. Large particles (D greater than 0.3 microns) contribute two-thirds to the total extinction in the visible (lambda = 0.525 microns), and almost 100% in the infrared (lambda = 10.64 microns). These results have been used to define an IR optical aerosol climatology of the Pacific Basin free troposphere, from which it follows that the infrared backscatter coefficient at lambda = 9.25 microns wavelength fluctuates between 5.0 x 10(exp -10) and 2.0 x 10(exp -7) km(exp -1) sr(exp -1) with a modal value 2.0 x 10(exp -8) km(exp -1) sr(exp -1).
Pueschel, R. F.↗
Validation of MODIS Aerosol Retrieval Over Ocean
The MODerate resolution Imaging Spectroradiometer (MODIS) algorithm for determining aerosol characteristics over ocean is performing with remarkable accuracy. A two-month data set of MODIS retrievals co-located with observations from the AErosol RObotic NETwork (AERONET) ground-based sunphotometer network provides the necessary validation. Spectral radiation measured by MODIS (in the range 550 - 2100 nm) is used to retrieve the aerosol optical thickness, effective particle radius and ratio between the submicron and micron size particles. MODIS-retrieved aerosol optical thickness at 660 nm and 870 nm fall within the expected uncertainty, with the ensemble average at 660 nm differing by only 2% from the AERONET observations and having virtually no offset. MODIS retrievals of aerosol effective radius agree with AERONET retrievals to within +/- 0.10 micrometers, while MODIS-derived ratios between large and small mode aerosol show definite correlation with ratios derived from AERONET data.
Remer, Lorraine A.↗
Retrieval of Aerosol Optical Properties From GOCI-II Observations: Continuation of Long-Term Geostationary Aerosol Monitoring Over East Asia
Since the Geostationary Ocean Color Imager (GOCI) was successfully launched in 2010, the GOCI Yonsei aerosol retrieval (YAER) algorithm has been continuously updated to retrieve hourly aerosol optical properties. GOCI-II has 4 more channels including UV, finer spatial resolution (250 m), and daily full disk coverage as compared to GOCI, and was launched in February 2020, onboard the GEO-KOMPSAT-2B (GK-2B) satellite. In this study, we extended the YAER algorithm to GOCI-II data based on its improved performance in many aspects and present the first results of aerosol optical properties retrieved from GOCI-II data. Utilizing the overlapping period between the GOCI-II and GOCI in geostationary Earth orbit, we present GOCI-II aerosol retrievals for high aerosol-loading cases over East Asia and show that these have a consistent spatial distribution with those from GOCI. Furthermore, GOCI-II provides AOD at an even higher spatial resolution, revealing finer changes in aerosol concentrations. Validation results for one year data show that the GOCI-II AOD has a correlation coefficient of 0.83 and a ratio within the expected error (EE) of 59.4 % when compared with the aerosol robotic network (AERONET) data. We compared statistical metrics for the GOCI and GOCI-II AODs to assess the consistency between the two datasets. In addition, it was found that there is a strong correlation between the two datasets from the comparison of gridded GOCI and GOCI-II AOD products. It is expected that data from GOCI-II will continue long-term aerosol records with high accuracy that can be used to address air-quality issues over East Asia.
AOD↗
Multi-Sensor Cloud and Aerosol Retrieval Simulator and Remote Sensing from Model Parameters : Aerosols - Part 2
The Multi-sensor Cloud Retrieval Simulator (MCRS) produces a simulated radiance product from any high-resolution general circulation model with interactive aerosol as if a specific sensor such as the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of the atmospheric column and land ocean surface at a specific location. Previously the MCRS code only included contributions from atmosphere and clouds in its radiance calculations and did not incorporate properties of aerosols. In this paper we added a new aerosol properties module to the MCRS code that allows users to insert a mixture of up to 15 different aerosol species in any of 36 vertical layers. This new MCRS code is now known as MCARS (Multi-sensor Cloud and Aerosol Retrieval Simulator). Inclusion of an aerosol module into MCARS not only allows for extensive, tightly controlled testing of various aspects of satellite operational cloud and aerosol properties retrieval algorithms, but also provides a platform for comparing cloud and aerosol models against satellite measurements. This kind of two-way platform can improve the efficacy of model parameterizations of measured satellite radiances, allowing the assessment of model skill consistently with the retrieval algorithm. The MCARS code provides dynamic controls for appearance of cloud and aerosol layers. Thereby detailed quantitative studies of the impacts of various atmospheric components can be controlled. In this paper we illustrate the operation of MCARS by deriving simulated radiances from various data field output by the Goddard Earth Observing System version 5 (GEOS-5) model. The model aerosol fields are prepared for translation to simulated radiance using the same model sub grid variability parameterizations as are used for cloud and atmospheric properties profiles, namely the ICA technique. After MCARS computes modeled sensor radiances equivalent to their observed counterparts, these radiances are presented as input to operational remote-sensing algorithms. Specifically, the MCARS-computed radiances are input into the processing chain used to produce the MODIS Data Collection 6 aerosol product (MOYD04). TheMOYD04 product is of course normally produced from MOYD021KM MODIS Level-1B radiance product directly acquired by the MODIS instrument. MCARS matches the format and metadata of a MOYD021KM product. The resulting MCARS output can be directly provided to MODAPS (MODIS Adaptive Processing System) as input to various operational atmospheric retrieval algorithms. Thus the operational algorithms can be tested directly without needing to make any software changes to accommodate an alternative input source. We show direct application of this synthetic product in analysis of the performance of the MOD04 operational algorithm. We use biomass-burning case studies over Amazonia employed in a recent Working Group on Numerical Experimentation (WGNE)-sponsored study of aerosol impacts on numerical weather prediction (Freitas et al., 2015). We demonstrate that a known low bias in retrieved MODIS aerosol optical depth appears to be due to a disconnect between actual column relative humidity and the value assumed by the MODIS aerosol product.
aerosol retrieval↗
Nitrogen Dioxide Observations from the Geostationary Trace Gas and Aerosol Sensor Optimization (GeoTaso) Airborne Instrument: Retrieval Algorithm and Measurements During DISCOVER-AQ Texas 2013
The Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument is a test bed for upcoming air quality satellite instruments that will measure backscattered ultraviolet, visible and near-infrared light from geostationary orbit. GeoTASO flew on the NASA Falcon aircraft in its first intensive field measurement campaign during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) Earth Venture Mission over Houston, Texas, in September 2013. Measurements of backscattered solar radiation between 420 and 465 nm collected on 4 days during the campaign are used to determine slant column amounts of NO2 at 250 m x 250 m spatial resolution with a fitting precision of 2.2 x 10(exp 15) molecules/sq cm. These slant columns are converted to tropospheric NO2 vertical columns using a radiative transfer model and trace gas profiles from the Community Multiscale Air Quality (CMAQ) model. Total column NO2 from GeoTASO is well correlated with ground-based Pandora observations (r = 0.90 on the most polluted and cloud-free day of measurements and r = 0.74 overall), with GeoTASO NO2 slightly higher for the most polluted observations. Surface NO2 mixing ratios inferred from GeoTASO using the CMAQ model show good correlation with NO2 measured in situ at the surface during the campaign (r = 0.85). NO2 slant columns from GeoTASO also agree well with preliminary retrievals from the GEO-CAPE Airborne Simulator (GCAS) which flew on the NASA King Air B200 (r = 0.81, slope = 0.91). Enhanced NO2 is resolvable over areas of traffic NOx emissions and near individual petrochemical facilities.
Aerosol Sensor Optimization↗
Is It Possible to Distinguish Between Dust and Salt Aerosol Over Waters with Unknown Chlorophyll Concentrations Using Spectral Remote Sensing?
Atmospheric aerosol has uncertain impacts on the global climate system, as well as on atmospheric and bio-geo-chemical processes of regional and local scales. EOS-MODIS is one example of a satellite sensor designed to improve understanding of the aerosols' type, size and distribution at all temporal and spatial scales. Ocean scientists also plan to use data from EOS-MODIS to assess the temporal and spatial coverage of in-water chlorophyll. MODIS is the first sensor planned to observe the combined ocean-atmosphere system with a wide spectral range (from 410 to 2200 nm). Dust aerosol and salt aerosol have similar spectral signals for wavelengths longer than 550 nm, but because dust selectively absorbs blue light, they have divergent signals in the blue wavelength regions (412 to 490 nm). Chlorophyll also selectively absorbs blue radiation, so that varying chlorophyll concentrations produces a highly varying signal in the blue regions, but less variability in the green, and almost no signal in the red to mid-infrared regions. Thus, theoretically, it may be difficult to differentiate dust and salt in the presence of unknown chlorophyll in the ocean. This study attempts to address the cases in which aerosol and chlorophyll signals can and cannot be separated. For the aerosol spectra, we use the aerosol lookup table from the operational MODIS aerosol-over-ocean algorithm, and for chlorophyll spectra, we use the SeaBAM data set (created for SeaWiFS). We compare the signals using Principal Component Analysis and attempt to retrieve both chlorophyll and aerosol properties using a variant of the operational MODIS aerosol-over-ocean algorithm. Results show that for small optical depths, less than 0.5, it is not possible to differentiate between dust and salt and to determine the chlorophyll concentration at the same time. For larger aerosol optical depths, the chlorophyll signals are comparatively insignificant, and we can hope to distinguish between dust and salt.
Levy, R. C.↗
Initial Verification of GEOS-4 Aerosols Using CALIPSO and MODIS: Scene Classification
A-train sensors such as MODIS and MISR provide column aerosol properties, and in the process a means of estimating aerosol type (e.g. smoke vs. dust). Correct classification of aerosol type is important because retrievals are often dependent upon selection of the right aerosol model. In addition, aerosol scene classification helps place the retrieved products in context for comparisons and analysis with aerosol transport models. The recent addition of CALIPSO to the A-train now provides a means of classifying aerosol distribution with altitude. CALIPSO level 1 products include profiles of attenuated backscatter at 532 and 1064 nm, and depolarization at 532 nm. Backscatter intensity, wavelength ratio, and depolarization provide information on the vertical profile of aerosol concentration, size, and shape. Thus similar estimates of aerosol type using MODIS or MISR are possible with CALIPSO, and the combination of data from all sensors provides a means of 3D aerosol scene classification. The NASA Goddard Earth Observing System general circulation model and data assimilation system (GEOS-4) provides global 3D aerosol mass for sulfate, sea salt, dust, and black and organic carbon. A GEOS-4 aerosol scene classification algorithm has been developed to provide estimates of aerosol mixtures along the flight track for NASA's Geoscience Laser Altimeter System (GLAS) satellite lidar. GLAS launched in 2003 and did not have the benefit of depolarization measurements or other sensors from the A-train. Aerosol typing from GLAS data alone was not possible, and the GEOS-4 aerosol classifier has been used to identify aerosol type and improve the retrieval of GLAS products. Here we compare 3D aerosol scene classification using CALIPSO and MODIS with the GEOS-4 aerosol classifier. Dust, smoke, and pollution examples will be discussed in the context of providing an initial verification of the 3D GEOS-4 aerosol products. Prior model verification has only been attempted with surface mass comparisons and column optical depth from AERONET and MODIS.
Welton, Ellsworth J.↗
Assessment of Aerosol Distributions from GEOS-5 Using the CALIPSO Feature Mask
A-train sensors such as MODIS, MISR, and CALIPSO are used to determine aerosol properties, and in the process a means of estimating aerosol type (e.g. smoke vs. dust). Correct classification of aerosol type is important for climate assessment, air quality applications, and for comparisons and analysis with aerosol transport models. The Aerosols-Clouds-Ecosystems (ACE) satellite mission proposed in the NRC Decadal Survey describes a next generation aerosol and cloud suite similar to the current A-train, including a lidar. The future ACE lidar must be able to determine aerosol type effectively in conjunction with modeling activities to achieve ACE objectives. Here we examine the current capabilities of CALIPSO and the NASA Goddard Earth Observing System general circulation model and data assimilation system (GEOS-5), to place future ACE needs in context. The CALIPSO level 2 feature mask includes vertical profiles of aerosol layers classified by type. GEOS-5 provides global 3D aerosol mass for sulfate, sea salt, dust, and black and organic carbon. A GEOS aerosol scene classification algorithm has been developed to provide estimates of aerosol mixtures and extinction profiles along the CALIPSO orbit track. In previous work, initial comparisons between GEOS-5 derived aerosol mixtures and CALIPSO derived aerosol types were presented for July 2007. In general, the results showed that model and lidar derived aerosol types did not agree well in the boundary layer. Agreement was poor over Europe, where CALIPSO indicated the presence of dust and pollution mixtures yet GEOS-5 was dominated by pollution with little dust. Over the ocean in the tropics, the model appeared to contain less sea salt than detected by CALIPSO, yet at high latitudes the situation was reserved. Agreement between CALIPSO and GEOS-5, aerosol types improved above the boundary layer, primarily in dust and smoke dominated regions. At higher altitudes (> 5 km), the model contained aerosol layers not detected by CALIPSO. Here we present new results for a full year study using the new Version 3 CALIPSO data and most recent GEOS-5 model results.
Welton, Ellsworth↗
Atmospheric Correction for Coastal Waters Based on Multi-Angle Polarimetric Observations
Atmospheric correction is an essential procedure of ocean color remote sensing, which obtains water-leaving signals from satellite or airborne sensors. Due to the small percentage of the water leaving signals in the total measurement, atmospheric correction requires precise evaluation of the radiometric contributions from the aerosol and ocean surface. It is often challenging over coastal waters when absorbing aerosols are present and when water leaving signals in the near infrared spectral region are non-negligible. In this work, we report on a joint retrieval algorithm that determines aerosol and ocean optical properties using the polarimetric measurements based on a coupled atmosphere and ocean radiative transfer model. Two bio-optical models are designed for ocean water properties. One model parameterizes the open ocean optical properties in terms of the concentration of chlorophyll a. The other is a generalized bio-optical model for coastal waters that describes the absorption and scattering by phytoplankton, colored dissolved organic matter and non-algal particles using multiple parameters. We applied the algorithm to the airborne Research Scanning Polarimeter (RSP) measurements acquired over both open and coastal ocean waters. Through the comparisons with in situ ocean color measurements, the flexibility and accuracy of the retrieval algorithm in retrieving aerosol and water leaving radiance properties are demonstrated under various aerosol and ocean water conditions.
Gao, Meng↗
Detecting Layer Height of Smoke Aerosols over Vegetated Land and Water Surfaces via Oxygen Absorption Bands: Hourly Results from EPIC/DSCOVR in Deep Space
We present an algorithm for retrieving aerosol layer height (ALH) and aerosol optical depth (AOD) for smoke over vegetated land and water surfaces from measurements of the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR). The algorithm uses Earth-reflected radiances in six EPIC bands in the visible and near-infrared and incorporates flexible spectral fitting that accounts for specifics of land and water surface reflectivity. The fitting procedure first determines AOD using EPIC atmospheric window bands (443 nm, 551 nm, 680nm, and 780 nm), then uses oxygen (O2) A and B bands (688 nm and 764 nm) to derive ALH, which represents an optical centroid altitude. ALH retrieval over vegetated surface primarily takes advantage of measurements in the O2B band. We applied the algorithm to EPIC observations of several biomass burning events over the United States and Canada in August 2017. We found that the algorithm can be used to obtain AOD and ALH multiple times daily over water and vegetated land surface. Validation is performed against aerosol extinction profiles detected by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and against AOD observed at nine Aerosol Robotic Network (AERONET) sites, showing, on average, an error of 0.58 km and a bias of -0.13 km in retrieved ALH and an error of 0.05 and a bias of 0.03 in retrieved AOD. Additionally, we show that the aerosol height information retrieved by the present algorithm can potentially benefit the retrieval of aerosol properties from EPIC’s ultraviolet (UV) bands.
Cloud–Aerosol Lidar with Orthogonal Polarization (↗