ALBERT

Description: In this study we developed a neural network (NN) that can be used to retrieve cloud microphysical properties from multiangular and multispectral polarimetric remote sensing observations. This effort builds upon our previous work, which explored the sensitivity of neural network input, architecture, and other design requirements for this type of remote sensing problem. In particular this work introduces a framework for appropriately weighting total and polarized reflectances, which have vastly different magnitudes and measurement uncertainties. The NN is trained using an artificial training set and applied to research scanning polarimeter (RSP) data obtained during the ORACLES field campaign (ObseRvations of Aerosols above CLouds and their intEractionS). The polarimetric RSP observations are unique in that they observe the same cloud from a very large number of angles within a variety of spectral bands, resulting in a large dataset that can be explored rapidly with a NN approach. The usefulness of applying a NN to a dataset such as this one stems from the possibility of rapidly obtaining a retrieval that could be subsequently applied as a first guess for slower but more rigorous physical-based retrieval algorithms. This approach could be particularly advantageous for more complicated atmospheric retrievals – such as when an aerosol layer lies above clouds like in ORACLES. For RSP observations obtained during ORACLES 2016, comparisons between the NN and standard parametric polarimetric (PP) cloud retrieval give reasonable results for droplet effective radius (re: R=0.756, RMSE=1.74 µm) and cloud optical thickness (τ: R=0.950, RMSE=1.82). This level of statistical agreement is shown to be similar to comparisons between the two most well-established cloud retrievals, namely, the polarimetric and the bispectral total reflectance cloud retrievals. The NN retrievals from the ORACLES 2017 dataset result in retrievals of re (R=0.54, RMSE=4.77 µm) and τ (R=0.785, RMSE=5.61) that behave much more poorly. In particular we found that our NN retrieval approach does not perform well for thin (τ

Description: 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 https://doi.org/10.5067/SUBORBITAL/ACEPOL2017/DATA001 (ACEPOL Science Team, 2017), while for AirMSPI it is https://doi.org/10.5067/AIRCRAFT/AIRMSPI/ACEPOL/RADIANCE/ELLIPSOID_V006 and https://doi.org/10.5067/AIRCRAFT/AIRMSPI/ACEPOL/RADIANCE/TERRAIN_V006 (ACEPOL AirMSPI 75 Science Team, 2017a, b). GroundMSPI data are at https://doi.org/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.

Description: Using collocated NASA’s Cloud Physics Lidar (CPL) and Research Scanning Polarimeter (RSP) data from the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) campaign, a new observational-based method was developed which uses a K-means clustering technique to classify ice crystal habit types into seven categories: column, plates, rosettes, spheroids and three different type of irregulars. Inter-compared with the collocated SPEC Inc. Cloud Particle Imager (CPI) data, the frequency of the detected ice crystal habits from the proposed method presented in the study agree within 5% of the CPI reported values for columns, irregulars, rosettes, and spheroids, with more disagreement for plates. This study suggests that a detailed ice crystal habit retrieval could be applied to combined space-based lidar and polarimeter observations such as CALIPSO and POLDER in addition to future missions such as the Aerosols, Clouds, Convection, and Precipitation (A-CCP).

Description: We provide a parameterization of the extinction efficiency, single-scattering albedo, and asymmetry parameter of single ice crystals with any combination of particle volume, projected area, component aspect ratio, and crystal distortion at any wavelength between 0.2 and 100 μm. The parameterization is an extension of the one previously published by van Diedenhoven et al. In addition, the parameterized optical properties are integrated over size distributions yielding bulk extinction efficiencies, single-scattering albedos, and asymmetry parameters for large ranges of effective radii, particle component aspect ratios, and crystal distortion values. The parameterization of single-particle optical properties is evaluated with a reference database. The bulk optical properties are evaluated against the ice model selected for the Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 products, for which accurate optical properties are available. Mean absolute errors in parameterized extinction efficiency, asymmetry parameter, and single-scattering albedo are shown to be 0.0272, 0.008 90, and 0.004 68, respectively, for shortwave wavelengths, while they are 0.0641, 0.0368, and 0.0200 in the longwave. Shortwave and longwave asymmetry parameters and single-scattering albedos are shown to vary strongly with particle component aspect ratio and distortion, resulting in substantial variation in shortwave fluxes, but relatively small variations in longwave cloud emissivity. The parameterization and bulk optical properties are made publicly available.

Description: The Twomey effect describes the radiative forcing associated with a change in cloud albedo due to an increase in anthropogenic aerosol emissions. It is driven by the perturbation in cloud droplet number concentration (ΔNd, ant) in liquid-water clouds and is currently understood to exert a cooling effect on climate. The Twomey effect is the key driver in the effective radiative forcing due to aerosol–cloud interactions, but rapid adjustments also contribute. These adjustments are essentially the responses of cloud fraction and liquid water path to ΔNd, ant and thus scale approximately with it. While the fundamental physics of the influence of added aerosol particles on the droplet concentration (Nd) is well described by established theory at the particle scale (micrometres), how this relationship is expressed at the large-scale (hundreds of kilometres) perturbation, ΔNd, ant, remains uncertain. The discrepancy between process understanding at particle scale and insufficient quantification at the climate-relevant large scale is caused by co-variability of aerosol particles and updraught velocity and by droplet sink processes. These operate at scales on the order of tens of metres at which only localised observations are available and at which no approach yet exists to quantify the anthropogenic perturbation. Different atmospheric models suggest diverse magnitudes of the Twomey effect even when applying the same anthropogenic aerosol emission perturbation. Thus, observational data are needed to quantify and constrain the Twomey effect. At the global scale, this means satellite data. There are four key uncertainties in determining ΔNd, ant, namely the quantification of (i) the cloud-active aerosol – the cloud condensation nuclei (CCN) concentrations at or above cloud base, (ii) Nd, (iii) the statistical approach for inferring the sensitivity of Nd to aerosol particles from the satellite data and (iv) uncertainty in the anthropogenic perturbation to CCN concentrations, which is not easily accessible from observational data. This review discusses deficiencies of current approaches for the different aspects of the problem and proposes several ways forward: in terms of CCN, retrievals of optical quantities such as aerosol optical depth suffer from a lack of vertical resolution, size and hygroscopicity information, non-direct relation to the concentration of aerosols, difficulty to quantify it within or below clouds, and the problem of insufficient sensitivity at low concentrations, in addition to retrieval errors. A future path forward can include utilising co-located polarimeter and lidar instruments, ideally including high-spectral-resolution lidar capability at two wavelengths to maximise vertically resolved size distribution information content. In terms of Nd, a key problem is the lack of operational retrievals of this quantity and the inaccuracy of the retrieval especially in broken-cloud regimes. As for the Nd-to-CCN sensitivity, key issues are the updraught distributions and the role of Nd sink processes, for which empirical assessments for specific cloud regimes are currently the best solutions. These considerations point to the conclusion that past studies using existing approaches have likely underestimated the true sensitivity and, thus, the radiative forcing due to the Twomey effect.

Description: This study presents a new algorithm that combines W-band reflectivity measurements from the Airborne Precipitation Radar – third generation (APR-3) passive radiometric cloud optical depth and effective radius retrievals from the Research Scanning Polarimeter (RSP) to estimate total liquid water path in warm clouds and identify the contributions from cloud water path (CWP) and rainwater path (RWP). The resulting CWP estimates are primarily determined by the optical depth input, although reflectivity measurements contribute ∼10 %–50 % of the uncertainty due to attenuation through the profile. Uncertainties in CWP estimates across all conditions are 25 % to 35 %, while RWP uncertainty estimates frequently exceed 100 %. Two-thirds of all radar-detected clouds observed during the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign that took place from 2016–2018 over the southeast Atlantic Ocean have CWP between 41 and 168 g m−2 and almost all CWPs (99 %) between 6 to 445 g m−2. RWP, by contrast, typically makes up a much smaller fraction of total liquid water path (LWP), with more than 70 % of raining clouds having less than 10 g m−2 of rainwater. In heavier warm rain (i.e., rain rate exceeding 40 mm h−1 or 1000 mm d−1), however, RWP is observed to exceed 2500 g m−2. CWP (RWP) is found to be approximately 30 g m−2 (7 g m−2) larger in unstable environments compared to stable environments. Surface precipitation is also more than twice as likely in unstable environments. Comparisons against in situ cloud microphysical probe data spanning the range of thermodynamic stability and meteorological conditions encountered across the southeast Atlantic basin demonstrate that the combined APR-3 and RSP dataset enable a robust joint cloud–precipitation retrieval algorithm to support future ORACLES precipitation susceptibility and cloud–aerosol–precipitation interaction studies.