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From a passive satellite remote sensing point of view, the richest set of information on aerosol properties can be obtained from instruments that measure both intensity and polarization of backscattered sunlight at multiple wavelengths and multiple viewing angles for one ground pixel. However, it is challenging to exploit this information at a global scale because complex algorithms are needed with many fit parameters (aerosol and land/ocean reflection), based on online radiative transfer models. So far, two such algorithms have demonstrated this capability at a global scale: the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm and the Remote sensing of Trace gas and Aerosol Products (RemoTAP) algorithm. In this paper, we present a detailed comparison of the most recent versions of RemoTAP and GRASP. We evaluate both algorithms for synthetic observations, for real PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) observations against AERONET (Aerosol Robotic Network) for common pixels, and for global PARASOL retrievals for the year 2008. For the aerosol optical depth (AOD) over land, both algorithms show a root mean square error (RMSE) of 0.10 (at 550 nm). For single scattering albedo (SSA), both algorithms show a good performance in terms of RMSE (0.04), but RemoTAP has a smaller bias (0.002) compared to GRASP (0.021). For the Ångström exponent (AE), GRASP has a smaller RMSE (0.367) than RemoTAP (0.387), mainly caused by a small overestimate of AE at low values (large particles). Over ocean both algorithms perform very well. For AOD, RemoTAP has an RMSE of 0.057 and GRASP an even smaller RMSE of 0.047. For AE, the RMSEs of RemoTAP and GRASP are 0.285 and 0.224, respectively. Based on the AERONET comparison, we conclude that both algorithms show very similar overall performance, where both algorithms have stronger and weaker points. For the global data products, we find a root mean square difference (RMSD) between RemoTAP and GRASP AOD of 0.12 and 0.038 over land and ocean, respectively. The largest differences occur over the biomass burning region in equatorial Africa. The global mean values are virtually unbiased with respect to each other. For AE the RMSD between RemoTAP and GRASP is 0.33 over land and 0.23 over ocean. For SSA, we find much better agreement over land (bias = - 0.01, RMSD = 0.043 for retrievals with AOD > 0.2) than over ocean (bias = 0.053, RMSD = 0.074). As expected, the differences increase towards low AOD, over both land and ocean. We also compared the GRASP and RemoTAP AOD and AE products against MODIS. For AOD over land, the agreement of either GRASP or RemoTAP with MODIS is worse than the agreement between the two PARASOL algorithms themselves. Over ocean, the agreement is very similar among the three products for AOD. For AE, the agreement between GRASP and RemoTAP is much better than the agreement of both products with MODIS. The agreement of the latest product versions with each other and with AERONET improved significantly compared to the previous version of the global products of GRASP and RemoTAP. The results demonstrate that the dedicated effort in algorithm development for multi-angle polarimetric (MAP) aerosol retrievals still leads to substantial improvement of the resulting aerosol products, and this is still an ongoing process. [ABSTRACT FROM AUTHOR]

A framework to infer volume water fraction, soluble fraction and dry size distributions of fine-mode aerosol from multi-angle, multi-spectral polarimetry retrievals of column-averaged ambient aerosol properties is presented. The method is applied to observations of the Research Scanning Polarimeter (RSP) obtained during two NASA aircraft campaigns, namely the Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) and the Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP 2 Ex). All aerosol retrievals are statistically evaluated using in situ data. Volume water fraction is inferred from the retrieved ambient real part of the refractive index, assuming a dry refractive index of 1.54 and by applying a volume mixing rule to obtain the effective ambient refractive index. The uncertainties in inferred volume water fraction resulting from this simplified model are discussed and estimated to be lower than 0.2 and decreasing with increasing volume water fraction. The daily mean retrieved volume water fractions correlate well with the in situ values with a mean absolute difference of 0.09. Polarimeter-retrieved ambient effective radius for daily data is shown to increase as a function of volume water fraction as expected. Furthermore, the effective variance of the size distributions also increases with increasing effective radius, which we show is consistent with an external mixture of soluble and insoluble aerosol. The relative variations of effective radius and variance over an observation period are then used to estimate the soluble fraction of the aerosol. Daily results of soluble fraction correlate well with in situ-observed sulfate mass fraction with a correlation coefficient of 0.79. Subsequently, inferred water and soluble fractions are used to derive dry fine-mode size distributions from their ambient counterparts. While dry effective radii obtained in situ and from RSP show similar ranges, in situ values are generally substantially smaller during the ACTIVATE deployments, which may be due to biases in RSP retrievals or in the in situ observations, or both. Both RSP and in situ observations indicate the dominance of aerosol with low hygroscopicity during the ACTIVATE and CAMP 2 Ex campaigns. Furthermore, RSP indicates a high degree of external mixing of particles with low and high hygroscopicity. These retrievals of fine-mode water volume fraction and soluble fraction may be used for the evaluation of water uptake in atmospheric models. Furthermore, the framework allows us to estimate the variation in the concentration of fine-mode aerosol larger than a specific dry radius limit, which can be used as a proxy for the variation in cloud condensation nucleus concentrations. This framework may be applied to multi-angle, multi-spectral satellite data expected to be available in the near future. [ABSTRACT FROM AUTHOR]

The Directional Polarization Camera (DPC) onboard the Chinese Gaofen-5 satellite, launched in May 2018, has similar specifications as the POLDER-3 instrument. The SRON Remote Sensing of Trace gas and Aerosol Products (RemoTAP) full retrieval algorithm is applied to DPC measurements to retrieve aerosol properties including the total Aerosol Optical Depth (AOD), the fine/coarse mode AOD and the SSA (Single Scattering Albedo). Measurements of the global ground-based AERONET network between December 2019 and April 2020 have been used for the validation of the DPC retrievals. According to the average Fine Mode Fraction (FMF) of the selected AERONET stations, the stations are divided into urban stations (FMF ≥ 0.5) and dust stations (FMF < 0.5). For the total AOD validation, DPC retrievals show better performance over urban stations than over dust stations, with average biases of 0.055 and 0.106, and RMSEs of 0.151 and 0.228, respectively. Regarding the fine mode AOD, the retrieval also performs better over urban stations. Compared with the total AOD validation, both the relatively lower bias (0.021 and 0.065) and the higher Gfrac (Fraction of Good retrieval, 63.8% and 47.3%, respectively) further indicate that DPC performs better when fine mode aerosols dominate. For the land SSA validation, most of our SSA retrievals (~71%) show differences with AERONET SSA retrievals lower than 0.05. Case studies over fire spots and dust over northern China demonstrate the encouraging application potential of DPC aerosol products. The difference between fine and coarse AOD can provide more aerosol source information compared with the total AOD alone. Since the SSA retrievals are particularly sensitive to absorbing fine particles, they can be easily used in the tracking of biomass burning aerosol. [ABSTRACT FROM AUTHOR]

Atmospheric aerosols have been known to be a major source of uncertainties in CO2 concentrations retrieved from space. In this study, we investigate the added value of multi-angle polarimeter (MAP) measurements in the context of the Copernicus Anthropogenic Carbon Dioxide Monitoring (CO2M) mission. To this end, we compare aerosol-induced XCO2 errors from standard retrievals using a spectrometer only (without MAP) with those from retrievals using both MAP and a spectrometer. MAP observations are expected to provide information about aerosols that is useful for improving XCO2 accuracy. For the purpose of this work, we generate synthetic measurements for different atmospheric and geophysical scenes over land, based on which XCO2 retrieval errors are assessed. We show that the standard XCO2 retrieval approach that makes no use of auxiliary aerosol observations returns XCO2 errors with an overall bias of 1.12 ppm and a spread (defined as half of the 15.9–84.1 percentile range) of 2.07 ppm. The latter is far higher than the required XCO2 accuracy (0.5 ppm) and precision (0.7 ppm) of the CO2M mission. Moreover, these XCO2 errors exhibit a significantly larger bias and scatter at high aerosol optical depth, high aerosol altitude, and low solar zenith angle, which could lead to worse performance in retrieving XCO2 from polluted areas where CO2 and aerosols are co-emitted. We proceed to determine MAP instrument specifications in terms of wavelength range, number of viewing angles, and measurement uncertainties that are required to achieve XCO2 accuracy and precision targets of the mission. Two different MAP instrument concepts are considered in this analysis. We find that for either concept, MAP measurement uncertainties on radiance and degree of linear polarization should be no more than 3 % and 0.003, respectively. A retrieval exercise using MAP and spectrometer measurements of the synthetic scenes is carried out for each of the two MAP concepts. The resulting XCO2 errors have an overall bias of -0.004 ppm and a spread of 0.54 ppm for one concept, and a bias of 0.02 ppm and a spread of 0.52 ppm for the other concept. Both are compliant with the CO2M mission requirements; the very low bias is especially important for proper emission estimates. For the test ensemble, we find effectively no dependence of the XCO2 errors on aerosol optical depth, altitude of the aerosol layer, and solar zenith angle. These results indicate a major improvement in the retrieved XCO2 accuracy with respect to the standard retrieval approach, which could lead to a higher data yield, better global coverage, and a more comprehensive determination of CO2 sinks and sources. As such, this outcome underlines the contribution of, and therefore the need for, a MAP instrument aboard the CO2M mission. [ABSTRACT FROM AUTHOR]

Atmospheric aerosols have been known to be a major source of uncertainties in CO2 concentrations retrieved from space. In this study, we investigate the added value of multi-angle polarimeter (MAP) measurements in the context of the Copernicus candidate mission for anthropogenic CO2 monitoring (CO2M). To this end, we compare aerosol-induced XCO2 errors from standard retrievals using spectrometer only (without MAP) with those from retrievals using both MAP and spectrometer. MAP observations are expected to provide information about aerosols that is useful for improving XCO2 accuracy. For the purpose of this work, we generate synthetic measurements for different atmospheric and geophysical scenes over land, based on which XCO2 retrieval errors are assessed. We show that the standard XCO2 retrieval approach that makes no use of auxiliary aerosol observations returns XCO2 errors with an overall bias of 1.12 ppm, and a spread (defined as half of the 15.9th to the 84.1th percentile range) of 2.07 ppm. The latter is far higher than the required XCO2 accuracy (0.5 ppm) and precision (0.7 ppm) of the CO2M mission. Moreover, these XCO2 errors exhibit a significantly larger bias and scatter at high aerosol optical depth, high aerosol altitude, and low solar zenith angle, which could lead to a worse performance in retrieving XCO2 from polluted areas where CO2 and aerosols are co-emitted. We proceed to determine MAP instrument specifications in terms of wavelength range, number of viewing angles, and measurement uncertainties that are required to achieve XCO2 accuracy and precision targets of the mission. Two different MAP instrument concepts are considered in this analysis. We find that for either concept, MAP measurement uncertainties on radiance and degree of linear polarization should be no more than 3 % and 0.003, respectively. Adopting the derived MAP requirements, a retrieval exercise using both MAP and spectrometer measurements of the synthetic scenes delivers XCO2 errors with an overall bias of −0.004 ppm and a spread of 0.54 ppm, implying compliance with the CO2M mission requirements; the very low bias is especially important for proper emission estimates. For the test ensemble, we find effectively no dependence of the XCO2 errors on aerosol optical depth, altitude of the aerosol layer, and solar zenith angle. These results indicate a major improvement in the retrieved XCO2 accuracy with respect to the standard retrieval approach, which could lead to a higher data yield, better global coverage, and a more comprehensive determination of CO2 sinks and sources. As such, this outcome underlines the contribution of, and therefore the need for, a MAP instrument onboard the CO2M mission. [ABSTRACT FROM AUTHOR]

Atmospheric aerosols have been known to be a major source of uncertainties in CO2 concentrations retrieved from space. In this study, we investigate the added value of multi-angle polarimeter (MAP) measurements in the context of the Copernicus candidate mission for anthropogenic CO2 monitoring (CO2M). To this end, we compare aerosol-induced XCO2 errors from standard retrievals using spectrometer only (without MAP) with those from retrievals using both MAP and spectrometer. MAP observations are expected to provide information about aerosols that is useful for improving XCO2 accuracy. For the purpose of this work, we generate synthetic measurements for different atmospheric and geophysical scenes over land, based on which XCO2 retrieval errors are assessed. We show that the standard XCO2 retrieval approach that makes no use of auxiliary aerosol observations returns XCO2 errors with an overall bias of 1.12 ppm, and a spread (defined as half of the 15.9th to the 84.1th percentile range) of 2.07 ppm. The latter is far higher than the required XCO2 accuracy (0.5 ppm) and precision (0.7 ppm) of the CO2M mission. Moreover, these XCO2 errors exhibit a significantly larger bias and scatter at high aerosol optical depth, high aerosol altitude, and low solar zenith angle, which could lead to a worse performance in retrieving XCO2 from polluted areas where CO2 and aerosols are co-emitted. We proceed to determine MAP instrument specifications in terms of wavelength range, number of viewing angles, and measurement uncertainties that are required to achieve XCO2 accuracy and precision targets of the mission. Two different MAP instrument concepts are considered in this analysis. We find that for either concept, MAP measurement uncertainties on radiance and degree of linear polarization should be no more than 3 % and 0.003, respectively. Adopting the derived MAP requirements, a retrieval exercise using both MAP and spectrometer measurements of the synthetic scenes delivers XCO2 errors with an overall bias of -0.004 ppm and a spread of 0.54 ppm, implying compliance with the CO2M mission requirements; the very low bias is especially important for proper emission estimates. For the test ensemble, we find effectively no dependence of the XCO2 errors on aerosol optical depth, altitude of the aerosol layer, and solar zenith angle. These results indicate a major improvement in the retrieved XCO2 accuracy with respect to the standard retrieval approach, which could lead to a higher data yield, better global coverage, and a more comprehensive determination of CO2 sinks and sources. As such, this outcome underlines the contribution of, and therefore the need for, a MAP instrument onboard the CO2M mission. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Passive remote sensing of atmospheric carbon dioxide uses spectroscopic measurements of sunlight backscattered by the Earth's surface and atmosphere. The current state-of-the-art retrieval methods use three different spectral bands, the oxygen A band at 0.76 µ m and the weak and strong CO2 absorption bands at 1.61 and 2.06 µ m, respectively, to infer information on light scattering and the carbon dioxide column-averaged dry-air mole fraction XCO2. In this study, we propose a one-band XCO2 retrieval technique which uses only the 2.06 µ m band measurements from the Orbiting Carbon Observatory-2 (OCO-2) satellite. We examine the data quality by comparing the OCO-2 XCO2 with collocated ground-based measurements from the Total Carbon Column Observing Network (TCCON). Over land and ocean the OCO-2 one-band retrieval shows differences from TCCON observations with a standard deviation of ∼1.30 ppm and a station-to-station variability of ∼0.50 ppm. Moreover, we compare one-band and three-band retrievals over Europe, the Middle East, and Africa and see high correlation between the two retrievals with a SD of 0.93 ppm. Compared to the three-band retrievals, XCO2 retrievals using only the 2.06 µ m band have similar retrieval accuracy, precision, and data yield. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Passive remote sensing of atmospheric carbon dioxide uses spectroscopic measurements of sunlight back-scattered by the Earth's surface and atmosphere. The current state-of-the-art retrieval methods use three different spectral bands, the oxygen A band at 0.76 μm and the weak and strong CO2 absorption bands at 1.61 and 2.06 μm, respectively, to infer information on light scattering and the carbon dioxide column-averaged dry-air mole fraction XCO2. In this study, we propose a one-band XCO2 retrieval technique which uses only the 2.06 μm band measurements from the OCO-2 satellite. We examine the data quality by comparing the OCO-2 XCO2 with collocated ground based measurements from the Total Carbon Column Observing Network (TCCON). Over land and ocean the OCO-2 one-band retrieval shows differences to TCCON observations with a standard deviation of ~ 1.30 ppm and a station-to-station variability of ~ 0.50 ppm. Moreover, we compare one-band and three-band retrievals over Europe,the Middle East and Africa region and see high correlation between the two retrievals with a SD of 0.93 ppm. Compared to the three-band retrievals, using only the 2.06 μm band similar XCO2 retrieval accuracy and precision can be obtained while retaining a similar data yield. [ABSTRACT FROM AUTHOR]

Highlights • We introduce the SPEXone instrument for the NASA PACE mission. • We demonstrate that SPEXone has high capability for aerosol retrieval. • Polarimetric accuracy is the most important factor determining capability for aerosol retrieval. • Having many spectral measurements, it is sufficient to measure at a small number (5) of viewing angles for aerosol retrieval. • On the PACE mission, retrieval capability will be further enhanced by combining with HARP-2 and OCI. Abstract SPEXone is a Multi-Angle Polarimeter instrument that is baselined to fly on the NASA Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission, to be launched in 2022. It will perform hyper-spectral measurements of radiance and polarization in the spectral range 385–770 nm at 5 viewing angles (± 57 o , ± 20 o , 0 o ) with high accuracy (0.003) on the Degree of Linear Polarization (DoLP). Based on linear error analysis and retrievals on synthetic data, we conclude that SPEXone has the capability to significantly advance the accuracy of retrievals of optical and microphysical aerosol properties compared to past, present, and planned satellite instruments, as required for better quantification of the effect of aerosols on climate. The products that SPEXone will provide are Aerosol Optical Depth (AOD), Single Scattering Albedo (SSA), Aerosol Layer Height (ALH), effective radius, effective variance, complex refractive index, particle number column for both the fine and coarse mode as well as a shape parameter for the coarse mode. PACE will carry two other instrument: the Ocean Color Instrument (OCI) which is the main instrument and the Hyper-Angular Rainbow Polarimeter-2 (HARP-2). The synergistic use of SPEXone with these instruments will further increase retrieval accuracy, in particular for coarse mode parameters and absorption, and will provide unprecedented capability for aerosol above cloud retrievals. [ABSTRACT FROM AUTHOR]

This paper describes a neural network algorithm for the estimation of liquid water cloud optical properties from the Polarization and Directionality of Earth's Reflectances-3 (POLDER-3) instrument aboard the Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) satellite. The algorithm has been trained on synthetic multi-angle, multi-wavelength measurements of reflectance and polarization and has been applied to the processing of 1 year of POLDER-3 data. Comparisons of the retrieved cloud properties with Moderate Resolution Imaging Spectroradiometer (MODIS) products show that the neural network algorithm has a low bias of around 2 in cloud optical thickness (COT) and between 1 and 2 µ m in the cloud effective radius. Comparisons with existing POLDER-3 datasets suggest that the proposed scheme may have enhanced capabilities for cloud effective radius retrieval, at least over land. An additional feature of the presented algorithm is that it provides COT and effective radius retrievals at the native POLDER-3 Level 1B pixel level. [ABSTRACT FROM AUTHOR]

Polarimeter retrievals can provide detailed and accurate information on aerosol microphysical and optical properties. The SRON aerosol algorithm is one of the few retrieval approaches that can fully exploit this information. The algorithm core is a two-mode retrieval in which effective radius (reff), effective variance (veff), refractive index, and column number are retrieved for each mode; the fraction of spheres for the coarse mode and an aerosol layer height are also retrieved. Further, land and ocean properties are retrieved simultaneously with the aerosol properties. In this contribution, we extend the SRON aerosol algorithm by implementing a multimode approach in which each mode has fixed reff and veff. In this way the algorithm obtains more flexibility in describing the aerosol size distribution and avoids the high nonlinear dependence of the forward model on the aerosol size parameters. Conversely, the approach depends on the choice of the modes. We compare the performances of multimode retrievals (varying the number of modes from 2 to 10) with those based on the original (parametric) two-mode approach. Experiments with both synthetic measurements and real measurements (PARASOL satellite level-1 data of intensity and polarization) are conducted. The synthetic data experiments show that multimode retrievals are good alternatives to the parametric two-mode approach. It is found that for multimode approaches, with five modes the retrieval results can already be good for most parameters. The real data experiments (validated with AERONET data) show that, for the aerosol optical thickness (AOT), multimode approaches achieve higher accuracy than the parametric two-mode approach. For single scattering albedo (SSA), both approaches have similar performances. [ABSTRACT FROM AUTHOR]

This paper describes a neural network algorithm for the estimation of liquid water cloud optical properties from the Polarization and Directionality of Earth's Reflectances-3 (POLDER-3) instrument, on board the Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) satellite. The algorithm has been trained on synthetic multi-angle, multi-wavelength measurements of reflectance and polarization, and has been applied to the processing of one year of POLDER-3 data. Comparisons of the retrieved cloud properties with Moderate resolution Imaging Spectroradiometer (MODIS) products show negative biases around −2 in retrieved cloud optical thicknesses (COTs) and between −1 and −2μm in retrieved cloud effective radii. Comparisons with existing POLDER-3 datasets suggest that the proposed scheme may have enhanced capabilities for cloud effective radius retrieval at least over land. An additional feature of the presented algorithm is that it provides COT and effective radius retrievals at the native POLDER-3 Level 1B pixel level. [ABSTRACT FROM AUTHOR]

Polarimeter retrievals can provide detailed and accurate information on aerosol microphysical and optical properties. The SRON-Aerosol algorithm is one of the few retrieval approaches that can fully exploit this information. The algorithm core is a 2-mode retrieval where effective radius (reff), effective variance (veff), refractive index, number column are retrieved for each mode, the fraction of spheres for the coarse mode, and an aerosol layer height. Further, land and ocean properties are retrieved simultaneously with the aerosol properties. In this contribution, we extend the SRON-Aerosol algorithm by implementing a multi-mode approach where each mode has fixed reff and veff. In this way the algorithm gets more flexibility in describing the aerosol size distribution and avoids the high non-linear dependence of the forward model on the aerosol size parameters. On the other hand, the approach depends on the choice of the modes used in the multi-mode approach. We compare the performances of multi-mode retrievals (varying mode number from 2 to 10) with those based on the original (parametric) 2-mode approach. Experiments with both synthetic measurements and real measurements (PARASOL satellite level-1 data of intensity and polarization) are conducted. The synthetic data experiments show that multi-mode retrievals are good alternatives to the parametric 2-mode approach. It is found that for multi-mode approaches, with a small number of modes the retrieval results can be already good for most parameters. The real data experiments (validated with AERONET data) show that, for the Aerosol Optical Thickness (AOT), multi-mode approaches achieve higher accuracy than the parametric 2-mode approach. A tendency is found that the accuracy gets better as the mode number increases. For Single Scattering Albedo (SSA), both approaches have similar performances. [ABSTRACT FROM AUTHOR]

In this study we present the retrieval of the column-averaged dry air mole fraction of carbon dioxide (XCO2) from the Orbiting Carbon Observatory-2 (OCO-2) satellite observations using the RemoTeC algorithm, previously successfully applied to retrieval of greenhouse gas concentration from the Greenhouse Gases Observing Satellite (GOSAT). The XCO2 product has been validated with collocated ground-based measurements from the Total Carbon Column Observing Network (TCCON) for almost 2 years of OCO-2 data from September 2014 to July 2016. We found that fitting an additive radiometric offset in all three spectral bands of OCO-2 significantly improved the retrieval. Based on a small correlation of the XCO2 error over land with goodness of fit, we applied an a posteriori bias correction to our OCO-2 retrievals. In overpass averaged results, XCO2 retrievals have an SD of ~1:30 ppm and a station-to-station variability of ~0:40 ppm among collocated TCCON sites. The seasonal relative accuracy (SRA) has a value of 0.52 ppm. The validation shows relatively larger difference with TCCON over high-latitude areas and some specific regions like Japan. [ABSTRACT FROM AUTHOR]

In previous work, we derived the adjoint method as a computationally efficient path to three-dimensional (3D) retrievals of clouds and aerosols. In this paper we will demonstrate the use of adjoint methods for retrieving two-dimensional (2D) fields of cloud extinction. The demonstration uses a new 2D radiative transfer solver (FSDOM). This radiation code was augmented with adjoint methods to allow efficient derivative calculations needed to retrieve cloud and surface properties from multi-angle reflectance measurements. The code was then used in three synthetic retrieval studies. Our retrieval algorithm adjusts the cloud extinction field and surface albedo to minimize the measurement misfit function with a gradient-based, quasi-Newton approach. At each step we compute the value of the misfit function and its gradient with two calls to the solver FSDOM. First we solve the forward radiative transfer equation to compute the residual misfit with measurements, and second we solve the adjoint radiative transfer equation to compute the gradient of the misfit function with respect to all unknowns. The synthetic retrieval studies verify that adjoint methods are scalable to retrieval problems with many measurements and unknowns. We can retrieve the vertically-integrated optical depth of moderately thick clouds as a function of the horizontal coordinate. It is also possible to retrieve the vertical profile of clouds that are separated by clear regions. The vertical profile retrievals improve for smaller cloud fractions. This leads to the conclusion that cloud edges actually increase the amount of information that is available for retrieving the vertical profile of clouds. However, to exploit this information one must retrieve the horizontally heterogeneous cloud properties with a 2D (or 3D) model. This prototype shows that adjoint methods can efficiently compute the gradient of the misfit function. This work paves the way for the application of similar methods to 3D remote sensing problems. [ABSTRACT FROM AUTHOR]

In this paper, an algorithm for the retrieval of aerosol and land surface properties from airborne spectropolarimetric measurements - combining neural networks and an iterative scheme based on Phillips-Tikhonov regularization - is described. The algorithm - which is an extension of a scheme previously designed for ground-based retrievals - is applied to measurements from the Research Scanning Polarimeter (RSP) on board the NASA ER-2 aircraft. A neural network, trained on a large data set of synthetic measurements, is applied to perform aerosol retrievals from real RSP data, and the neural network retrievals are subsequently used as a first guess for the Phillips-Tikhonov retrieval. The resulting algorithm appears capable of accurately retrieving aerosol optical thickness, fine-mode effective radius and aerosol layer height from RSP data. Among the advantages of using a neural network as initial guess for an iterative algorithm are a decrease in processing time and an increase in the number of converging retrievals. [ABSTRACT FROM AUTHOR]

In order to conduct accurate aerosol retrieval over snow, the Remote Sensing of Trace Gases and Aerosol Products (RemoTAP) algorithm developed by SRON Netherlands Institute for Space Research is extended with a bi-directional reflection distribution function (BRDF) for snow surfaces. The capability of the extended algorithm is validated with both synthetic measurements and real satellite measurements from the Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL), and a comparison has been made to retrievals with the baseline RemoTAP (without a snow kernel). For retrievals with real PARASOL observations, we use pixels over Aerosol Robotic Network (AERONET) stations for validation and we use the MODIS snow cover products to identify pixels over snow. We evaluate the retrieved aerosol optical thickness (AOT) at 550 nm (τ550) , single-scattering albedo (SSA) at 550 nm (ω550) and Ångström exponent (AE) for 440–870 nm (AE440-870). The experiments with both synthetic and real data show that the extended RemoTAP maintains capability for snow-free pixels and has obvious advantages in accuracy and the fraction of successful retrievals for retrieval over snow, especially over surfaces with snow cover >75 %. According to the real-data experiment, we find that the retrieval algorithm has difficulty in fitting the PARASOL 1020 nm band, where snow reflectance is significantly lower than that for the visible bands. When we perform a four-band retrieval (490, 565, 670, 865 nm) with the extended RemoTAP, we obtain a good retrieval result for τ550 , ω550 and AE440-870. Therefore, the four-band retrieval with the extended RemoTAP is recommended for aerosol retrieval over snow. [ABSTRACT FROM AUTHOR]

In this paper, an algorithm for the retrieval of aerosol and land surface properties from airborne spectropolarimetric measurements -- combining neural networks and an iterative scheme based on Phillips-Tikhonov regularization -- is described. The algorithm -- which is an extension of a scheme previously designed for ground-based retrievals -- is applied to measurements from the Research Scanning Polarimeter (RSP) onboard the NASA ER-2 aircraft. A neural network, trained on a large dataset of synthetic measurements, is applied to perform aerosol retrievals from real RSP data, and the neural network retrievals are subsequently used as first guess for the Phillips-Tikhonov retrieval. The resulting algorithm appears capable of accurately retrieving aerosol optical thickness, fine mode effective radius and aerosol layer height from RSP data. Among the advantages of using a neural network as initial guess for an iterative algorithm are a decrease in processing time and an increase in the number of converging retrievals. [ABSTRACT FROM AUTHOR]

This work presents the operational methane retrieval algorithm for the Sentinel 5 Precursor (S5P) satellite and its performance tested on realistic ensembles of simulated measurements. The target product is the column-averaged dry air volume mixing ratio of methane (XCH4), which will be retrieved simultaneously with scattering properties of the atmosphere. The algorithm attempts to fit spectra observed by the shortwave and near-infrared channels of the TROPOspheric Monitoring Instrument (TROPOMI) spectrometer aboard S5P. The sensitivity of the retrieval performance to atmospheric scattering properties, atmospheric input data and instrument calibration errors is evaluated. In addition, we investigate the effect of inhomogeneous slit illumination on the instrument spectral response function. Finally, we discuss the cloud filters to be used operationally and as backup. We show that the required accuracy and precision of  < 1 % for the XCH4 product are met for clear-sky measurements over land surfaces and after appropriate filtering of difficult scenes. The algorithm is very stable, having a convergence rate of 99 %. The forward model error is less than 1 % for about 95 % of the valid retrievals. Model errors in the input profile of water do not influence the retrieval outcome noticeably. The methane product is expected to meet the requirements if errors in input profiles of pressure and temperature remain below 0.3 % and 2 K, respectively. We further find that, of all instrument calibration errors investigated here, our retrievals are the most sensitive to an error in the instrument spectral response function of the shortwave infrared channel. [ABSTRACT FROM AUTHOR]

Abstract: For land surfaces and atmospheric aerosol characterization on the basis of satellite and airborne measurements models for surface reflection description are required. At present time for visible and infrared spectral regions semi-empirical model for surface reflection are usually used for these purposes. Due to the lack of physical basis, these models introduce a lot of uncertainties into the problem of aerosol and surface properties retrieval. In this paper we consider the possibility of using physically based models for bidirectional reflection matrix (BRM) which can be applied to the problem of simultaneous retrieval of aerosol and land surface properties. The physical model for the BRM is derived from the general solution of the electromagnetic scattering problems by random media. The equation for the reflection matrix is obtained within the far-field approximation when the ladder scattering diagrams are taken into account. To perform analytical averaging over orientation of the surface elements we assume that at different scales the surface can be considered as the Gaussian surface. We use multi-angle photopolarimetric airborne measurements of the Research Scanning Polarimeter (RSP) and satellite POLDER (Polarization and Directionality of the Earth''s Reflectances) measurements to investigate the performance of the presented BRM model. The results of the comparison are discussed. [Copyright &y& Elsevier]

Abstract: In this paper, we investigate the surface–atmosphere radiative interaction in application to the problem of aerosol satellite remote sensing over land. First, we test different models of the Bidirectional Reflectance and Polarization Distribution Function (BRDF and BPDF) for bare soil and vegetation surfaces using multi-angle, multi-spectral photopolarimetric airborne measurements of the Research Scanning Polarimeter (RSP). Then, we investigate the performance of different models of BRDF and BPDF for modeling top-of-atmosphere measurements. We have found that different BRDF models can describe the RSP measurements equally well. However, for soil surfaces, the different BRDF models show a different dependence on illumination geometry (solar zenith and azimuth angles), as well as a different dependence on viewing angle outside the range of RSP measurements. This implies that different models describe the surface–atmosphere interaction differently, leading for soil surfaces to differences in the top-of-atmosphere reflectance up to 4–5%, whereas at surface level the models agree within 2% for RSP illumination and measurement geometry. For vegetation, the different BRDF models show more similar dependence on illumination geometry, meaning that, in general, the differences in top-of-atmosphere reflectances are smaller than the differences in surface total reflectances. For the BPDF, we compare the empirical model of Nadal and Breon (1999) and the model developed by Maignan et al. (2009) with a newly developed model. The latter model compares better with RSP measurements. It was shown that, though all models have essentially different angular profiles at different illumination and viewing geometries, the difference of the top-of-atmosphere degree of linear polarization is less or is of the same order as the degree of linear polarization difference at the surface level taken at RSP illumination and measurement geometry. For the considered models, it can be up to 0.015 but is mostly below 0.005. [Copyright &y& Elsevier]

Abstract: The reflection properties of soil and vegetation surfaces have been investigated using airborne photopolarimetric data from the research scanning polarimeter (RSP). For both surface types, it was found that the ratios of total reflectances taken at two different wavelengths from visible and short-wave infrared channels are the same for different illumination and scattering geometries, and in general independent of the scattering angle. From an analysis of the angular and spectral dependencies of the intensity and polarization, we show that the modeled total and polarized reflectances can be expressed in the same form both for soil and vegetation surfaces, namely, as the product of geometrical scattering term depending only on illumination and viewing angles, and a term that varies solely with wavelength and scattering angle. [Copyright &y& Elsevier]

The atmospheric chemistry instrument SCIAMACHY is a joint German-Dutch-Belgian contribution to the European Space Agency environmental satellite ENVISAT scheduled for launch in mid 2001. Instrument performance and calibration measurements recently carried out under thermal vacuum conditions have confirmed predicted performance. Algorithm development at SRON using the UV and visible part of the solar spectrum for ozone profile retrieval and the near infrared for carbon monoxide and methane retrieval has shown good progress. In particular, new avenues have been explored for the development of a fast operational algorithm for the retrieval of ozone profiles, including a new forward model based on perturbation theory that is faster than the current operational algorithm, coupled to a new inversion scheme with no use of a-priori ozone profile information. The near-infrared retrieval of CO and CH4 is expected to yield higher accuracy data products based on the optimization of retrieval using micro-windows. New and advanced SCIAMACHY data products will be piloted in the Netherlands SCIAMACHY Data Centre that complements the operational ENVISAT payload data segment. [Copyright &y& Elsevier]

This paper describes a neural network cloud masking scheme from PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) multi-angle polarimetric measurements. The algorithm has been trained on synthetic measurements and has been applied to the processing of 1 year of PARASOL data. Comparisons of the retrieved cloud fraction with MODIS (Moderate Resolution Imaging Spectroradiometer) products show overall agreement in spatial and temporal patterns, but the PARASOL neural network (PARASOL-NN) retrieves lower cloud fractions. Comparisons with a goodness-of-fit mask from aerosol retrievals suggest that the NN cloud mask flags fewer clear pixels as cloudy than MODIS (∼ 3 % of the clear pixels versus ∼ 15 % by MODIS). On the other hand the NN classifies more pixels incorrectly as clear than MODIS (∼ 20 % by NN, versus ∼ 15 % by MODIS). Additionally, the NN and MODIS cloud mask have been applied to the aerosol retrievals from PARASOL using the Remote Sensing of Trace Gas and Aerosol Products (RemoTAP) algorithm. Validation with AERONET shows that the NN cloud mask performs comparably with MODIS in screening residual cloud contamination in retrieved aerosol properties. Our study demonstrates that cloud masking from multi-angle polarimeter (MAP) aerosol retrievals can be performed based on the MAP measurements themselves, making the retrievals independent of the availability of a cloud imager. [ABSTRACT FROM AUTHOR]

In this study, we present aerosol level-2 retrieval results from ACEPOL (Aerosol Characterization from Polarimeter and Lidar) campaign, which was a joint initiative between NASA and SRON (NL) and took place October-November 2017 over the western part of the United States. We perform aerosol retrievals using the SRON multimode algorithm on SPEX airborne L1C data, the Research Scanning Polarimeter (RSP) L1 data, and the Airborne Multi-angle SpectroPolarimeter Imager (AirMSPI) L1B2 data. The AERONET data and the High Spectral Resolution Lidar-2 (HSRL2) data are used to validate polarimeter retrieved/diagnostics aerosol properties. The polarimeter retrievals are based on SRON 5-mode approaches. Other multimode (from 6 to 10 modes) tests show similar performances with the 5-mode approach.Validations with AERONET show that SPEX, RSP, and AirMSPI retrievals achieve similar performances on aerosol optical thickness (AOT) and they agree well with AERONET. Here, we also show that AirMSPI-AERONET based on the SRON algorithm compares well with that based on the JPL algorithm. The Angstrom Exponent is compared between SPEX-AERONET and RSP-AERONET when AOT>0.1. Validations with HSRL2 are performed on two days. One day with low aerosol (AOD in range 0.02-0.14) and one day with measurements of an inhomogeneous smoke plume with high AOD (including AOD values > 1.0). Considering the data collocations among instruments, we compare SPEX-HSRL2, RSP-HSRL2, and SPEX-RSP for these two cases. SPEX and RSP both compare well to HSRL2 for AOT, backscatter coefficients, and depolarization ratio. [ABSTRACT FROM AUTHOR]

There is growing consensus in the aerosol remote sensing community that Multi-Angle Photo-polarimetric (MAP) measurements are essential to unambiguously determine all aerosol properties relevant to quantify and understand the impact of aerosols on climate and air quality. This presentation will discuss the advanced algorithm development for aerosol and cloud retrieval from MAP instruments. This includes aerosol and cloud retrieval from the PARASOL mission (2004-2013) and its validation, estimation the Direct Radiative Effect of Aerosols (DREA) using PARASOL and other A-Train sensors, use of Neural Network techniques for cloud retrievals, and aerosol retrieval from airborne MAP instruments. Further, we will discuss the development of the SPEXone instrument for the NASA PACE mission (launch 2022) and its aerosol retrieval capabilities. SPEXone is a high accuracy MAP with 5 viewing angles with hyperspectral capability, that is expected to provide aerosol properties with unprecedented detail and accuracy. On the PACE mission SPEXone will be used in synergy with the Hyper-Angular Rainbow Polarimeter-2 (HARP-2) and the Ocean Color Instrument (OCI). [ABSTRACT FROM AUTHOR]

Atmospheric aerosols affect the Earth's climate in many ways, including acting as the seeds on which cloud droplets form. Since a large fraction of these particles is anthropogenic, the clouds' microphysical and radiative characteristics are influenced by human activity on a global scale leading to important climatic effects. The respective change in the energy budget at the top of the atmosphere is defined as the effective radiative forcing due to aerosol‐cloud interaction (ERFaci). It is estimated that the ERFaci offsets presently nearly 1/4 of the greenhouse‐induced warming, but the uncertainty is within a factor of two. A common method to calculate the ERFaci is by the multiplication of the susceptibility of the cloud radiative effect to changes in aerosols by the anthropogenic change of the aerosol concentration. This has to be done by integrating it over all cloud regimes. Here we review the various methods of the ERFaci estimation. Global measurements require satellites' global coverage. The challenge of quantifying aerosol amounts in cloudy atmospheres are met with the rapid development of novel methodologies reviewed here. The aerosol characteristics can be retrieved from space based on their optical properties, including polarization. The concentrations of the aerosols that serve as cloud drop condensation nuclei can be also estimated from their impact on the satellite‐retrieved cloud drop number concentrations. These observations are critical for reducing the uncertainty in the ERFaci calculated from global climate models (GCMs), but further development is required to allow GCMs to properly simulate and benefit these novel observables. Plain Language Summary: Aerosols affect the climate in many ways, including serving as the basis for the formation of cloud droplets. Therefore, aerosols have profound impacts on clouds and through that on the Earth's energy budget. Increasing aerosols leads to additional cloud droplets, changing cloud properties such that they reflect more solar radiation back to space and offset nearly 1/4 of the warming induced by greenhouse gases, but with a large uncertainty within a factor of two. Here we review the ways by which aerosols and their radiative effects are retrieved from space. A major challenge is to reduce the uncertainty by better retrieval methods of atmospheric aerosol and cloud properties. This challenge is met by rapid satellite retrieval methodological developments and numerous new satellite missions, which are described here. These new methodologies have to be matched with parallel development in the global circulation models for the improved estimation of the actual climatic impacts. Key Points: Calculating the susceptibility of clouds to aerosols has to include the cleanest conditions where measuring the aerosols is challengingThe definition and use of regimes to group clouds with similar responses to aerosol is vital for future observation‐based effective radiative forcing due to aerosol‐cloud interaction (ERFaci) estimatesNew observational tools are vital for reducing ERFaci uncertainty but require further global climate model development to simulate these novel observables [ABSTRACT FROM AUTHOR]

The University of Maryland, Baltimore County (UMBC) Hyper-Angular Rainbow Polarimeter (HARP2) will be on board NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in January 2024. In this study we systematically evaluate the retrievability and uncertainty of aerosol and ocean parameters from HARP2 multi-angle polarimeter (MAP) measurements. To reduce the computational demand of MAP-based retrievals and maximize data processing throughput, we developed improved neural network (NN) forward models for spaceborne HARP2 measurements over a coupled atmosphere and ocean system within the FastMAPOL retrieval algorithm. To this end, a cascading retrieval scheme is implemented in FastMAPOL, which leverages a series of NN models of varying size, speed, and accuracy to optimize performance. Two sets of NN models are used for reflectance and polarization, respectively. A full day of global synthetic HARP2 data was generated and used to test various retrieval parameters including aerosol microphysical and optical properties, aerosol layer height, ocean surface wind speed, and ocean chlorophyll a concentration. To assess retrieval quality, pixel-wise retrieval uncertainties were derived from error propagation and evaluated against the difference between the retrieval parameters and truth based on a Monte Carlo method. We found that the fine-mode aerosol properties can be retrieved well from the HARP2 data, though the coarse-mode aerosol properties are more uncertain. Larger uncertainties are associated with a reduced number of available viewing angles, which typically occur near the scan edge of the HARP2 instrument. Results of the performance assessment demonstrate that the algorithm is a viable approach for operational application to HARP2 data after the PACE launch. [ABSTRACT FROM AUTHOR]

This paper describes a neural network cloud masking scheme from PARASOL (Polarisation and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) Multi-Angle Polarimetric measurements. The algorithm has been trained on synthetic measurements and has been applied to the processing of one year of PARASOL data. Comparisons of the retrieved cloud fraction with MODIS (Moderate Resolution Imaging Spectroradiometer) products show overall agreement in spatial and temporal patterns but the PARASOL-NN retrieves lower cloud fractions. Comparisons with a goodness-of-fit mask from aerosol retrievals suggest that the NN cloud mask flags less clear pixels as cloudy than MODIS (∼ 3% of the clear pixels, versus ∼ 15% by MODIS). On the other hand the NN classifies more pixels incorrectly as clear than MODIS (∼ 19% by NN, versus ∼ 15% by MODIS). Additionally, the NN and MODIS cloud mask have been applied to the aerosol retrievals from PARASOL using the Remote Sensing of Trace Gas and Aerosol Products (RemoTAP) algorithm. Validation with AERONET shows that the NN cloud mask performs comparably with MODIS in screening residual cloud contamination in retrieved aerosol properties. Our study demonstrates that cloud masking from MAP aerosol retrievals can be performed based on the MAP measurements themselves, making the retrievals independent of the availability of a cloud imager. [ABSTRACT FROM AUTHOR]

The TROPOspheric Monitoring Instrument (TROPOMI), due to its wide swath, performs observations over the ocean in every orbit, enhancing the monitoring capabilities of methane from space. In the short-wave–infrared (SWIR) spectral band ocean surfaces are dark except for the specific sun glint geometry, for which the specular reflectance detected by the satellite provides a signal that is high enough to retrieve methane with high accuracy and precision. In this study, we build upon the RemoTeC full-physics retrieval algorithm for land measurements, and we retrieve 4 years of methane concentrations over the ocean from TROPOMI. We fully assess the quality of the dataset by performing a validation using ground-based measurements of the Total Carbon Column Observing Network (TCCON) from near-ocean sites. The validation results in an agreement of -0.5±0.3 % (-8.4±6.3 ppb) for the mean bias and station-to-station variability, which show that glint measurements comply with the mission requirement of precision and accuracy below 1 %. Comparison to ocean measurements from the Greenhouse gases Observing SATellite (GOSAT) results in a bias of -0.2±0.9 % (-4.4±15.7 ppb), equivalent to the comparison of measurements over land. The full-physics algorithm simultaneously retrieves the amount of atmospheric methane and the physical scattering properties of the atmosphere from measurements in the near-infrared (NIR) and SWIR spectral bands. Based on the scattering properties of the atmosphere and ocean surface reflection we further validate retrievals over the ocean. Using the "upper-edge" method, we identify a set of ocean glint observations where scattering by aerosols and clouds can be ignored in the measurement simulation to investigate other possible error sources such as instrumental errors, radiometric inaccuracies or uncertainties related to spectroscopic absorption cross-sections. With this ensemble we evaluate the RemoTeC forward model via the validation of the total atmospheric oxygen (O 2) column retrieved from the O 2 A-band, as well as the consistency of XCH4 retrievals using sub-bands from the SWIR band, which show a consistency within 1 %. We discard any instrumental and radiometric errors by a calibration of the O 2 absorption line strengths as suggested in the literature. [ABSTRACT FROM AUTHOR]

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 the nonlinear dependence of the forward model on the retrieved parameters 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 efficient 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 uncertainty evaluation technique 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, as well as guide MAP algorithm development for current and future satellite systems such as NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. [ABSTRACT FROM AUTHOR]

Several single-platform satellite missions have been designed during the past decades in order to retrieve the atmospheric concentrations of anthropogenic greenhouse gases (GHG), initiating worldwide efforts towards better monitoring of their sources and sinks. To set up a future operational system for anthropogenic GHG emission monitoring, both revisit frequency and spatial resolution need to be improved. The Space Carbon Observatory (SCARBO) project aims at significantly increasing the revisit frequency of spaceborne GHG measurements, while reaching state-of-the-art precision requirements, by implementing a concept of small satellite constellation. It would accommodate a miniaturised GHG sensor named NanoCarb coupled with an aerosol instrument, the multi-angle polarimeter SPEXone. More specifically, the NanoCarb sensor is a static Fabry–Pérot imaging interferometer with a 2.3×2.3 km 2 spatial resolution and 200 km swath. It samples a truncated interferogram at optical path differences (OPDs) optimally sensitive to all the geophysical parameters necessary to retrieve column-averaged dry-air mole fractions of CO 2 and CH 4 (hereafter XCO2 and XCH4). In this work, we present the Level 2 performance assessment of the concept proposed in the SCARBO project. We perform inverse radiative transfer to retrieve XCO2 and XCH4 directly from synthetic NanoCarb truncated interferograms and provide their systematic and random errors, column vertical sensitivities, and degrees of freedom as a function of five scattering-error-critical atmospheric and observational parameters. We show that NanoCarb XCO2 and XCH4 systematic retrieval errors can be greatly reduced with SPEXone posterior outputs used as improved prior aerosol constraints. For two-thirds of the soundings, located at the centre of the 200 km NanoCarb swath, XCO2 and XCH4 random errors span 0.5–1 ppm and 4–6 ppb, respectively, compliant with their respective 1 ppm and 6 ppb precision objectives. Finally, these Level 2 performance results are parameterised as a function of the explored scattering-error-critical atmospheric and observational parameters in order to time-efficiently compute extensive L2 error maps for future CO 2 and CH 4 flux estimation performance studies. [ABSTRACT FROM AUTHOR]

We present a top-down approach for aerosol emission estimation from Spectropolarimeter for Planetary Exploration (SPEXone) polarimetric retrievals related to the aerosol amount, size, and absorption using a fixed-lag ensemble Kalman smoother (LETKS) in combination with the ECHAM-HAM model. We assess the system by performing observing system simulation experiments (OSSEs) in order to evaluate the ability of the future multi-angle polarimeter instrument, SPEXone, as well as a satellite with near-perfect global coverage. In our OSSEs, the nature run (NAT) is a simulation by the global climate aerosol model ECHAM-HAM with altered aerosol emissions. The control (CTL) and the data assimilation (DAS) experiments are composed of an ensemble of ECHAM-HAM simulations, where the default aerosol emissions are perturbed with factors taken from a Gaussian distribution. Synthetic observations, specifically aerosol optical depth at 550 nm (AOD 550), Ångström exponent from 550 to 865 nm (AE 550–865), and single-scattering albedo at 550 nm (SSA 550) are assimilated in order to estimate the aerosol emission fluxes of desert dust (DU), sea salt (SS), organic carbon (OC), black carbon (BC), and sulfate (SO 4), along with the emission fluxes of two SO 4 precursor gases (SO 2 , DMS). The prior emission global relative mean absolute error (MAE) before the assimilation ranges from 33 % to 117 %. Depending on the species, the assimilated observations sampled using the satellite with near-perfect global coverage reduce this error to equal to or lower than 5 %. Despite its limited coverage, the SPEXone sampling shows similar results, with somewhat larger errors for DU and SS (both having a MAE equal to 11 %). Further, experiments show that doubling the measurement error increases the global relative MAE up to 22 % for DU and SS. In addition, our results reveal that when the wind of DAS uses a different reanalysis dataset (ERA5 instead of ERA-Interim) to the NAT, the estimated SS emissions are negatively affected the most, while other aerosol species are negatively affected to a smaller extent. If the DAS uses dust or sea salt emission parametrizations that are very different from the NAT, posterior emissions can still be successfully estimated, but this experiment revealed that the source location is important for the estimation of dust emissions. This work suggests that the upcoming SPEXone sensor will provide observations related to aerosol amount, size, and absorption with sufficient coverage and accuracy in order to estimate aerosol emissions. [ABSTRACT FROM AUTHOR]

The TROPOspheric Monitoring Instrument (TROPOMI) due to its wide swath performs observations over the ocean in every orbit, enhancing the monitoring capabilities of methane from space. In the short-wave infrarred (SWIR) spectral band ocean surfaces are dark except for the specific sun-glint geometry, for which the specular reflectance detected by the satellite provides a signal that is high enough to retrieve methane with high accuracy and precision. In this study, we build upon the RemoTeC full-physics retrieval algorithm for land measurements and we retrieve four years of methane concentrations over the ocean from TROPOMI. We fully assess the quality of the dataset by performing a validation using ground-based measurements of the Total Carbon Column Observing Network (TCCON) from near-ocean sites. The validation results in an agreement of -0.5 ± 0.3% (-8.4 ± 6.3 ppb) for the mean bias and station-to-station variability, which show that glint measurements comply with the mission requirement of precision and accuracy below 1 %. Comparison to ocean measurements from the Greenhouse gases Observing SATellite (GOSAT) satellite results in a bias of -0.2 ± 0.9% (-4.4 ± 15.7 ppb), equivalent to the comparison of measurements over land. The full-physics algorithm simultaneously retrieves the amount of atmospheric methane and the physical scattering properties of the atmosphere from measurements in the near-infrarred (NIR) and SWIR spectral bands. Based on the backscattering properties of the atmosphere and ocean surface reflection we further validate retrievals over the ocean. Using the "upper-edge" method, we identify a set of ocean glint observations where scattering by aerosols and clouds can be ignored in the measurement simulation to investigate other possible error sources such as instrumental errors, radiometric inaccuracies or uncertainties related to spectroscopic absorption cross-sections. With this ensemble we evaluate the RemoTeC forward model via the validation of the total atmospheric oxygen (O2) column retrieved from the O2-A band, as well as the consistency of XCH4 retrievals using sub-bands from the SWIR band, which show a consistency within 1%. We discard any instrumental and radiometric errors by a calibration of the O2 absorption line strengths as suggested in the literature. [ABSTRACT FROM AUTHOR]

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]

We present a linearization of pseudo-spherical vector radiative transfer using the forward-adjoint perturbation theory. The method, which is based on the usual forward and the adjoint formulation of radiative transfer, is extended to radiative transfer problems with internal radiation sources. This allows one to linearize vector radiative transfer in pseudo-spherical approximation. The model PS-LIRA is developed based on this approach. Here, the calculation of the direct beam as well as the integration along the line of sight are performed for a full spherical geometry. The multiply scattered radiation is calculated in a plane-parallel atmosphere. The proposed model provides the intensity vector as well as its derivatives at the top of the atmosphere for solar zenith angles (SZA) up to 90.0° and for off-nadir viewing geometries ranging from limb to limb. In its scalar reduction the developed radiative transfer model is verified with a reference model and shows an accuracy of better than 0.005% in the calculation of the first Stokes parameter. Further, the calculated derivatives are verified with a finite difference method. The comparison shows deviations of less than 0.25% in the calculation of the derivatives. PS-LIRA is then used for a retrieval of atmospheric ozone profiles at large SZA. Subsequently the effect of a pseudo-spherical atmosphere on the retrieval of ozone profiles is investigated for large SZA and large viewing zenith angles. [Copyright &y& Elsevier]

We present a top-down approach for aerosol emission estimation from SPEXone polarimetric retrievals related to the aerosol amount, size, and absorption using a fixed-lag ensemble Kalman smoother (LETKS) in combination with the ECHAM-HAM model. We assess the system by performing Observing System Simulation Experiments (OSSEs), in order to evaluate the ability of the future multi-angle polarimeter instrument, SPEXone, as well as a satellite with near perfect global coverage. In our OSSEs, the Nature Run (NAT) is a simulation by the global climate aerosol model ECHAM-HAM with altered aerosol emissions. The Control (CTL) and the data assimilation (DAS) experiments are composed of an ensemble of ECHAM-HAM simulations, where the default aerosol emissions are perturbed with factors taken from a Gaussian distribution. Synthetic observations, specifically Aerosol Optical Depth at 550 nm (AOD550), Angstrom Exponent from 550 nm to 865 nm (AE550-865) and Single Scattering Albedo at 550 nm (SSA550) are assimilated in order to estimate the aerosol emission fluxes of desert dust (DU), sea salt (SS), organic carbon (OC), black carbon (BC) and sulphate (SO4), along with the emission fluxes of two SO4 precursor gases (SO2, DMS). The synthetic observations are sampled from the NAT according to two satellite observing systems, with different spatial coverages. The first is the sensor SPEXone, a hyperspectral multi-angle polarimeter with a narrow swath (~100 km), that will be a part of the NASA PACE mission. The second is an idealized sensor that can retrieve observations over the whole globe even under cloudy conditions. The prior emission global relative Mean Absolute Error (MAE) before the assimilation ranges from 33 % to 117 %. Depending on the species, the assimilated observations sampled using the idealized sensor, reduce this error to equal to or lower than 5 %. Despite its limited coverage, the SPEXone sampling bares similar results, with somewhat larger errors for DU and SS (both having a MAE equal to 11 %). Further, experiments show that doubling the measurement error, increases the global relative MAE to 22 % for DU and SS. The emission estimation of the other species is not affected as much by these changes. In addition, the role of biased meteorology on emission estimation was quantified by using two different datasets (ERA-5 and ERA-interim) to nudge the U and V wind components of the model. The results reveal that when the wind of DAS uses a different reanalysis dataset than the NAT the estimated SS emissions are negatively affected the most, while the estimated emissions of DU, OC, BC and SO2 are negatively affected to a smaller extent. If the DAS uses dust or sea salt emission parametrisations that are very different from the NAT, posterior emissions can still be successfully estimated but this experiment revealed that the source location is important for the estimation of dust emissions. This work suggests that the upcoming SPEXone sensor will provide observations related to aerosol amount, size and absorption with sufficient coverage and accuracy, in order to estimate aerosol emissions. [ABSTRACT FROM AUTHOR]

Several single-platform satellite missions have been designed during the past decades in order to retrieve the atmospheric concentrations of anthropogenic greenhouse gases (GHG), initiating worldwide efforts towards better monitoring of their sources and sinks. To set up a future operational system for anthropogenic GHG emission monitoring, both revisit frequency and spatial resolution need to be improved. The Space CARBon Observatory (SCARBO) project aims at significantly increasing the revisit frequency of spaceborne GHG measurements, while reaching state-of-the-art precision requirements, by implementing a concept of small satellite constellation. It would accommodate a miniaturized GHG sensor named NanoCarb coupled with an aerosol instrument, the multi-angle polarimeter SPEXone. More specifically, the NanoCarb sensor is a static Fabry-Perot imaging interferometer with a 2.3 x 2.3 km² spatial resolution and 200 km swath. It samples a truncated interferogram at optical path differences (OPDs) optimally sensitive to all the geophysical parameters necessary to retrieve column-averaged dry-air mole fractions of CO2 and CH4 (hereafter XCO2 and XCH4). In this work, we present the Level 2 performance assessment of the concept proposed in the SCARBO project. We perform inverse radiative transfer to retrieve XCO2 and XCH4 directly from synthetic NanoCarb truncated interferograms, and provide their systematic and random errors, column vertical sensitivities and degrees of freedom as a function of five scattering error-critical atmospheric and observational parameters. We show that NanoCarb XCO2 and XCH4 systematic retrieval errors can be greatly reduced with SPEXone posterior outputs used as improved prior aerosol constraints. For two thirds of the soundings, located at the centre of the 200 km NanoCarb swath, XCO2 and XCH4 random errors span 0.5 – 1 ppm and 4 – 6 ppb, respectively, compliant with their respective 1-ppm and 6-ppb precision objectives. Finally, these Level 2 performance results are parameterized as a function of the explored scattering error-critical atmospheric and observational parameters in order to time-efficiently compute extensive L2 error maps for future CO2 and CH4 flux estimation performance studies. [ABSTRACT FROM AUTHOR]

This study presents an investigation of aerosol microphysical retrievals from high spectral resolution lidar (HSRL) measurements. Firstly, retrievals are presented for synthetically generated lidar measurements, followed by an application of the retrieval algorithm to real lidar measurements. Here, we perform the investigation for an aerosol state vector that is typically used in multi-angle polarimeter (MAP) retrievals, so that the results can be interpreted in relation to a potential combination of lidar and MAP measurements. These state vectors correspond to a bimodal size distribution, where column number, effective radius, and effective variance of both modes are treated as fit parameters, alongside the complex refractive index and particle shape. The focus is primarily on a lidar configuration based on that of the High Spectral Resolution Lidar-2 (HSRL-2), which participated in the ACEPOL (Aerosol Characterization from Polarimeter and Lidar) campaign, a combined project between NASA and SRON (Netherlands Institute for Space Research). The measurement campaign took place between October and November 2017, over the western region of the USA. Six different instruments were mounted on the aeroplane: four MAPs and two lidar instruments, HSRL-2 and the Cloud Physics Lidar (CPL). Most of the flights were carried out over land, passing over scenes with a low aerosol load. One of the flights passed over a prescribed forest fire in Arizona on 9 November, with a relatively higher aerosol optical depth (AOD), and it is the data from this flight that are focussed on in this study. A retrieval of the aerosol microphysical properties of the smoke plume mixture was attempted with the data from HSRL-2 and compared with a retrieval from the MAPs carried out in previous work pertaining to the ACEPOL data. The synthetic HSRL-2 retrievals resulted for the fine mode in a mean absolute error (MAE) of 0.038 (0.025) µm for the effective radius (with a mean truth value of 0.195 µm), 0.052 (0.037) for the real refractive index, 0.010 (7.20×10-3) for the imaginary part of the refractive index, 0.109 (0.071) for the spherical fraction, and 0.054 (0.039) for the AOD at 532 nm, where the retrievals inside brackets indicate the MAE for noise-free retrievals. For the coarse mode, we find the MAE is 0.459 (0.254) µm for the effective radius (with a mean truth value of 1.970 µm), 0.085 (0.075) for the real refractive index, 2.06×10-4 (1.90×10-4) for the imaginary component, 0.120 (0.090) for the spherical fraction, and 0.051 (0.039) for the AOD. A study of the sensitivity of retrievals to the choice of prior and first guess showed that, on average, the retrieval errors increase when the prior deviates too much from the truth value. These experiments revealed that the measurements primarily contain information on the size and shape of the aerosol, along with the column number. Some information on the real component of the refractive index is also present, with the measurements providing little on absorption or on the effective variance of the aerosol distribution, as both of these were shown to depend heavily on the choice of prior. Retrievals using the HSRL-2 smoke-plume data yielded, for the fine mode, an effective radius of 0.107 µm , a real refractive index of 1.561, an imaginary component of refractive index of 0.010, a spherical fraction of 0.719, and an AOD at 532 nm of 0.505. Additionally, the single-scattering albedo (SSA) from the HSRL-2 retrievals was 0.940. Overall, these results are in good agreement with those from the Spectropolarimeter for Planetary Exploration (SPEX) and Research Scanning Polarimeter (RSP) retrievals. [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 (MAPs): 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) forward 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 0.01 s in a single CPU core or 1 ms in a 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. Comparing to the retrieval speed using a conventional radiative transfer forward model, the computational acceleration is 103 times faster with CPU or 104 times with GPU processors. 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]

This study presents an investigation of aerosol microphysical retrievals from High Spectral Resolution Lidar (HSRL) measurements. Firstly, retrievals are presented for synthetically-generated lidar measurements, followed by an application of the retrieval algorithm to real lidar measurements. Here, we perform the investigation for an aerosol state vector that is typically used in multi-angle polarimeter (MAP) retrievals, so that the results can be interpreted in relation to a potential combination of lidar and MAP measurements. These state vectors correspond to a bimodal size distribution, where column number, effective radius, and effective variance of both modes are treated as fit parameters, alongside the complex refractive index and particle shape. The focus is primarily on a lidar configuration based on that of the High Spectral Resolution Lidar-2 (HSRL-2), which participated in the ACEPOL (Aerosol Characterization from Polarimeter and Lidar) campaign, a combined project between NASA and SRON (Netherlands Institute for Space Research). The measurement campaign took place between October and November 2017, over the western region of the USA. Six different instruments were mounted on the aeroplane: four MAPs, and two lidar instruments: HSRL-2, and the Cloud Physics Lidar (CPL). Most of the flights were carried out over land, passing over scenes with a low aerosol load. One of the flights passed over a prescribed forest fire in Arizona on the 9th of November, with a relatively higher AOD, and it is the data from this flight that is focussed on in this study. A retrieval of the aerosol microphysical properties of the smoke plume mixture was attempted with the data from HSRL-2, and compared with a retrieval from the MAPs carried out in previous work pertaining to the ACEPOL data. The synthetic HSRL-2 retrievals resulted for the fine mode in a mean absolute error (MAE) of 0.038 (0.025) µm for the effective radius, 0.052 (0.037) for the real refractive index, 0.010 (7.20×10-3) for the imaginary part of the refractive index, 0.109 (0.071) for the spherical fraction, and 0.054 (0.039) for the AOD at 532 nm, where the retrievals inside brackets indicate the MAE for noise-free retrievals. For the coarse mode, we find the MAE is 0.459 (0.254) µm for the effective radius, 0.085 (0.075) for the real refractive index, 2.06×10-4 (1.90×10-4) for the imaginary component, 0.120 (0.090) for the spherical fraction, and 0.051 (0.039) for the AOD. A study of the sensitivity of retrievals to the choice of prior and first guess showed that, on average, the retrieval errors increase when the prior deviates too much from the truth value. These experiments revealed that the measurements primarily contain information on the size and shape of the aerosol, along with the column number. Some information on the real component of the refractive index is also present, with the measurements providing little on absorption or on the effective variance of the aerosol distribution, as both of these were shown to depend heavily on the choice of prior. Retrievals using the HSRL-2 smoke-plume data yielded, for the fine mode, an effective radius of 0.107 µm, a real refractive index of 1.561, an imaginary component of refractive index of 0.010, a spherical fraction of 0.719, and an AOD at 532 nm of 0.505. Additionally, the single-scattering albedo (SSA) from the HSRL-2 retrievals was 0.940. Overall, these results are in good agreement with those from the SPEX and RSP retrievals. [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]

The TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor (S5-P) satellite provides methane (CH 4) measurements with high accuracy and exceptional temporal and spatial resolution and sampling. TROPOMI CH 4 measurements are highly valuable to constrain emissions inventories and for trend analysis, with strict requirements on the data quality. This study describes the improvements that we have implemented to retrieve CH 4 from TROPOMI using the RemoTeC full-physics algorithm. The updated retrieval algorithm features a constant regularization scheme of the inversion that stabilizes the retrieval and yields less scatter in the data and includes a higher resolution surface altitude database. We have tested the impact of three state-of-the-art molecular spectroscopic databases (HITRAN 2008, HITRAN 2016 and Scientific Exploitation of Operational Missions – Improved Atmospheric Spectroscopy Databases SEOM-IAS) and found that SEOM-IAS provides the best fitting results. The most relevant update in the TROPOMI XCH 4 data product is the implementation of an a posteriori correction fully independent of any reference data that is more accurate and corrects for the underestimation at low surface albedo scenes and the overestimation at high surface albedo scenes. After applying the correction, the albedo dependence is removed to a large extent in the TROPOMI versus satellite (Greenhouse gases Observing SATellite – GOSAT) and TROPOMI versus ground-based observations (Total Carbon Column Observing Network – TCCON) comparison, which is an independent verification of the correction scheme. We validate 2 years of TROPOMI CH 4 data that show the good agreement of the updated TROPOMI CH 4 with TCCON (- 3.4 ± 5.6 ppb) and GOSAT (- 10.3 ± 16.8 ppb) (mean bias and standard deviation). Low- and high-albedo scenes as well as snow-covered scenes are the most challenging for the CH 4 retrieval algorithm, and although the a posteriori correction accounts for most of the bias, there is a need to further investigate the underlying cause. [ABSTRACT FROM AUTHOR]

The TROPOspheric Monitoring Instrument (TROPOMI) aboard of the Sentinel 5 Precursor (S5-P) satellite provides methane (CH4) measurements with high accuracy and exceptional temporal and spatial resolution. TROPOMI CH4 measurements are highly valuable to constrain emissions inventories and for trend analysis, with strict requirements on the data quality. This study describes the improvements that we have implemented to retrieve CH4 from TROPOMI using the RemoTeC full-physics algorithm. The updated TROPOMI CH4 product features a constant regularization scheme of the inversion that stabilizes the retrieval and yields less scatter in the data, and includes a higher resolution surface altitude database. We have tested the impact of three state-of-the-art molecular spectroscopic databases (HITRAN 2008, HITRAN 2016 and Scientific Exploitation of Operational Missions - Improved Atmospheric Spectroscopy Databases SEOM-IAS) and found that SEOM-IAS provides the best fitting results. The most relevant update in the TROPOMI XCH4 data product is the implementation of a posteriori correction fully independent of any reference data that is more accurate and corrects for the underestimation at low surface albedo scenes and the overestimation at high surface albedo scenes. After applying the correction, the albedo dependence is removed to a large extent in the TROPOMI versus satellite (Greenhouse gases Observing SATellite - GOSAT) and TROPOMI versus ground-based observations (Total Carbon Column Observing Network - TCCON) comparison, which is an independent verification of the correction scheme. We validate two years of TROPOMI CH4 data that shows the good agreement of the updated TROPOMI CH4 with TCCON (-3.4 ± 5.6 ppb) and GOSAT (-10.3 ± 16.8 pbb) (mean bias and standard deviation). Low and high albedo scenes as well as snow covered scenes are the most challenging for the CH4 retrieval algorithm, and although the posteriori correction accounts for most of the bias, there is a need to further investigate the underlying cause. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the fall of 2017, an airborne field campaign was conducted from the NASA Armstrong Flight Research Center in Palmdale, California, to advance the remote sensing of aerosols and clouds with multi-angle polarimeters (MAP) and lidars. The Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign was jointly sponsored by NASA and the Netherlands Institute for Space Research (SRON). Six instruments were deployed on the ER-2 high-altitude aircraft. Four were MAPs: the Airborne Hyper Angular Rainbow Polarimeter (AirHARP), the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI), the Airborne Spectrometer for Planetary EXploration (SPEX airborne), and the Research Scanning Polarimeter (RSP). The remainder were lidars, including the Cloud Physics Lidar (CPL) and the High Spectral Resolution Lidar 2 (HSRL-2). The southern California base of ACEPOL enabled observation of a wide variety of scene types, including urban, desert, forest, coastal ocean, and agricultural areas, with clear, cloudy, polluted, and pristine atmospheric conditions. Flights were performed in coordination with satellite overpasses and ground-based observations, including the Ground-based Multiangle SpectroPolarimetric Imager (GroundMSPI), sun photometers, and a surface reflectance spectrometer. ACEPOL is a resource for remote sensing communities as they prepare for the next generation of spaceborne MAP and lidar missions. Data are appropriate for algorithm development and testing, instrument intercomparison, and investigations of active and passive instrument data fusion. They are freely available to the public. The DOI for the primary database is 10.5067/SUBORBITAL/ACEPOL2017/DATA001 (ACEPOL Science Team, 2017), while for AirMSPI it is 10.5067/AIRCRAFT/AIRMSPI/ACEPOL/RADIANCE/ELLIPSOID_V006 and 10.5067/AIRCRAFT/AIRMSPI/ACEPOL/RADIANCE/TERRAIN_V006 (ACEPOL AirMSPI 75 Science Team, 2017a, b). GroundMSPI data are at 10.5067/GROUND/GROUNDMSPI/ACEPOL/RADIANCE_v009 (GroundMSPI Science Team, 2017). Table 3 lists further details of these archives. This paper describes ACEPOL for potential data users and also provides an outline of requirements for future field missions with similar objectives. [ABSTRACT FROM AUTHOR]

In the fall of 2017, an airborne field campaign was conducted from the NASA Armstrong Flight Research Center in Palmdale, California to advance the remote sensing of aerosols and clouds with Multi- angle Polarimeters (MAP) and Lidars. The Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign was jointly sponsored by NASA and the Netherlands Institute for Space Research (SRON). Six instruments were deployed on the ER-2 high altitude aircraft. Four were MAPs: the Airborne Hyper Angular Rainbow Polarimeter (AirHARP), the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI), the Airborne Spectrometer for Planetary EXploration (SPEX Airborne) and the Research Scanning Polarimeter (RSP). The remainder were Lidars, including the Cloud Physics Lidar (CPL) and the High Spectral Resolution Lidar 2 (HSRL2). The southern California base of ACEPOL enabled observation of a wide variety of scene types, including urban, desert, forest, coastal ocean and agricultural areas, with clear, cloudy, polluted and pristine atmospheric conditions. Flights were performed in coordination with satellite overpasses and ground based observations, including the Groundbased Multiangle SpectroPolarimetric Imager (GroundMSPI), sun photometers, and a surface reflectance spectrometer. ACEPOL is a resource for remote sensing communities as they prepare for the next generation of spaceborne MAP and lidar missions. Data are appropriate for algorithm development and testing, instrument intercomparison, and investigations of active and passive instrument data fusion. They are freely available to the public, at https://doi.org/10.5067/SUBORBITAL/ACEPOL2017/DATA001 (ACEPOL Science Team, 2017). This paper describes ACEPOL for potential data users, and also provides an outline of requirements for future field missions with similar objectives. [ABSTRACT FROM AUTHOR]

NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in the timeframe of late 2022 to early 2023, will carry the Ocean Color Instrument (OCI), a hyperspectral scanning radiometer, and two multi-angle polarimeters (MAP), the UMBC Hyper-Angular Rainbow Polarimeter (HARP2) and the SRON Spectro-Polarimeter for Planetary EXploration one (SPEXone). One purpose of the PACE MAPs is to better characterize aerosols 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 collocated hyperspectral ocean color measurements has 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 collocated RSP and SPEX Airborne measurements and Aerosol Robotic Network (AERONET) in-situ observations. The aerosol retrievals are performed and compared with two options: one uses reflectance only and the other use 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 with good agreement. 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. [ABSTRACT FROM AUTHOR]