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Aerosol properties are fundamentally different near clouds than away from clouds. This paper reviews the current state of knowledge of aerosol properties in the near-low-cloud environment and quantitatively compares them with aerosols far from clouds, according to remote sensing observations. It interprets observations of aerosol properties from different sensors using satellite, aircraft, and ground-based observations. The correlation (and anticorrelation) between proximity to cloud and aerosol properties is discussed. Retrieval artifacts in the near-cloud environment are demonstrated and quantified for different sensor attributes and environmental conditions. Finally, the paper describes the possible corrections for near-cloud enhancement in remote sensing retrievals. This study is timely in view of science definition studies for NASA’s Aerosol, Cloud, Convection and Precipitation (ACCP) mission, which will also seek to directly link aerosol properties to nearby clouds.

Ocean color remote sensing is a challenging task over coastal waters due to the complex optical properties of aerosols and hydrosols. In order to conduct accurate atmospheric correction, we previously implemented a joint retrieval algorithm, hereafter referred to as the Multi-Angular Polarimetric Ocean coLor (MAPOL) algorithm, to obtain the aerosol and water-leaving signal simultaneously. The MAPOL algorithm has been validated with synthetic data generated by a vector radiative transfer model, and good retrieval performance has been demonstrated in terms of both aerosol and ocean water optical properties (Gao et al., 2018). In this work we applied the algorithm to airborne polarimetric measurements from the Research Scanning Polarimeter (RSP) over both open and coastal ocean waters acquired in two field campaigns: the Ship-Aircraft Bio-Optical Research (SABOR) in 2014 and the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) in 2015 and 2016. Two different yet related bio-optical models are designed for ocean water properties. One model aligns with traditional open ocean water bio-optical models that parameterize the ocean optical properties in terms of the concentration of chlorophyll a. The other is a generalized bio-optical model for coastal waters that includes seven free parameters to describe the absorption and scattering by phytoplankton, colored dissolved organic matter, and nonalgal particles. The retrieval errors of both aerosol optical depth and the water-leaving radiance are evaluated. Through the comparisons with ocean color data products from both in situ measurements and the Moderate Resolution Imaging Spectroradiometer (MODIS), and the aerosol product from both the High Spectral Resolution Lidar (HSRL) and the Aerosol Robotic Network (AERONET), the MAPOL algorithm demonstrates both flexibility and accuracy in retrieving aerosol and water-leaving radiance properties under various aerosol and ocean water conditions.

NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in the timeframe of 2023, will carry a hyperspectral Ocean Color Instrument (OCI) and two Multi-Angle Polarimeters (MAP): the UMBC Hyper-Angular Rainbow Polarimeter (HARP2) and the SRON Spectro-Polarimeter for Planetary EXploration one (SPEXone). The MAP measurements contain rich information on the microphysical properties of aerosols and hydrosols, and therefore can be used to retrieve accurate aerosol properties for complex atmosphere and ocean systems. Most polarimetric aerosol retrieval algorithms utilize vector radiative transfer models iteratively in an optimization approach, which leads to high computational costs that limit their usage in the operational processing of large data volumes acquired by the MAP imagers. In this work, we propose a deep neural network (NN) model to represent the radiative transfer simulation of coupled atmosphere and ocean systems, for applications to the HARP instrument. Through the evaluation of synthetic datasets for AirHARP (airborne version of HARP2), the NN model achieves a numerical accuracy smaller than the instrument uncertainties, with a running time of 0.01 s in a single CPU core or 1 ms in GPU. Using the NN as a forward model, we built an efficient joint aerosol and ocean color retrieval algorithm called FastMAPOL, evolved from the well-validated Multi-Angular Polarimetric Ocean coLor (MAPOL) algorithm. Retrievals of aerosol properties and water leaving signals were conducted on both the synthetic data and the AirHARP field measurements from the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign in 2017. From the validation with the synthetic data and the collocated High Spectral Resolution Lidar (HSRL) aerosol products, we demonstrated that the aerosol microphysical properties and water leaving signals can be retrieved efficiently and within acceptable error. The FastMAPOL algorithm can be used to operationally process the large volume of polarimetric data acquired by PACE and other future Earth observing satellite missions with similar capabilities.

Since early 2000, NASA's Multi-angle Imaging SpectroRadiometer (MISR) instrument has been performing remote sensing retrievals of aerosol optical properties from the polar orbiting Terra spacecraft. A noteworthy aspect of MISR observations over the ocean is that, for much of the Earth, some of the multi-angle views have contributions from solar reflection by the ocean surface (glint, or glitter), while others do not. Aerosol retrieval algorithms often discard these glint influenced observations because they can overwhelm the signal and are difficult to predict without knowledge of the (wind speed driven) ocean surface roughness. However, theoretical studies have shown that multi-angle observations of a location at geometries with and without reflected sun glint can be a rich source of information, sufficient to support simultaneous retrieval of both the aerosol state and the wind speed at the ocean surface. We are in the early stages of creating such an algorithm. In this manuscript, we describe our assessment of the appropriate level of parameterization for simultaneous aerosol and ocean surface property retrievals using sun glint. For this purpose, we use Generalized Nonlinear Retrieval Analysis (GENRA), an information content assessment (ICA) technique employing Bayesian inference, and simulations from the Ahmad-Fraser iterative radiative transfer code. We find that four parameters are suitable: aerosol optical depth (τ), particle size distribution (expressed as the fine mode fraction f of small particles in a bimodal size distribution), surface wind speed (w), and relative humidity (r, to define the aerosol water content and complex refractive index). None of these parameters define ocean optical properties, as we found that the aerosol state could be retrieved with the nine MISR near-infrared views alone, where the ocean body is black in the open ocean. We also found that retrieval capability varies with observation geometry, and that as τ increases so does the ability to determine aerosol intensive optical properties (r and f, while it decreases for w). Increases in wind speed decrease the ability to determine the true value of that parameter, but have minimal impact on retrieval of aerosol properties. We explored the benefit of excluding the two most extreme MISR view angles for which radiative transfer with the plane parallel approximation is less certain, but found no advantage in doing so. Finally, the impact of treating wind speed as a scalar parameter, rather than as a two parameter directional wind, was tested. While the simpler scalar model does contribute to overall aerosol uncertainty, it is not sufficiently large to justify the addition of another dimension to parameter space. An algorithm designed upon these principles is in development. It will be used to perform an atmospheric correction with MISR for coincident ocean color (OC) observations by the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument, also on the NASA Terra spacecraft. Unlike MISR, MODIS is a single view angle instrument, but it has a more complete set of spectral channels ideal for determination of ocean optical properties. The atmospheric correction of MODIS OC data can therefore benefit from MISR aerosol retrievals. Furthermore, higher spatial resolution data from coincident MISR observations may also improve glint screening.

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will carry into space the Ocean Color Instrument (OCI), a spectrometer measuring at 5 nm spectral resolution in the ultraviolet (UV) to near infrared (NIR) with additional spectral bands in the shortwave infrared (SWIR), and two multi-angle polarimeters that will overlap the OCI spectral range and spatial coverage, i. e., the Spectrometer for Planetary Exploration (SPEXone) and the Hyper-Angular Rainbow Polarimeter (HARP2). These instruments, especially when used in synergy, have great potential for improving estimates of water reflectance in the post Earth Observing System (EOS) era. Extending the top-of-atmosphere (TOA) observations to the UV, where aerosol absorption is effective, adding spectral bands in the SWIR, where even the most turbid waters are black and sensitivity to the aerosol coarse mode is higher than at shorter wavelengths, and measuring in the oxygen A-band to estimate aerosol altitude will enable greater accuracy in atmospheric correction for ocean color science. The multi-angular and polarized measurements, sensitive to aerosol properties (e.g., size distribution, index of refraction), can further help to identify or constrain the aerosol model, or to retrieve directly water reflectance. Algorithms that exploit the new capabilities are presented, and their ability to improve accuracy is discussed. They embrace a modern, adapted heritage two-step algorithm and alternative schemes (deterministic, statistical) that aim at inverting the TOA signal in a single step. These schemes, by the nature of their construction, their robustness, their generalization properties, and their ability to associate uncertainties, are expected to become the new standard in the future. A strategy for atmospheric correction is presented that ensures continuity and consistency with past and present ocean-color missions while enabling full exploitation of the new dimensions and possibilities. Despite the major improvements anticipated with the PACE instruments, gaps/issues remain to be filled/tackled. They include dealing properly with whitecaps, taking into account Earth-curvature effects, correcting for adjacency effects, accounting for the coupling between scattering and absorption, modeling accurately water reflectance, and acquiring a sufficiently representative dataset of water reflectance in the UV to SWIR. Dedicated efforts, experimental and theoretical, are in order to gather the necessary information and rectify inadequacies. Ideas and solutions are put forward to address the unresolved issues. Thanks to its design and characteristics, the PACE mission will mark the beginning of a new era of unprecedented accuracy in ocean-color radiometry from space.

In this paper, we present aerosol retrieval results from the ACEPOL (Aerosol Characterization from Polarimeter and Lidar) campaign, which was a joint initiative between NASA and SRON – the Netherlands Institute for Space Research. The campaign took place in October–November 2017 over the western part of the United States. During ACEPOL six different instruments were deployed on the NASA ER-2 high-altitude aircraft, including four multi-angle polarimeters (MAPs): SPEX airborne, the Airborne Hyper Angular Rainbow Polarimeter (AirHARP), the Airborne Multi-angle SpectroPolarimetric Imager (AirMSPI), and the Research Scanning Polarimeter (RSP). Also, two lidars participated: the High Spectral Resolution Lidar-2 (HSRL-2) and the Cloud Physics Lidar (CPL). Flights were conducted mainly for scenes with low aerosol load over land, but some cases with higher AOD were also observed. We perform aerosol retrievals from SPEX airborne, RSP (410–865 nm range only), and AirMSPI using the SRON aerosol retrieval algorithm and compare the results against AERONET (AErosol RObotic NETwork) and HSRL-2 measurements (for SPEX airborne and RSP). All three MAPs compare well against AERONET for the aerosol optical depth (AOD), with a mean absolute error (MAE) between 0.014 and 0.024 at 440 nm. For the fine-mode effective radius the MAE ranges between 0.021 and 0.028 µm. For the comparison with HSRL-2 we focus on a day with low AOD (0.02–0.14 at 532 nm) over the California Central Valley, Arizona, and Nevada (26 October) as well as a flight with high AOD (including measurements with AOD>1.0 at 532 nm) over a prescribed forest fire in Arizona (9 November). For the day with low AOD the MAEs in AOD (at 532 nm) with HSRL-2 are 0.014 and 0.022 for SPEX and RSP, respectively, showing the capability of MAPs to provide accurate AOD retrievals for the challenging case of low AOD over land. For the retrievals over the smoke plume a reasonable agreement in AOD between the MAPs and HSRL-2 was also found (MAE 0.088 and 0.079 for SPEX and RSP, respectively), despite the fact that the comparison is hampered by large spatial variability in AOD throughout the smoke plume. A good comparison is also found between the MAPs and HSRL-2 for the aerosol depolarization ratio (a measure of particle sphericity), with an MAE of 0.023 and 0.016 for SPEX and RSP, respectively. Finally, SPEX and RSP agree very well for the retrieved microphysical and optical properties of the smoke plume.

NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in the time frame of late 2022 to early 2023, will carry the Ocean Color Instrument (OCI), a hyperspectral scanning radiometer, and two multiangle polarimeters (MAPs), the UMBC Hyper-Angular Rainbow Polarimeter 2 (HARP2) and the SRON Spectro-Polarimeter for Planetary EXploration one (SPEXone). One purpose of the PACE MAPs is to better characterize aerosol properties, which can then be used to improve atmospheric correction for the retrieval of ocean color in coastal waters. Though this is theoretically promising, the use of MAP data in the atmospheric correction of colocated hyperspectral ocean color measurements have not yet been well demonstrated. In this work, we performed aerosol retrievals using the MAP measurements from the Research Scanning Polarimeter (RSP) and demonstrate its application to the atmospheric correction of hyperspectral radiometric measurements from SPEX airborne. Both measurements were collected on the same aircraft from the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) field campaign in 2017. Two cases over ocean with small aerosol loading (aerosol optical depth ∼0.04) are identified including colocated RSP and SPEX airborne measurements and Aerosol Robotic Network (AERONET) ground-based observations. The aerosol retrievals are performed and compared with two options: one uses reflectance measurement only and the other uses both reflectance and polarization. It is demonstrated that polarization information helps reduce the uncertainties of aerosol microphysical and optical properties. The retrieved aerosol properties are then used to compute the contribution of atmosphere and ocean surface for atmospheric correction over the discrete bands from RSP measurements and the hyperspectral SPEX airborne measurements. The water-leaving signals determined this way are compared with both AERONET and Moderate Resolution Imaging Spectroradiometer (MODIS) ocean color products for performance analysis. The results and lessons learned from this work will provide a basis to fully exploit the information from the unique combination of sensors on PACE for aerosol characterization and ocean ecosystem research.

We present a neural network (NN) based algorithm for the retrieval of liquid low-level marine stratocumulus cloud microphysical property parameters (cloud optical depth, cloud droplet size effective radius and variance) from airborne multi-angle polarimetric measurements. We establish our retrieval method for the Research Scanning Polarimeter (RSP) airborne instrument, which measures both polarized and total reflectance in the spectral range of 410–2260 nm, scanning along the flight track at ∼150 viewing zenith angles spanning the angular range between −60° and 60°. In this study, we present the development of the algorithm, including the optimization and selection of input parameters and the network architecture. We perform a sensitivity study to test the effect of random and correlated instrument noise on the retrieval performance, and to assess which of the measured radiometric quantities (i.e., total reflectance, polarized reflectance, degree of linear polarization and combinations thereof) are best suited for marine stratocumulus liquid cloud property retrievals using simulated RSP data. Finally, we show the application of this method to airborne observations from the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) 2016 field campaign, which primarily encountered low altitude marine clouds. Retrieved cloud optical depth compares favorably (r2 = 0.96) to standard algorithms, but cloud droplet size effective radius less so (r2= 0.45), providing an assessment of the NN approach strengths and limitations. Specifically, the latter seemed to be affected by the cloud macro-structure and the liquid cloud droplet vertical distribution.

The classical multi-spectral Atmospheric Correction (AC) algorithm is inadequate for the new generation of spaceborne hyperspectral sensors such as NASA's first hyperspectral Ocean Color Instrument (OCI) onboard the anticipated Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite mission. The AC process must estimate and remove the atmospheric path radiance contribution due to the Rayleigh scattering by air molecules and scattering by aerosols from the measured top-of-atmosphere (TOA) radiance, compensate for the absorption by atmospheric gases, and correct for reflection and refraction of the air-sea interface. In this work, we present and evaluate an improved AC for hyperspectral sensors developed within NASA's SeaWiFS Data Analysis System software package (SeaDAS). The improvement is based on combining the classical AC approach of multi-spectral capabilities to correct for the atmospheric path radiance, extended to hyperspectral, with a gas correction algorithm to compensate for absorbing gases in the atmosphere, including water vapor. The SeaDAS-hyperspectral version is capable of operationally processing the AC of any hyperspectral airborne or spaceborne sensor. The new algorithm development was evaluated and assessed using the Hyperspectral Imager for Coastal Ocean (HICO) scenes collected at the Marine Optical BuoY (MOBY) site, and other SeaWiFS Bio-optical Archive and Storage System (SeaBASS) and AERosol Robotic NETwork - Ocean Color (AERONET-OC) coastal sites. A hyperspectral vicarious calibration was applied to HICO, showing the validity and consistency of HICO's ocean color products. The hyperspectral AC capability is currently available in SeaDAS to the scientific community at https://oceancolor.gsfc.nasa.gov/ .

Many applications of geophysical data – whether from surface observations, satellite retrievals, or model simulations – rely on aggregates produced at coarser spatial (e.g. degrees) and/or temporal (e.g. daily and monthly) resolution than the highest available from the technique. Almost all of these aggregates report the arithmetic mean and standard deviation as summary statistics, which are what data users employ in their analyses. These statistics are most meaningful for normally distributed data; however, for some quantities, such as aerosol optical depth (AOD), it is well-known that distributions are on large scales closer to log-normal, for which a geometric mean and standard deviation would be more appropriate. This study presents a method of assessing whether a given sample of data is more consistent with an underlying normal or log-normal distribution, using the Shapiro–Wilk test, and tests AOD frequency distributions on spatial scales of 1∘ and daily, monthly, and seasonal temporal scales. A broadly consistent picture is observed using Aerosol Robotic Network (AERONET), Multiangle Imaging SpectroRadiometer (MISR), Moderate Resolution Imagining Spectroradiometer (MODIS), and Goddard Earth Observing System Version 5 Nature Run (G5NR) data. These data sets are complementary: AERONET has the highest AOD accuracy but is sparse, and MISR and MODIS represent different satellite retrieval techniques and sampling. As a model simulation, G5NR is spatiotemporally complete. As timescales increase from days to months to seasons, data become increasingly more consistent with log-normal than normal distributions, and the differences between arithmetic- and geometric-mean AOD become larger, with geometric mean becoming systematically smaller. Assuming normality systematically overstates both the typical level of AOD and its variability. There is considerable regional heterogeneity in the results: in low-AOD regions such as the open ocean and mountains, often the AOD difference is small enough () to be unimportant for many applications, especially on daily timescales. However, in continental outflow regions and near source regions over land, and on monthly or seasonal timescales, the difference is frequently larger than the Global Climate Observation System (GCOS) goal uncertainty in a climate data record (the larger of 0.03 or 10 %). This is important because it shows that the sensitivity to an averaging method can and often does introduce systematic effects larger than the total goal GCOS uncertainty. Using three well-studied AERONET sites, the magnitude of estimated AOD trends is shown to be sensitive to the choice of arithmetic vs. geometric means, although the signs are consistent. The main recommendations from the study are that (1) the distribution of a geophysical quantity should be analysed in order to assess how best to aggregate it, (2) ideally AOD aggregates such as satellite level 3 products (but also ground-based data and model simulations) should report a geometric-mean or median AOD rather than (or in addition to) arithmetic-mean AOD, and (3) as this is unlikely in the short term due to the computational burden involved, users can calculate geometric-mean monthly aggregates from widely available daily mean data as a stopgap, as daily aggregates are less sensitive to the choice of aggregation scheme than those for monthly or seasonal aggregates. Furthermore, distribution shapes can have implications for the validity of statistical metrics often used for comparison and evaluation of data sets. The methodology is not restricted to AOD and can be applied to other quantities.

In this paper, we present aerosol retrieval results from the ACEPOL (Aerosol Characterization from Polarimeter and Lidar) campaign, which was a joint initiative between NASA and SRON – Netherlands Institute for Space Research. The campaign took place in October-November 2017 over the western part of the United States. During ACEPOL six different instruments were deployed on the NASA ER-2 high altitude aircraft, including four Multi-Angle Polarimeters (MAPs): SPEX airborne, the Airborne Hyper Angular Rainbow Polarimeter (AirHARP), the Airborne Multi-angle SpectroPolarimeter Imager (AirMSPI), and the Research Scanning Polarimeter (RSP). Also, two lidars participated: the High Spectral Resolution Lidar -2 (HSRL-2) and the Cloud Physics Lidar (CPL). Flights were conducted mainly for scenes with low aerosol load over land but also some cases with higher AOD were observed. We perform aerosol retrievals from SPEX airborne, RSP (410–865 nm range only), and AirMSPI using the SRON aerosol retrieval algorithm and compare the results against AERONET and HSRL-2 measurements (for SPEX airborne and RSP). All three MAPs compare well against AERONET for the Aerosol Optical Depth (AOD) (Mean Absolute Error (MAE) between 0.014–0.024 at 440 nm). For the fine mode effective radius the MAE ranges between 0.021–0.028 micron. For the comparison with HSRL-2 we focus on a day with low AOD (0.02–0.14 at 532 nm) over the California Central Valley, Arizona and Nevada (26 October) and a flight with high AOD (including measurements with AOD > 1.0 at 532 nm) over a prescribed forest fire in Arizona (9 November). For the day with low AOD the MAE in AOD (at 532 nm) with HSRL-2 are 0.014 and 0.022 for SPEX and RSP, respectively, showing the capability of MAPs to provide accurate AOD retrievals for the challenging case of low AOD over land. For the retrievals over the smoke plume also a reasonable agreement in AOD between the MAPs and HSRL-2 was found (MAE 0.088 and 0.079 for SPEX and RSP, respectively), despite the fact that the comparison is hampered by large spatial variability in AOD throughout the smoke plume. Also a good comparison is found between the MAPs and HSRL-2 for the aerosol depolarization ratio (a measure for particles sphericity) with MAE of 0.023 and 0.016 for SPEX and RSP, respectively. Finally, SPEX and RSP agree very well for the retrieved microphysical and optical properties of the smoke plume.

We developed a fast and accurate polynomial based atmospheric correction (POLYAC) algorithm for hyperspectral radiometric measurements, which parameterizes the atmospheric path radiances using aerosol properties retrieved from co-located multi-wavelength multi-angle polarimeter (MAP) measurements. This algorithm has been applied to co-located spectrometer for planetary exploration (SPEX) airborne and research scanning polarimeter (RSP) measurements, where SPEX airborne was used as a proxy of hyperspectral radiometers, and RSP as the MAP. The hyperspectral remote sensing reflectance obtained from POLYAC is accurate when compared to Aerosol Robotic Network (AERONET), and Visible Infrared Imaging Radiometer Suite (VIIRS) ocean color products. POLYAC provides a robust alternative atmospheric correction algorithm for hyperspectral or multi-spectral radiometric measurements for scenes involving coastal oceans and/or absorbing aerosols, where traditional atmospheric correction algorithms are less reliable.

There is considerable interest in retrieving profiles of aerosol effective radius, total number concentration, and complex refractive index from lidar measurements of extinction and backscatter at several wavelengths. The combination of three backscatter channels plus two extinction channels (3β + 2α) is particularly important since it is believed to be the minimum configuration necessary for the retrieval of aerosol microphysical properties and because the technological readiness of lidar systems permits this configuration on both an airborne and future spaceborne instrument. The second-generation NASA Langley airborne High Spectral Resolution Lidar (HSRL-2) has been making 3β + 2α measurements since 2012. The planned NASA Aerosol/Clouds/Ecosystems (ACE) satellite mission also recommends the 3β + 2α combination.Here we develop a deeper understanding of the information content and sensitivities of the 3β + 2α system in terms of aerosol microphysical parameters of interest. We use a retrieval-free methodology to determine the basic sensitivities of the measurements independent of retrieval assumptions and constraints. We calculate information content and uncertainty metrics using tools borrowed from the optimal estimation methodology based on Bayes' theorem, using a simplified forward model look-up table, with no explicit inversion. The forward model is simplified to represent spherical particles, monomodal log-normal size distributions, and wavelength-independent refractive indices. Since we only use the forward model with no retrieval, the given simplified aerosol scenario is applicable as a best case for all existing retrievals in the absence of additional constraints. Retrieval-dependent errors due to mismatch between retrieval assumptions and true atmospheric aerosols are not included in this sensitivity study, and neither are retrieval errors that may be introduced in the inversion process. The choice of a simplified model adds clarity to the understanding of the uncertainties in such retrievals, since it allows for separately assessing the sensitivities and uncertainties of the measurements alone that cannot be corrected by any potential or theoretical improvements to retrieval methodology but must instead be addressed by adding information content.The sensitivity metrics allow for identifying (1) information content of the measurements vs. a priori information; (2) error bars on the retrieved parameters; and (3) potential sources of cross-talk or "compensating" errors wherein different retrieval parameters are not independently captured by the measurements. The results suggest that the 3β + 2α measurement system is underdetermined with respect to the full suite of microphysical parameters considered in this study and that additional information is required, in the form of additional coincident measurements (e.g., sun-photometer or polarimeter) or a priori retrieval constraints. A specific recommendation is given for addressing cross-talk between effective radius and total number concentration.

International audience; Earth observing systems have proven to be a unique source of long-term synoptic information on numerous physical, chemical and biological parameters on a global scale. Merging this information for integrated studies that peruse key questions about the ocean-atmosphere interface is, however, very challenging. Such studies require interdisciplinary frameworks and novel insights into ways to address the problem. We present here a perspective review on how current and emerging remote sensing technologies could help address two scientific questions within the Surface Ocean-Lower Atmosphere Study (SOLAS) science plan: (1) to what extent doesupper-ocean biology affect the composition and radiative properties of the marine boundary layer; and (2) to what extent does upper-ocean turbulence drive fluxes of mass and energy at the air-sea interface. We provide a thorough review of how these questions have been addressed and discuss novel potential avenues using multiplatform space-borne missions, from visible to microwave, active and passive sensors.

We present retrievals of water cloud properties from the measurements made by the Research Scanning Polarimeter (RSP) during the Polarimeter Definition Experiment (PODEX) held between January 14 and February 6, 2013. The RSP was onboard the high-altitude NASA ER-2 aircraft based at NASA Dryden Aircraft Operation Facility in Palmdale, California. The retrieved cloud characteristics include cloud optical thickness, effective radius and variance of cloud droplet size distribution derived using a parameter-fitting technique, as well as the complete droplet size distribution function obtained by means of Rainbow Fourier Transform. Multi-modal size distributions are decomposed into several modes and the respective effective radii and variances are computed. The methodology used to produce the retrieval dataset is illustrated on the examples of a marine stratocumulus deck off California coast and stratus/fog over California's Central Valley. In the latter case the observed bimodal droplet size distributions were attributed to two-layer cloud structure. All retrieval data are available online from NASA GISS website.

The primary goal of this project has been to investigate if ground-based visible and near-infrared passive radiometers that have polarization sensitivity can determine the thermodynamic phase of overlying clouds, i.e., if they are comprised of liquid droplets or ice particles. While this knowledge is important by itself for our understanding of the global climate, it can also help improve cloud property retrieval algorithms that use total (unpolarized) radiance to determine cloud optical depth (COD). This is a potentially unexploited capability of some instruments in the NASA Aerosol Robotic Network (AERONET), which, if practical, could expand the products of that global instrument network at minimal additional cost. We performed simulations that found, for zenith observations, that cloud thermodynamic phase is often expressed in the sign of the Q component of the Stokes polarization vector. We chose our reference frame as the plane containing solar and observation vectors, so the sign of Q indicates the polarization direction, parallel (positive) or perpendicular (parallel) to that plane. Since the fraction of linearly polarized to total light is inversely proportional to COD, optically thin clouds are most likely to create a signal greater than instrument noise. Besides COD and instrument accuracy, other important factors for the determination of cloud thermodynamic phase are the solar and observation geometry (scattering angles between 40 and 60° are best), and the properties of ice particles (pristine particles may have halos or other features that make them difficult to distinguish from water droplets at specific scattering angles, while extreme ice crystal aspect ratios polarize more than compact particles). We tested the conclusions of our simulations using data from polarimetrically sensitive versions of the Cimel 318 sun photometer/radiometer that compose a portion of AERONET. Most algorithms that exploit Cimel polarized observations use the degree of linear polarization (DoLP), not the individual Stokes vector elements (such as Q). Ability to determine cloud thermodynamic phase depends on Q measurement accuracy, which has not been rigorously assessed for Cimel instruments. For this reason, we did not know if cloud phase could be determined from Cimel observations successfully. Indeed, comparisons to ceilometer observations with a single polarized spectral channel version of the Cimel at a site in the Netherlands showed little correlation. Comparisons to lidar observations with a more recently developed, multi-wavelength polarized Cimel in Maryland, USA, show more promise. The lack of well-characterized observations has prompted us to begin the development of a small test instrument called the Sky Polarization Radiometric Instrument for Test and Evaluation (SPRITE). This instrument is specifically devoted to the accurate observation of Q, and the testing of calibration and uncertainty assessment techniques, with the ultimate goal of understanding the practical feasibility of these measurements.

The top-of-atmosphere (TOA) radiation field from a coupled atmosphere-ocean system (CAOS) includes contributions from the atmosphere, surface, and water body. Atmospheric correction of ocean color imagery is to retrieve water-leaving radiance from the TOA measurement, from which ocean bio-optical properties can be obtained. Knowledge of the absolute and relative magnitudes of water-leaving signal in the TOA radiation field is important for designing new atmospheric correction algorithms and developing retrieval algorithms for new ocean biogeochemical parameters. In this paper we present a systematic sensitivity study of water-leaving contribution to the TOA radiation field, from 340 nm to 865 nm, with polarization included. Ocean water inherent optical properties are derived from bio-optical models for two kinds of waters, one dominated by phytoplankton (PDW) and the other by non-algae particles (NDW). In addition to elastic scattering, Raman scattering and fluorescence from dissolved organic matter in ocean waters are included. Our sensitivity study shows that the polarized reflectance is minimized for both CAOS and ocean signals in the backscattering half plane, which leads to numerical instability when calculating water leaving relative contribution, the ratio between polarized water leaving and CAOS signals. If the backscattering plane is excluded, the water-leaving polarized signal contributes less than 9% to the TOA polarized reflectance for PDW in the whole spectra. For NDW, the polarized water leaving contribution can be as much as 20% in the wavelength range from 470 to 670 nm. For wavelengths shorter than 452 nm or longer than 865 nm, the water leaving contribution to the TOA polarized reflectance is in general smaller than 5% for NDW. For the TOA total reflectance, the water-leaving contribution has maximum values ranging from 7% to 16% at variable wavelengths from 400 nm to 550 nm from PDW. The water leaving contribution to the TOA total reflectance can be as large as 35% for NDW, which is in general peaked at 550 nm. Both the total and polarized reflectances from water-leaving contributions approach zero in the ultraviolet and near infrared bands. These facts can be used as constraints or guidelines when estimating the water leaving contribution to the TOA reflectance for new atmospheric correction algorithms for ocean color imagery.

Many passive remote sensing techniques have been developed to retrieve cloud microphysical properties from satellite-based sensors, with the most common approaches being the bispectral and polarimetric techniques. These two vastly different retrieval techniques have been implemented for a variety of polar-orbiting and geostationary satellite platforms, providing global climatological datasets. Prior instrument comparison studies have shown that there are systematic differences between the droplet size retrieval products (effective radius) of bispectral (e.g. MODIS, Moderate Resolution Imaging Spectroradiometer) and polarimetric (e.g. POLDER, Polarization and Directionality of Earth’s Reflectances) instruments. However, intercomparisons of airborne bispectral and polarimetric instruments have yielded results that do not appear to be systematically biased relative to one another. Diagnosing this discrepancy is complicated, because it is often difficult for instrument intercomparison studies to isolate differences between retrieval technique sensitivities and specific instrumental differences such as calibration, atmospheric correction, etc. In addition to these technical differences the polarimetric retrieval is also sensitive to the dispersion of the droplet size distribution (effective variance), which could influence the interpretation of the droplet size retrieval. To avoid these instrument-dependent complications, this study makes use of a cloud remote sensing retrieval simulator. Created by coupling a large eddy simulation (LES) cloud model with radiative transfer models, the simulator serves as a test bed for understanding differences between bispectral and polarimetric retrievals. With the help of this simulator we can not only compare the two techniques to one another (retrieval intercomparison), but also validate retrievals directly against the LES cloud properties. Using the satellite retrieval simulator we are able to verify that at high spatial resolution (50 m) the bispectral and polarimetric retrievals are indeed highly correlated with one another. The small differences at high spatial resolution can be attributed to different sensitivity limitations of the two retrievals. In contrast, a systematic difference between the two retrievals emerges at coarser resolution. This bias largely stems from differences related to sensitivity of the two retrievals to unresolved inhomogeneities in effective variance and optical thickness. The influence of coarse angular resolution is found to increase uncertainty in the polarimetric retrieval, but generally maintains a constant mean value.

Classifying observed aerosols into types (e.g., urban-industrial, biomass burning, mineral dust, maritime) helps to understand aerosol sources, transformations, effects, and feedback mechanisms; to improve accuracy of satellite retrievals; and to quantify aerosol radiative impacts on climate. The number of aerosol parameters retrieved from spaceborne sensors has been growing, from the initial aerosol optical depth (AOD) at one or a few wavelengths to a list that now includes AOD, complex refractive index, single scattering albedo (SSA), and depolarization of backscatter, each at several wavelengths, plus several particle size and shape parameters. Making optimal use of these varied data products requires objective, multidimensional analysis methods. We describe such a method, which makes explicit use of uncertainties in input parameters. It treats an N-parameter retrieved data point and its N-dimensional uncertainty as an extended data point, E. It then uses a modified Mahalanobis distance, DEC, to assign an observation to the class (cluster) C that has minimum DEC from the point. We use parameters retrieved from the Aerosol Robotic Network (AERONET) to define seven prespecified clusters (pure dust, polluted dust, urban-industrial/developed economy, urban-industrial/developing economy, dark biomass smoke, light biomass smoke, and pure marine), and we demonstrate application of the method to a 5 year record of retrievals from the spaceborne Polarization and Directionality of the Earth's Reflectances 3 (POLDER 3) polarimeter over the island of Crete, Greece. Results show changes of aerosol type at this location in the eastern Mediterranean Sea, which is influenced by a wide variety of aerosol sources.

In early 2013, three airborne polarimeters were flown on the high altitude NASA ER-2 aircraft in California for the Polarimeter Definition Experiment (PODEX). PODEX supported the pre-formulation NASA Aerosol-Cloud-Ecosystem (ACE) mission, which calls for an imaging polarimeter in polar orbit (among other instruments) for the remote sensing of aerosols, oceans, and clouds. Several polarimeter concepts exist as airborne prototypes, some of which were deployed during PODEX as a capabilities test. Two of those instruments to date have successfully produced Level 1 (georegistered, calibrated radiance and polarization) data from that campaign: the Airborne Multiangle Spectropolarimetric Imager (AirMSPI) and the Research Scanning Polarimeter (RSP). We compared georegistered observations of a variety of scene types by these instruments to test whether Level 1 products agreed within stated uncertainties. Initial comparisons found radiometric agreement, but polarimetric biases beyond measurement uncertainties. After subsequent updates to calibration, georegistration, and the measurement uncertainty models, observations from the instruments now largely agree within stated uncertainties. However, the 470 nm reflectance channels have a roughly +6% bias of AirMSPI relative to RSP, beyond expected measurement uncertainties. We also find that observations of dark (ocean) scenes, where polarimetric uncertainty is expected to be largest, do not agree within stated polarimetric uncertainties. Otherwise, AirMSPI and RSP observations are consistent within measurement uncertainty expectations, providing credibility for the subsequent creation of Level 2 (geophysical product) data from these instruments, and comparison thereof. The techniques used in this work can also form a methodological basis for other intercomparisons, for example, of the data gathered during the recent Aerosol Characterization from Polarimeter and Lidar (ACEPOL) field campaign, carried out in October and November of 2017 with four polarimeters (including AirMSPI and RSP).

We have built an aerosol retrieval algorithm which combines the Look Up Table (LUT) and least squares fitting methods. The algorithm is based on the multi-angle multi-wavelength polarized reflectance at the Top Of the Atmosphere (TOA) measured by the Research Scanning Polarimeter (RSP). The aerosol state parameters are the aerosol particle effective radius, effective variance, complex index of refraction, and aerosol column number density. Monomodal aerosol size distribution is assumed. The Cost Function (CF) of the least squares fitting is designed in consideration of the RSP instrumental characteristics. The aerosol retrieval algorithm inherently assumes one layer of aerosols within the atmosphere. Synthetic polarized radiance data at the TOA have been created assuming either one or two layers of aerosols using the vector radiative transfer code based on successive order of scattering method. Test cases for one-layer aerosol systems show great performance. Around 90% of the total 1200 test cases have CF values smaller than 50. For these cases, the correlation coefficients of the input and retrieved parameters are generally around or larger than 0.98. The effective variance is slightly worse with the correlation coefficient of 0.76938. On the other hand, test cases for two-layer aerosol systems show that only 50% of the total (also 1200) tested cases have final CFs smaller than 50. Among these successful cases ( CF ≤ 50 ), the retrieved optical depth can still be interpreted as the total column optical depth, though the correlation coefficient is decreased in comparison with the one-layer aerosol cases. We propose to interpret other retrieved aerosol parameters as the average of corresponding parameters for each layer weighted by its optical depth at 865 nm. The retrieved effective radius and complex refractive index can be explained by this scheme (correlation coefficient around 0.9). The effective variance, however, shows decreased performance with the correlation coefficient of 0.46421. This may be due to the strong nonlinearity dependence of the scattering properties on the effective variance.

In 2010, the Goddard Institute for Space Studies (GISS) Research Scanning Polarimeter (RSP) performed several aerial surveys over the region affected by the oil spill caused by the explosion of the Deepwater Horizon offshore platform. The instrument was deployed on the NASA Langley B200 aircraft together with the High Spectral Resolution Lidar (HSRL), which provides information on the distribution of the aerosol layers beneath the aircraft, including an accurate estimate of aerosol optical depth. This work illustrates the merits of polarization measurements in detecting variations of ocean surface properties linked to the presence of an oil slick. In particular, we make use of the degree of linear polarization in the glint region, which is severely affected by variations in the refractive index but insensitive to the waviness of the water surface. Alterations in the surface optical properties are therefore expected to directly affect the polarization response of the RSP channel at 2264 nm, where both molecular and aerosol scattering are negligible and virtually all of the observed signal is generated via Fresnel reflection at the surface. The glint profile at this wavelength is fitted with a model which can optimally estimate refractive index, wind speed and direction, together with aircraft attitude variations affecting the viewing geometry. The retrieved refractive index markedly increases over oil-contaminated waters, while the apparent wind speed is significantly lower than in adjacent uncontaminated areas, suggesting that the slick dampens high-frequency components of the ocean wave spectrum. The constraint on surface reflectance provided by the short-wave infrared channels is a cornerstone of established procedures to retrieve atmospheric aerosol microphysical parameters based on the inversion of the RSP multispectral measurements. This retrieval, which benefits from the ancillary information provided by the HSRL, was in this specific case hampered by prohibitive variability in atmospheric conditions (very inhomogeneous aerosol distribution and cloud cover). Although the results presented for the surface are essentially unaffected, we discuss the results obtained by typing algorithms in sorting the complex mix of aerosol types, and show evidence of oriented ice in cirrus clouds present in the area. In this context, polarization measurements at 1880 nm were used to infer ice habit and cirrus optical depth, which was found in the subvisual/threshold-visible regime, confirming the utility of the aforementioned RSP channel for the remote sensing of even thin cold clouds.

For remote sensing of aerosol over the ocean, there is a contribution from light scattered underwater. The brightness and spectrum of this light depends on the biomass content of the ocean, such that variations in the color of the ocean can be observed even from space. Rayleigh scattering by pure sea water, and Rayleigh-Gans type scattering by plankton, causes this light to be polarized with a distinctive angular distribution. To study the contribution of this underwater light polarization to multiangle, multispectral observations of polarized reflectance over ocean, we previously developed a hydrosol model for use in underwater light scattering computations that produces realistic variations of the ocean color and the underwater light polarization signature of pure sea water. In this work we review this hydrosol model, include a correction for the spectrum of the particulate scattering coefficient and backscattering efficiency, and discuss its sensitivity to variations in colored dissolved organic matter (CDOM) and in the scattering function of marine particulates. We then apply this model to measurements of total and polarized reflectance that were acquired over open ocean during the MILAGRO field campaign by the airborne Research Scanning Polarimeter (RSP). Analyses show that our hydrosol model faithfully reproduces the water-leaving contributions to RSP reflectance, and that the sensitivity of these contributions to Chlorophyll a concentration [Chl] in the ocean varies with the azimuth, height, and wavelength of observations. We also show that the impact of variations in CDOM on the polarized reflectance observed by the RSP at low altitude is comparable to or much less than the standard error of this reflectance whereas their effects in total reflectance may be substantial (i.e. up to >30%). Finally, we extend our study of polarized reflectance variations with [Chl] and CDOM to include results for simulated spaceborne observations.

Absorbing aerosols play an important, but uncertain, role in the global climate. Much of this uncertainty is due to a lack of adequate aerosol measurements. While great strides have been made in observational capability in the previous years and decades, it has become increasingly apparent that this development must continue. Scanning polarimeters have been designed to help resolve this issue by making accurate, multi-spectral, multi-angle polarized observations. This work involves the use of the Research Scanning Polarimeter (RSP). The RSP was designed as the airborne prototype for the Aerosol Polarimetery Sensor (APS), which was due to be launched as part of the (ultimately failed) NASA Glory mission. Field observations with the RSP, however, have established that simultaneous retrievals of aerosol absorption and vertical distribution over bright land surfaces are quite uncertain. We test a merger of RSP and High Spectral Resolution Lidar (HSRL) data with observations of boreal forest fire smoke, collected during the Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS). During ARCTAS, the RSP and HSRL instruments were mounted on the same aircraft, and validation data were provided by instruments on an aircraft flying a coordinated flight pattern. We found that the lidar data did indeed improve aerosol retrievals using an optimal estimation method, although not primarily because of the contraints imposed on the aerosol vertical distribution. The more useful piece of information from the HSRL was the total column aerosol optical depth, which was used to select the initial value (optimization starting point) of the aerosol number concentration. When ground based sun photometer network climatologies of number concentration were used as an initial value, we found that roughly half of the retrievals had unrealistic sizes and imaginary indices, even though the retrieved spectral optical depths agreed within uncertainties to independent observations. The convergence to an unrealistic local minimum by the optimal estimator is related to the relatively low sensitivity to particles smaller than 0.1 (μm) at large optical thicknesses. Thus, optimization algorithms used for operational aerosol retrievals of the fine mode size distribution, when the total optical depth is large, will require initial values generated from table look-ups that exclude unrealistic size/complex index mixtures. External constraints from lidar on initial values used in the optimal estimation methods will also be valuable in reducing the likelihood of obtaining spurious retrievals.

The Airborne Multiangle SpectroPolarimetric Imager (AirMSPI), a precursor to the future Multi-Angle Imager for Aerosols satellite instrument, is a remote-sensing instrument for the characterization of atmospheric aerosols and clouds. To help discriminate between different aerosol particle types, which is crucial to improve our understanding of their impact on climate and air quality, AirMSPI acquires imagery over multiple view angles in the ultraviolet, visible, and near-infrared, and it employs dual photoelastic modulators (PEMs) to target an uncertainty requirement of ±0.005 in the degree of linear polarization (DoLP) at selected wavelengths. Laboratory polarimetric calibrations using a second-generation Polarization State Generator-2 (PSG-2) and validation measurements at 0

Governed by the OSA Open Access Publishing Agreement. Authors and readers may use, reuse, and build upon the article, or use it for text or data mining without asking prior permission from the publisher or the Author(s), as long as the purpose is non-commercial and appropriate attribution is maintained., Ocean color remote sensing is an important tool to monitor water quality and biogeochemical conditions of ocean. Atmospheric correction, which obtains water-leaving radiance from the total radiance measured by satellite-borne or airborne sensors, remains a challenging task for coastal waters due to the complex optical properties of aerosols and ocean waters. In this paper, we report a research algorithm on aerosol and ocean color retrieval with emphasis on coastal waters, which uses coupled atmosphere and ocean radiative transfer model to fit polarized radiance measurements at multiple viewing angles and multiple wavelengths. Ocean optical properties are characterized by a generalized bio-optical model with direct accounting for the absorption and scattering of phytoplankton, colored dissolved organic matter (CDOM) and non-algal particles (NAP). Our retrieval algorithm can accurately determine the water-leaving radiance and aerosol properties for coastal waters, and may be used to improve the atmospheric correction when apply to a hyperspectral ocean color instrument.

For several years, the NASA SIMBIOS Project has collected, processed, and archived optical aerosol data from shipboard sun photometers. The calibration, processing, quality control, and archival methodology for handheld sun photometers are described here, along with their deployment statistics. Data processing has been standardized for all instruments by using identical calibration methods, ancillary data, and processing software. Statistical analysis reveals a dataset influenced by its temporal and geographic distribution, while multimodal histograms for aerosol optical thickness (AOT) and Angstrom exponent reveal varied aerosol populations. A K-means unsupervised classification technique is used to separate these populations. This separation is validated by showing individual classes are more likely to be log-normally (for AOTs) or normally (for Angstrom exponents) distributed than the dataset as a whole. Properties for each class are presented, along with the characteristics of each class by regional oceanic basin. Results also compare favorably with maritime aerosols measured by land-based AERONET Cimels in island sites, while providing data coverage in previously sparsely sampled regions. Aerosol models employed by SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) also compare favorably with these ground based measurements.