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Multi-angle polarimetric (MAP) measurements can enable detailed characterization of aerosol microphysical and optical properties and improve atmospheric correction in ocean color remote sensing. Advanced retrieval algorithms have been developed to obtain multiple geophysical parameters in the atmosphere-ocean system. Theoretical pixel-wise retrieval uncertainties based on error propagation have been used to quantify retrieval performance and determine the quality of data products. However, standard error propagation techniques in high-dimensional retrievals may not always represent true retrieval errors well due to issues such as local minima and nonlinearity of radiative transfer near the solution. In this work, we analyze these theoretical uncertainty estimates and validate them using a flexible Monte Carlo approach. The Fast Multi-Angular Polarimetric Ocean coLor (FastMAPOL) retrieval algorithm, based on several neural network forward models, is used to conduct the retrievals and uncertainty quantification on both synthetic HARP2 (Hyper-Angular Rainbow Polarimeter 2) and AirHARP (airborne version of HARP2) datasets. In addition, for practical application of the technique to uncertainty evaluation in operational data processing, we use the automatic differentiation method to calculate derivatives analytically based on the neural network models. Both the speed and accuracy associated with uncertainty quantification for MAP retrievals are addressed in this study. Pixel-wise retrieval uncertainties are further evaluated for the real AirHARP field campaign data. The uncertainty quantification methods and results can be used to evaluate the quality of data products, and guide MAP algorithm development for current and future satellite systems such as NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. [ABSTRACT FROM AUTHOR]

Except for excessive nutrient input and climate warming, the rapidly rising SO4 2- 20 concentration is considered as a crucial contributor to the eutrophication in shallow lakes, however, the driving process and mechanism are still far from clear. In this study, we constructed a series of microcosms with initial SO4 2- concentrations of 0, 30, 60, 90, 120 and 150 mg/L to simulate the rapidly SO4 2- increase of Lake Taihu subjected to cyanobacteria blooms. Results showed that the sulfate reduction rate was stimulated by the increase of initial SO4 2- concentrations and cyanobacteria-derived organic matter, with the maximal sulfate reduction rate of 39.68 mg/L∙d in the treatment of 150 mg/L SO4 2- concentration. During the sulfate reduction, the produced maximal ∑S 2- concentration in the overlying water and acid volatile sulfate (AVS) in the sediments were 3.15 mg/L and 11.11 mg/kg, respectively, and both of them were positively correlated with initial SO4 2- concentrations (R²=0.97; R² 30 =0.92). The increasing abundance of sulfate reduction bacteria (SRB) was also linearly correlated with initial SO4 2- concentrations (R²=0.96), ranging from 6.65×107 to 1.97×108 copies/g. However, the Fe2+ concentrations displayed a negative correlation with initial SO4 2- concentrations, and the final Fe2+ concentrations were 9.68, 7.07, 6.5, 5.57, 4.42 and 3.46 mg/L, respectively. As a result, the released TP in the overlying water, to promote the eutrophication, was up to 1.4 mg/L in the treatment of 150 mg/L SO4 2- concentration. Therefore, it is necessary to consider the effect of rapidly increasing SO4 2- concentrations on the release of endogenous phosphorus and the eutrophication in lakes. [ABSTRACT FROM AUTHOR]

NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in the timeframe of 2023, will carry a hyperspectral scanning radiometer named the 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 HARP2 instrument and its predecessors. 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 0.01s 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. [ABSTRACT FROM AUTHOR]

Multi-angle polarimetric (MAP) imaging of Earth scenes can be used for the retrieval of microphysical and optical parameters of aerosols and clouds. The Airborne Hyper-Angular Rainbow Polarimeter (AirHARP) is an aircraft MAP instrument with the hyper-angular imaging capability of 60 along-track viewing angles at 670 nm, and 20 along-track viewing angles at other wavelengths 440, 550, 870 nm across the full 114° (94°) along-track (cross-track) field-of-view. Here we report the retrieval of aerosol properties using the Generalized Retrieval of Aerosols and Surface Properties (GRASP) algorithm applied to AirHARP observations collected during the NASA Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign in October-November 2017. The retrieved aerosol properties include spherical fraction (SF), aerosol volume concentration in multiple size distribution modes, and with sufficient aerosol loading, complex aerosol refractive index. From these primary retrievals, we derive aerosol optical depth (AOD), Angstrom exponent (AE), and single scattering albedo (SSA). AOD retrieved from AirHARP measurements are compared with the High Spectral Resolution LiDAR-2 (HSRL2) AOD measurements at 532 nm and validated with measurements from collocated Aerosol Robotic NETwork (AERONET) stations. A good correlation with HSRL2 (… = 0.940, | … = 0.062) and AERONET AOD (0.013 ≤ Mean Absolute Error (…) ≤ 0.017, 0.013 ≤ | … | ≤ 0.017) measurements is observed for the collocated points. Forest fire smoke intercepted during ACEPOL provided a situation with sufficient aerosol loading to retrieve the real part of the refractive index (RRI) of 1.55 and the imaginary part of the refractive index (IRI) of 0.024 The derived SSAs for this case are 0.87, 0.86, 0.84, 0.81 at wavelengths of 440 nm, 550 nm, 670 nm, and 870 nm, respectively. Finer particles with an average AE of 1.53, volume median radius of 0.157 µm and standard deviation of 0.55 µm for fine mode is observed for the same smoke plume. These results serve as a proxy for the scale and detail of aerosol retrievals that are anticipated from future space mission data, as :HARP CubeSat (mission begins 2020) and HARP2 (aboard the NASA PACE mission with launch in 2023) are near duplicates of AirHARP and are expected to provide the same level of aerosol characterization. [ABSTRACT FROM AUTHOR]

SO2 and NO2 observations from the Ozone Mapping and Profiler Suite (OMPS) sensor are used for the first time in conjunction with GEOS-Chem adjoint model to optimize both SO2 and NOx emission estimates over China for October 2013. OMPS SO2 and NO2 observations are first assimilated separately to optimize emissions of SO2 and NOx, respectively. Posterior emissions, compared to the prior, yield improvements in simulating columnar SO2 and NO2, in comparison to measurements from OMI and OMPS. The posterior SO2 and NOx emissions from separate inversions are 748 Gg S and 672 Gg N, which are 36 % and 6 % smaller than prior MIX emissions, respectively. In spite of the large reduction of SO2 emissions over the North China Plain, the simulated sulfate-nitrate-ammonium Aerosol Optical Depth (AOD) only decrease slightly, which can be attributed to (a) nitrate rather than sulfate as the dominant contributor to AOD and (b) replacement of ammonium sulfate with ammonium nitrate as SO2 emissions are reduced. Both data quality control and the weight given to SO2 relative to NO2 observations can affect the spatial distributions of the joint inversion results. When the latter is properly balanced, the posterior emissions from assimilating OMPS SO2 and NO2 jointly yield a difference of -3 % to 15 % with respect to the separate assimilations for total anthropogenic SO2 emissions and ± 2 % for total anthropogenic NOx emissions; but the differences can be up to 100 % for SO2 and 40 % for NO2 in some grid cells. Improvements on SO2 and NO2 simulations evaluated with OMPS and OMI measurements from the joint inversions are overall consistent with those from separate inversions. Moreover, the joint assimilations save ~ 50 % of the computational time than assimilating SO2 and NO2 separately when computational resources are limited to run one inversion at a time sequentially. The sensitivity analysis shows that a perturbation of NH3 to 50 % (20 %) of the prior emission inventory: (a) has negligible impact on the separate SO2 inversion, but can lead to decrease of posterior SO2 emissions over China by -2.4 % (-7.0 %) in total and up to -9.0 % (-27.7 %) in some grid cells in the joint inversion with NO2; (b) yield posterior NOx emissions over China decrease by -0.7 % (-2.8 %) for the separate NO2 inversion and by -2.7 % (-5.3 %) in total and up to -15.2 % (-29.4 %) in some grid cells for the joint inversion. The large reduction of SO2 between 2010 and 2013, however, only leads to ~ 10 % decrease of aerosol optical depth regionally; reducing surface aerosol concentration requires the reduction of emissions of NH3 as well. [ABSTRACT FROM AUTHOR]

Lofted mineral dust over data-sparse regions presents considerable challenges to satellite-based remote sensing methods and numerical weather prediction alike. The Southwest Asia domain is replete with such examples, with its diverse array of dust sources, dust mineralogy, and meteorologically-driven lofting mechanisms on multiple spatial and temporal scales. A microcosm of these challenges occurred over 3–4 August 2016 when two dust plumes, one lofted within an inland dry air mass and another embedded within a moist air mass, met over the Southern Arabian Peninsula. Whereas conventional infrared-based techniques readily detected the dry air mass dust plume, they experienced marked difficulties in detecting the moist air mass dust plume, which only became apparent when visible reflectance revealed it crossing over an adjacent dark water background. In combining information from numerical modelling, multi-satellite/multi-sensor observations of lofted dust and moisture profiles, and idealized radiative transfer simulations, we develop a better understanding of the environmental controls of this event, characterizing the sensitivity of infrared-based dust detection to column water vapor, dust vertical extent, and dust optical properties. Differences in assumptions of dust complex refractive index translate to variations in the sign and magnitude of the split-window brightness temperature difference commonly used for detecting mineral dust. A multi-sensor technique for mitigating the radiative masking effects of water vapor via modulation of the split-window dust-detection threshold, predicated on idealized simulations tied to these driving factors, is proposed and demonstrated. The new technique, indexed to independent-sensor description of the surface-to-500 mb atmospheric column moisture, reveals parts of the missing dust plume embedded in the moist air mass, with best performance over land surfaces. [ABSTRACT FROM AUTHOR]

We present an algorithm for retrieving aerosol layer height (ALH) and aerosol optical depth (AOD) for smoke over vegetated land and water surfaces from measurements of the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR). The algorithm uses earth-reflected radiances in six EPIC bands in visible and near-infrared and incorporates flexible spectral fittings that account for specifics of land and water surface reflectivity. The fitting procedure first determines AOD using EPIC atmospheric window bands (443nm, 551nm, 680nm, and 780nm), then uses oxygen (O2) A and B bands (688nm and 764nm) to derive the ALH that represents an optical centroid altitude. ALH retrieval over vegetated surface primarily takes advantage of the measurements in the O2 B band. We applied the algorithm to EPIC observations of several biomass burning events over United State and Canada in August 2017. We found the algorithm can well capture AOD and ALH multiple times daily over water and vegetated land surface. Validations are performed against aerosol extinction profile detected by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and AOD observed at nine Aerosol Robotic Network (AERONET) sites, showing, in average, an error of 0.58km and a bias of -0.13km in retrieved ALH and an error of 0.05 and a bias of 0.03 in retrieved AOD. Additionally, we show that the aerosol height information retrieved by the present algorithm can potentially benefit the retrieval of aerosol properties from the EPIC's ultraviolet (UV) bands. [ABSTRACT FROM AUTHOR]

Theoretical analysis is conducted to reveal the information content of aerosol vertical profile in space-borne measurements of the backscattered radiance and degree of linear polarization (DOLP) in oxygen (O2) A and B bands. Assuming a quasi-Gaussian shape for aerosol vertical profile characterized by peak height H and half width γ (at half maximum), the Unified Linearized Vector Radiative Transfer Model (UNL-VRTM) is used to simulate the Stokes fourvector elements of upwelling radiation at the top of atmosphere (TOA) and their Jacobians with respect to H and γ. Calculations for different aerosol types and different combinations of H and values show that the wide range of gas absorption optical depth in O2 A and B band enables the sensitivity of backscattered DOLP and radiance at TOA to the aerosol layer at different altitudes. Quantitatively, DOLP in O2 A and B bands is found to be more sensitive to H and γ than radiance, especially over the bright surfaces (with large visible reflectance). In many O2 absorption wavelengths, the degree of freedom of signal (DFS) for retrieving H (or γ) generally increases with H (and γ) and can be close to unity in many cases, assuming that the composite uncertainty from surface and aerosol scattering properties as well as measurements is less than 5 %. Further analysis demonstrates that DFS needed for simultaneous retrieval of H and γ can be obtained from a combined use of DOLP measurements at ~10-100 O2 A and B absorption wavelengths (or channels), depending on the specific values of H. The higher the aerosol layer, the fewer number of channels for DOLP measurements in O2 A and B bands are needed for characterizing H and . Future hyperspectral measurements of DOLP in O2 A and B bands are needed to continue studying their potential and their combination with radiance and DOLP in atmospheric window channels for retrieving the vertical profiles of aerosols, especially highly scattering aerosols, over land. [ABSTRACT FROM AUTHOR]