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NASA NTRS2012

The Physics of Imaging with Remote Sensors : Photon State Space & Radiative Transfer

Standard (mono-pixel/steady-source) retrieval methodology is reaching its fundamental limit with access to multi-angle/multi-spectral photo- polarimetry. Next... Two emerging new classes of retrieval algorithm worth nurturing: multi-pixel time-domain Wave-radiometry transition regimes, and more... Cross-fertilization with bio-medical imaging. Physics-based remote sensing: - What is "photon state space?" - What is "radiative transfer?" - Is "the end" in sight? Two wide-open frontiers! center dot Examples (with variations.

clouds↗
NASA NTRSDate not supplied

Validation of Modis Cloud Liquid Water Path to Prepare for Pace Evaluation Efforts

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will launch in January 2024, extending and improving NASA’s global satellite observations in its eponymous domains. PACE’s hyperspectral Ocean Color Instrument (OCI) will offer daily near-global spatial coverage with a 1.2 km horizontal pixel size at the subsatellite point. PACE will also have two multi-angle polarimeters (HARP2 and SPEXone) capable of advanced atmospheric characterization. Validation of cloud retrievals is challenging. Here we evaluate liquid water path (LWP) from MODIS from the standard (MOD06) product. The same cloud optical properties retrieval algorithm will be applied to OCI data. This will allow us to understand the expected performance of this algorithm and to develop the processing and analysis code needed to evaluate PACE data. Please tell us what you think and if we should be doing something differently!

MODIS↗
NASA NTRSDate not supplied

CyanoSCape: Freshwater Phytoplankton and Floating Aquatic Vegetation Biodiversity

In Southern Africa, the impacts of anthropogenic activities on biodiversity and ecosystem services are exacerbated by the climate crisis. Rapid land use change and the lack of emphasis on environmentally sustainable agricultural practices has hindered hydrological processes and compromised riverine and aquatic ecosystems. This poses obvious risks to natural/indigenous aquatic biodiversity and long-term ecosystem sustainability. Phytoplankton serve as the foundation of the freshwater food web with zooplankton as consumers, which feed fish, invertebrates, and so on up the food chain that comprises the biodiversity of the freshwater system that serves as habitat for biodiversity as well. The diversity of phytoplankton (microscopic organisms) includes photosynthesizing bacteria (cyanobacteria), plant-like diatoms, dinoflagellates, and green algae. Nutrient run-off from agricultural fertilizers and urban overflows, warm temperatures, abundant light availability and compromised hydrological systems provide an ideal environment for cyanobacteria to flourish. Increased prevalence of cyanobacteria, due to both natural and anthropogenic causes, can incur significant effects on the biodiversity of the overall phytoplankton assemblage. These bloom forming algae can significantly outcompete other phytoplankton classes in warmer and eutrophic waters where they are quick to dominate the freshwater system. Eutrophication and toxic cyanobacteria blooms (cyanoHABs) in the inland waters of the Greater Cape Floristic Region (GCFR) incur significant effects on the biodiversity of the overall phytoplankton assemblage and provide a favorable environment forthe overgrowth of floating aquatic vegetation (FAV), which is often invasive and associated with reduced aquatic biodiversity. The algal biodiversity of the GCFR’s freshwater systems is not well characterized. Hyperspectral optical observations are expected to facilitate the improvement of current phytoplankton functional type retrievals significantly, as the sensitivity is sufficient that the distinctive, fine spectral features of different phytoplankton groups can be detected. This will enable testing emerging algorithms and inform the development of new algorithms for use with upcoming hyperspectral satellite missions in this decade. Innovations in optical sensor sensitivity and next generation machine learning capabilities considerably enhance the potential for accurate and rapid detection of phytoplankton, namely the presence, extent, and diversity of cyanobacteria present in cyanoHABs and additionally, invasive FAV. Upcoming hyperspectral satellite missions such as NASA’s Surface Biology and Geology (SBG), Plankton, Aerosol, Cloud, ocean Ecosystem (PACE), and the European Space Agency’s Copernicus Hyperspectral Imaging Mission (CHIME) will provide imagery with unprecedented spectral and spatial resolution which will further enable the discovery of linkages between the seasonality and dynamics of HABs and FAV. The overarching goal of this project is to utilize hyperspectral data, with recently developed and next-generation algorithms, to determine the biodiversity of freshwater systems phytoplankton assemblage with emphasis on genus level distinction, as well as monitor the prevalence and diversity of FAV.

CyanoSCape↗
NASA NTRS2019

Remote Sensing of Daytime Water Leaving Reflectances of Oceans and Large Inland Lakes from EPIC onboard the DSCOVR Spacecraft at Lagrange-1 Point

The NASA’s Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) satellite has been making multiple observations of the entire sunlit Earth in a given day from the Sun-Earth Lagrangian L1 point since the summer of 2015. EPIC contains 10 narrow channels in the 317–780 nm solar spectral range. The data acquired with EPIC have already been used in a variety of scientific investigations, including the study of the global ozone levels, aerosol index and aerosol optical depth, UV reflectivity of clouds over land and ocean, cloud height over land and ocean, and vegetation indices. In this article, we report that EPIC data, particularly for the data measured with narrow channels centered near 443, 551, and 680 nm, can also have important applications in remote sensing of ocean color in different geographical regions. We have modified a version of a multi-channel atmospheric correction algorithm for Moderate Resolution Imaging Spectro Radiometer (MODIS) ocean color applications and adapted the algorithm for processing EPIC data. We present three case studies on water leaving reflectance retrievals from EPIC data acquired over a large turbid river, inland lakes, and oceans. We conclude that a future ocean color instrument on board a satellite at the L1 point, which provides continuous view of the full sunlit disk of the Earth, will complement and extend ocean color observations with the low Earth observing polar orbital and geostationary satellite instruments in both the spatial and time domains.

remote sensing↗
NASA NTRS2019

Retrieving Aerosol Characteristics From the PACE Mission, Part 2: Multi-Angle and Polarimetry

The Plankton, Aerosol, Clouds, ocean Ecosystem (PACE) mission presents new opportunities and new challenges in applying observations of two complementary multi-angle polarimeters for the space-based retrieval of global aerosol properties.Aerosol remote sensing from multi-angle radiometric-only observations enables aerosol characterization to a greater degree than single-view radiometers, as demonstrated by nearly two decades of heritage instruments. Adding polarimetry to the multi-angle observations allows for the retrieval of aerosol optical depth, Angstrom exponent,parameters of size distribution, measures of aerosol absorption, complex refractive index and degree of non-sphericity of the particles, as demonstrated by two independent retrieval algorithms applied to the heritage POLarization and Directionality of the Earth's Reflectance (POLDER) instrument. The reason why this detailed particle characterization is possible is because a multi-angle polarimeter measurement contains twice the number of Degrees of Freedom of Signal (DFS) compared to an observation from a single-view radiometer. The challenges of making use of this information content involve separating surface signal from atmospheric signal, especially when the surface is optically complex and especially in the ultraviolet portion of the spectrum where we show the necessity of polarization in making that separation. The path forward is likely to involve joint retrievalsthat will simultaneously retrieve aerosol and surface properties, although advances will berequired in radiative transfer modeling and in representing optically complex constituents in those models. Another challenge is in having the processing capability that can keep pace with the output of these instruments in an operational environment. Yet, preliminaryalgorithms applied to airborne multi-angle polarimeter observations offer encouraging results that demonstrate the advantages of these instruments to retrieve aerosol layer height, particle single scattering albedo, size distribution and spectral optical depth, and also show the necessity of polarization measurements, not just multi-angle radiometricmeasurements, to achieve these results.

Remer, Lorraine A.↗
NASA NTRS2012

Cloud Height Retrieval with Oxygen A and B Bands for the Deep Space Climate Observatory (DSCOVR) Mission

Planned to fly in 2014, the Deep Space Climate Observatory (DSCOVR) would see the whole sunlit half of the Earth from the L 1 Lagrangian point and would provide simultaneous data on cloud and aerosol properties with its Earth Polychromatic Imaging Camera (EPIC). EPIC images the Earth on a 2Kx2K CCD array, which gives a horizontal resolution of about 10 km at nadir. A filter-wheel provides consecutive images in 10 spectral channels ranging from the UV to the near-IR, including the oxygen A and B bands. This paper presents a study of retrieving cloud height with EPIC's oxygen A and B bands. As the first step, we analyzed the effect of cloud optical and geometrical properties, sun-view geometry, and surface type on the cloud height determination. Second, we developed two cloud height retrieval algorithms that are based on the Mixed Lambertian-Equivalent Reflectivity (MLER) concept: one utilizes the absolute radiances at the Oxygen A and B bands and the other uses the radiance ratios between the absorption and reference channels of the two bands. Third, we applied the algorithms to the simulated EPIC data and to the data from SCanning Imaging Absorption SpectroMeter for Atmospheric CartograpHY (SCIAMACHY) observations. Results show that oxygen A and B bands complement each other: A band is better suited for retrievals over ocean, while B band is better over vegetated land due to a much darker surface. Improvements to the MLER model, including corrections to surface contribution and photon path inside clouds, will also be discussed.

Yang, Yuekui↗
NASA NTRS2019

Atmospheric Research 2018 Technical Highlights

Atmospheric research in the Earth Sciences Division (610) consists of research and technology development programs dedicated to advancing knowledge and understanding of the atmosphere and its interaction with the climate of Earth. The Division’s goals are to improve understanding of the dynamics and physical properties of precipitation, clouds, and aerosols; atmospheric chemistry, including the role of natural and anthropogenic trace species on the ozone balance in the stratosphere and the troposphere; and radiative properties of Earth’s atmosphere and the influence of solar variability on the Earth’s climate. Major research activities are carried out in the Mesoscale Atmospheric Processes Laboratory, the Climate and Radiation Laboratory, the Atmospheric Chemistry and Dynamics Laboratory, and the Wallops Field Support Office. The overall scope of the research covers an end-to-end process, starting with the identification of scientific problems, leading to observation requirements for remote sensing platforms, technology and retrieval algorithm development; followed by flight projects and satellite missions; and eventually, resulting in data processing, analyses of measurements, and dissemination from flight projects and missions.

Highlights↗
NASA NTRS2022

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 MODIS and the C4C products have their own strengths and challenges for a given climate application and discuss needed research. even larger than what was diagnosed here. We conclude with a discussion on how

Aerosols↗
NASA NTRS1998

CART and GSFC raman lidar measurements of atmospheric aerosol backscattering and extinction profiles for EOS validation and ARM radiation studies

The aerosol retrieval algorithms used by the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Multi-Angle Imaging SpectroRadiometer (MISR) sensors on the Earth Observing Satellite (EOS) AM-1 platform operate by comparing measured radiances with tabulated radiances that have been computed for specific aerosol models. These aerosol models are based almost entirely on surface and/or column averaged measurements and so may not accurately represent the ambient aerosol properties. Therefore, to validate these EOS algorithms and to determine the effects of aerosols on the clear-sky radiative flux, we have begun to evaluate the vertical variability of ambient aerosol properties using the aerosol backscattering and extinction profiles measured by the Cloud and Radiation Testbed (CART) and NASA Goddard Space Flight Center (GSFC) Raman Lidars. Using the procedures developed for the GSFC Scanning Raman Lidar (SRL), we have developed and have begun to implement algorithms for the CART Raman Lidar to routinely provide profiles of aerosol extinction and backscattering during both nighttime and ,daytime operations. Aerosol backscattering and extinction profiles are computed for both lidar systems using data acquired during the 1996 and 1997 Water Vapor Intensive Operating Periods (IOPs). By integrating these aerosol extinction profiles, we derive measurements of aerosol optical thickness and compare these with coincident sun photometer measurements. We also use these measurements to measure the aerosol extinction/backscatter ratio S(sub a) (i.e. 'lidar ratio'). Furthermore, we use the simultaneous water vapor measurements acquired by these Raman lidars to investigate the effects of water vapor on aerosol optical properties.

Ferrare, R. A.↗
NASA NTRS2015

Technology Advancement for Active Remote Sensing of Carbon Dioxide from Space Using the ASCENDS CarbonHawk Experiment Simulator: First Results

The ASCENDS CarbonHawk Experiment Simulator (ACES) is a newly developed lidar developed at NASA Langley Research Center and funded by NASA's Earth Science Technology Office (ESTO) Instrument Incubator Program (IIP) that seeks to advance technologies critical to measuring atmospheric column carbon dioxide (CO2) mixing ratios in support of the NASA Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) mission. The technology advancements targeted include: (1) increasing the power-aperture product to approach ASCENDS mission requirements by implementing multi-aperture telescopes and multiple co-aligned laser transmitters; (2) incorporating high-efficiency, high-power Erbium-Doped Fiber Amplifiers (EDFAs); (3) developing and incorporating a high-bandwidth, low-noise HgCdTe detector and transimpedence amplifier (TIA) subsystem capable of long-duration autonomous operation on Global Hawk aircraft, and (4) advancing algorithms for cloud and aerosol discrimination. The ACES instrument architecture is being developed for operation on high-altitude aircraft and will be directly scalable to meet the ASCENDS mission requirements. These technologies are critical towards developing not only spaceborne instruments but also their airborne simulators, with lower platform requirements for size, mass, and power, and with improved instrument performance for the ASCENDS mission. ACES transmits five laser beams: three from commercial EDFAs operating near 1.57 microns, and two from the Exelis oxygen (O2) Raman fiber laser amplifier system operating near 1.26 microns. The three EDFAs are capable of transmitting up to 10 watts average optical output power each and are seeded by compact, low noise, stable, narrow-linewidth laser sources stabilized with respect to a CO2 absorption line using a multi-pass gas absorption cell. The Integrated-Path Differential Absorption (IPDA) lidar approach is used at both wavelengths to independently measure the CO2 and O2 column number densities and retrieve the average column CO2 mixing ratio. The ACES receiver uses three fiber-coupled 17.8-cm diameter athermal telescopes. The transmitter assembly consists of five fiber-coupled laser collimators and an associated Risley prism pair for each laser to co-align the outgoing laser beams and to align them with the telescope field of view. The backscattered return signals collected by the three telescopes are combined in a fiber bundle and sent to a single low noise detector. The detector/TIA development has improved the existing detector subsystem by increasing its bandwidth to 4.7 MHz from 500 kHz and increasing the duration of autonomous, service-free operation periods from 4 hours to >24 hours. The new detector subsystem enables the utilization of higher laser modulation rates, which provides greater flexibility for implementing advanced thin-cloud discrimination algorithms as well as improving range-determination resolution and error reduction. The cloud/aerosol discrimination algorithm development by Langley and Exelis features a new suite of algorithms for the minimization/elimination of bias errors in the return signal induced by the presence of intervening thin clouds. Multiple laser modulation schemes are being tested in an effort to significantly mitigate the effects of thin clouds on the retrieved CO2 column amounts. Full instrument development concluded in the spring of 2014. After ground range tests of the instrument, ACES successfully completed six test flights on the Langley Hu-25 aircraft in July, 2014, and recorded data at multiple altitudes over land and ocean surfaces with and without intervening clouds. Preliminary results from these test flights will be presented in this paper.

Obland, Michael D.↗
NASA NTRS2011

An Investigation of Interaction of Saharan Dust and Atlantic ITCZ Using Cloudsat-Calipso and A-Train Data

In this study, we investigate the radiative forcing of Saharan dust, its interactions with the Atlantic Intertropical Convergence Zone (ITCZ), through African easterly waves (AEW), African easterly jets (AEJ), and its impacts in short term numerical forecasts of tropical cyclogenesis using the GOCART-GEOS5 forecast system. Our approach is to develop and use an A-Train satellite simulator (ATSS) to constrain the observed aerosol index of refraction and particle size distribution by finding the values that simultaneously minimize the difference between observed CALIOP, CloudSat, OMI, and MODIS radiances and simulated radiances inverted from atmospheric model output using procedures and physical principles consistent with those used in corresponding retrieval algorithms. We use observations from the A-train and TRMM to determine relationships among the Saharan dust layer, transport by the AEW, and possible responses to dust radiative forcing in developing tropical cyclones in the A-ITCZ. Preliminary model results showing physical processes associated with the generation and transport of the Saharan dust layer, their interactions with the incipient moisture, clouds and rainfall in developing tropical cyclones will be presented. Also presented will be results of a case study of possible radiative impacts on AEW and AEJ during the NAMMA field campaign.

Lau, W.↗
NASA NTRSDate not supplied

Assessment of the Accuracy and Consistency of Cloud Property Retrievals from Passive Radiometers with Varying Spectral Information

Over the past few decades, space-based passive radiometers have been used to study the spatial distribution of clouds and some of their basic properties. Modern radiometers have significantly higher spatial resolution and collect more spectral information (i.e., have more spectral bands) than early instruments, and these advances have allowed researchers to characterize cloud physical and microphysical properties with greater detail. There is significant interest in utilizing as much of the existing satellite record as possible to study global and regional changes in cloud occurrence and distribution as well as the associated changes in reflected shortwave and outgoing longwave fluxes. Cloud property retrieval algorithms such as the NASA Satellite Cloud Optical and Radiative Property System (SatCORPS) have traditionally utilized as much spectral information as possible in order to maximize the accuracy of the retrievals. However, changes to the retrieval algorithm over time in order to take advantage of progressively more spectral information results in inconsistencies in cloud properties which in turn results in inconsistencies with products derived from these properties. It is not possible to implement modern state-of-the-art algorithms to earlier instruments which lack diverse spectral information, but many instruments since the early 2000s generally have approximately similar spectral bands in the visible (~0.65 microns), longwave infrared (~11 microns), and in many cases, the midwave infrared (~3.8 microns) and water vapor absorption (~6.7 microns), portions of the electromagnetic spectrum. Different versions of SatCORPS have been implemented with either the full set of spectral information collected by imagers such as GOES-16 and MODIS or with a reduced set of spectral bands in order to be more consistent with past imagers that lack such diverse spectral information. This study uses independent cloud property retrievals from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) to assess the accuracy of the retrievals (e.g., cloud fraction, thermodynamic phase, altitude) from the different retrieval methods and to demonstrate the advantages and disadvantages of using a reduced set of common spectral bands in order to achieve temporal and spatial consistency across different instruments.

Christopher R. Yost↗
NASA NTRS2022

Simultaneous Retrievals of Biomass Burning Aerosols and Trace Gases From the Ultraviolet to Near-Infrared Over Northern Thailand During the 2019 Pre-Monsoon Season

With the advent of spaceborne spectroradiometers in a geostationary constellation, measuring high spectral resolution ultraviolet–visible (UV-VIS) and selected near-/shortwave-infrared (NIR/SWIR) radiances can enable the probing of the life cycle of key atmospheric trace gases and aerosols at higher temporal resolutions over the globe. The UV-VIS measurements are important for retrieving several key trace gases (e.g., O 3 , SO 2 , NO 2 , and HCHO) and particularly for deriving aerosol characteristics (e.g., aerosol absorption and vertical profile). This study examines the merit of simultaneous retrievals of trace gases and aerosols using a ground-based spectroradiometer covering the UV-NIR to monitor their physicochemical processes and to obtain reliable aerosol information for various applications. During the 2019 pre-monsoon season over northern Thailand, we deployed a ground-based SMART–s (Spectral Measurements for Atmospheric Radiative Transfer–spectroradiometer) instrument, which is an extended-range Pandora with reliable radiometric calibration in the 330–820 nm range, to retrieve remotely sensed chemical and aerosol properties for the first time near biomass burning sources. The high spectral resolution (∼ 1.0 nm full width half maximum with ∼ 3.7 × oversampling) of sun and sky measurements from SMART–s provides several key trace gases (e.g., O 3 , NO 2 , and H 2 O) and aerosol properties covering the UV where significant light absorption occurs by the carbonaceous particles. During the measurement period, highly correlated total column amounts of NO 2 and aerosol optical thickness (т aer ) retrieved from SMART–s (correlation coefficient, R=0.74) indicated their common emissions from biomass burning events. The SMART–s retrievals of the spectral single scattering albedo (ω 0 ) of smoke aerosols showed an abrupt decrease in the UV, which is an important parameter dictating photochemical processes in the atmosphere. The values of ω 0 and column precipitable water vapor (H 2 O) gradually increase with the mixing of biomass burning smoke particles and higher water vapor concentrations when approaching the monsoon season. The retrieved ω 0 and weighted mean radius of fine-mode aerosols from SMART–s showed positive correlations with the H 2 O (R=0.81 for ω 0 at 330 nm and 0.56 for the volume-weighted mean radius), whereas the real part of the refractive index of fine-mode aerosol (n f ) showed negative correlations (R = -0.61 at 330 nm), which suggest that aerosol aging processes including hygroscopic growth (e.g., humidification and cloud processing) can be a major factor affecting the temporal trends of aerosol optical properties. Retrieved n f and ω 0 were closer to those of the water droplet (i.e., n f of about 1.33 and ω 0 of about 1.0) under lower amounts of NO 2 during the measurement period; considering that the NO 2 amounts in the smoke may indicate the aging of the plume after emission due to its short lifetime, the tendency is also consistent with active hygroscopic processes of the aerosols over this area. Retrieved UV aerosol properties from SMART–s generally support the assumed smoke aerosol models (i.e., the spectral shape of aerosol absorption) used in NASA's current satellite algorithms, and their spectral ω 0 retrievals from ground and satellites showed good agreements (R = 0.73–0.79). However, temporal and spectral variabilities in the aerosol absorption properties in the UV emphasize the importance of a realistic optical model of aerosols for further improvements in satellite retrievals.

biomass burning↗
NASA NTRS2016

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↗
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 NTRS2013

Classification of Aerosol Retrievals from Spaceborne Polarimetry Using a Multiparameter Algorithm

In this presentation, we demonstrate application of a new aerosol classification algorithm to retrievals from the POLDER-3 polarimter on the PARASOL spacecraft. Motivation and method: Since the development of global aerosol measurements by satellites and AERONET, classification of observed aerosols into several types (e.g., urban-industrial, biomass burning, mineral dust, maritime, and various subtypes or mixtures of these) has proven useful to: understanding aerosol sources, transformations, effects, and feedback mechanisms; improving accuracy of satellite retrievals and quantifying assessments of aerosol radiative impacts on climate.

Atmospheric Processes Clouds & Aerosols↗
NASA NTRS1983

Methodology for interpretation of SST retrievals using the AVHRR split window algorithm

Intercomparisons of sea surface temperature (SST) products derived from the operational NOAA-7 AVHRR-II algorithm and in situ observations are made. The 1982 data sets consist of ship survey data during the winter from the Mid-Atlantic Bight (MAB), ship and buoy measurements during April and September in the Gulf of Mexico and shipboard observations during April off the N.W. Spanish coast. The analyses included single pixel comparisons and the warmest pixel technique for 2 x 2 pixel and 10 x 10 pixel areas. The reason for using multi-pixel areas was for avoiding cloud contaminated pixels in the vicinity of the field measurements. Care must be taken when applying the warmest pixel technique near oceanic fronts. The Gulf of Mexico results clearly indicate a persistent degradation in algorithm accuracy due to El Chichon aerosols. The MAB and Spanish data sets indicate that very accurate estimates can be achieved if care is taken to avoid clouds and oceanic fronts.

Barbieri, R. W.↗
NASA NTRS2007

Extinction-to-Backscatter Ratios of Lofted Aerosol Layers Observed During the First Three Months of CALIPSO Measurements

Case studies from the first three months of the Cloud and Aerosol Lidar and Infrared Pathfinder Spaceborne Observations (CALIPSO) measurements of lofted aerosol layers are analyzed using transmittance [Young, 1995] and two-wavelength algorithms [Vaughan et al., 2004] to determine the aerosol extinction-to-backscatter ratios at 532 and 1064 nm. The transmittance method requires clear air below the layer so that the transmittance through the layer can be determined. Suitable scenes are selected from the browse images and clear air below features is identified by low 532 nm backscatter signal and confirmed by low depolarization and color ratios. The transmittance and two-wavelength techniques are applied to a number of lofted layers and the extinction-to-backscatter ratios are compared with values obtained from the CALIPSO aerosol models [Omar et al., 2004]. The results obtained from these studies are used to adjust the aerosol models and develop observations based extinction-to-backscatter ratio look-up tables and phase functions. Values obtained by these techniques are compared to Sa determinations using other independent methods with a goal of developing probability distribution functions of aerosol type-specific extinction to backscatter ratios. In particular, the results are compared to values determined directly by the High Spectral Resolution Lidar (HSRL) during the CALIPSO CloudSat Validation Experiments (CCVEX) and Sa determined by the application of the two-wavelength lidar Constrained Ratio Aerosol Model-fit (CRAM) retrieval approach [Cattrall et al., 2005; Reagan et al., 2004] to the HSRL data. The results are also compared to values derived using the empirical relationship between the multiple-scattering fraction and the linear depolarization ratio by using Monte Carlo simulations of water clouds [Hu et al., 2006].

Omar, Ali H.↗