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Network representation learning (NRL) aims to convert nodes of a network into vector forms in Euclidean space. The information of a network is needed to be preserved as much as possible when NRL converts nodes into vector representation. A hybrid approach proposed in this paper is a framework to improve other NRL methods by considering the structure of densely connected nodes (community-like structure). HARP [1] is to contract a network into a series of contracted networks and embed them from the high-level contracted network to the low-level one. The vector representation (or embedding) for a high-level contracted network is used to initialize the learning process of a low-level contracted graph hierarchically. In this method (Hybrid Approach), HARP is revised by using a well-designed initialization process on the most high-level contracted network to preserve more community-like structure information.

The considerable local dissolution of strengthening phases under gradient thermo-mechanical effects of friction stir welding leads to a noticeable decrease in material local hardness and tensile properties in friction stir welded Al-Li joints at the joint central area, known as the thermo-mechanical coupling zone, compared to its surrounding regions. Consequently, the resulting high gradient in joint local properties at borders of the joint center leads to high local stress concentration and tensile failure under relatively low loading values at this region. In this study, firstly, effects of joint center laser-peening-induced local compressive residual stresses evolution and work hardening are investigated on the enhancement in local hardness and tensile properties of AA2195-T6 friction stir welded joints. Then overall induced joint mechanical property homogeneity effects from these effects are evaluated on the improvement in joint global tensile properties. LP-induced local residual stresses are measured through the depth using the X-ray diffraction method. X-ray diffraction pattern peak broadening analysis is utilized to measure dislocation density local variations after LP which represent plastic work hardening effects. Local tensile properties are characterized by the digital image correlation technique with the corporation of the monotonic tensile test approach. Considerable increments in local material properties lead to the enhancement of overall joint tensile yield strength up to about 31% and ultimate tensile strength to 11% as a result of LP-induced compressive residual stresses, surface work hardening and joint tensile homogenization effects.

We quantify the performance of aerosol and ocean remote sensing products from the PolCube instrument using a previously developed polarimeter retrieval algorithm based on optimal estimation. PolCube is a modified version of the PolCam lunar instrument on the Korea Pathfinder Lunar Orbiter that has been optimized for Earth-Science observations of aerosol, ocean, and thin cloud optical properties. The objective of the PolCube instrument is to retrieve detailed fine-mode (pollution and smoke) and coarse-mode (sea-salt and dust) aerosol properties over the ocean for a range of light to heavy aerosol loadings using its polarimetric-imaging capability at multiple angles and wavelengths from 410−865 nm. An additional objective is to discriminate aerosols from thin clouds. PolCube’s retrieval performance of aerosol optical and microphysical properties and ocean products is quantitatively assessed. We estimate that PolCube can retrieve total aerosol optical depth at 555 nm (AOD555) within ±0.068, fine-mode AOD555 within ±0.078, and fine-mode single-scattering albedo within ±0.036, where all uncertainties are expressed as one standard deviation (1σ). PolCube’s accurate and high-resolution aerosol-retrieval products will provide unique spatial and temporal coverage of the Earth that can be used synergistically with other instruments, such as the Geostationary Environmental Monitoring Spectrometer to improve air-quality forecasting.

The Advanced Topographic Laser Altimeter System (ATLAS) aboard the Ice, Cloud, and land Elevation Satellite‐2 (ICESat‐2) has been making very high resolution measurements of the Earth’s surface elevation since October 2018. ATLAS uses photomultiplier tubes (PMTs) as detectors in photon counting mode, so that a single photon reflected back to the receiver triggers a detection within the ICESat‐2 data acquisition system. However, one characteristic of ICESat‐2 detected photons is the possible presence of afterpulses, defined as small amplitude pulses occurring after the primary signal pulse due to photon arrival. The disadvantage of these afterpulses is that they often confound the accurate measurements of low level signals following a large amplitude of signal and can degrade energy resolution and cause errors in pulse counting applications. This paper discusses and summarizes the after‐pulsing effects exhibited by the ATLAS PMTs based on on‐orbit measurements over different seasons and geographic regions. The potential impacts of these after‐pulsing effects on altimetry and ocean subsurface retrievals are discussed., Key Points The effects of after‐pulsing by the Advanced Topographic Laser Altimeter System (ATLAS) aboard the ICESat‐2 satellite are describedThe transient response of the ATLAS receiver is characterized over different measurement regimesThe potential impacts of these detector artifacts on ICESat‐2 science studies are discussed

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

The Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) views nearly the whole sunlit face of the Earth with 10 spectral bands ranging from the UV to the near-infrared, including two oxygen absorbing bands centered at 764 nm (A-band) and 687.75 nm (B-band). Clouds are among the primary remote sensing targets using EPIC images because of their important impacts on the Earth’s radiation budget. In order to facilitate the EPIC cloud data product development, we have built a radiative transfer simulator featuring flexible cloud microphysical parameters, gas absorptions, and the instrument line shape functions for each EPIC band. The radiative transfer simulator is used to explore the sensitivity of EPIC observations on liquid-phase cloud microphysical parameters, including optical depth, geometric thickness, and cloud top height. We found that the ratios of the reflectances in the oxygen A and B bands to their respective continuum measurements can be used to increase the confidence level of cloud masking over scenes with sun-glint. In addition, the 388 nm band can be used to differentiate low and high clouds with the uncertainty of roughly 2–3 km. Combining this information with the oxygen absorption bands, the cloud geometric thickness can be obtained with the rough uncertainty of 3–4 km.

This study applies fuzzy k-means (FKM) cluster analyses to a subset of the parameters reported in the CALIPSO lidar level 2 data products in order to classify the layers detected as either clouds or aerosols. The results obtained are used to assess the reliability of the cloud–aerosol discrimination (CAD) scores reported in the version 4.1 release of the CALIPSO data products. FKM is an unsupervised learning algorithm, whereas the CALIPSO operational CAD algorithm (COCA) takes a highly supervised approach. Despite these substantial computational and architectural differences, our statistical analyses show that the FKM classifications agree with the COCA classifications for more than 94 % of the cases in the troposphere. This high degree of similarity is achieved because the lidar-measured signatures of the majority of the clouds and the aerosols are naturally distinct, and hence objective methods can independently and effectively separate the two classes in most cases. Classification differences most often occur in complex scenes (e.g., evaporating water cloud filaments embedded in dense aerosol) or when observing diffuse features that occur only intermittently (e.g., volcanic ash in the tropical tropopause layer). The two methods examined in this study establish overall classification correctness boundaries due to their differing algorithm uncertainties. In addition to comparing the outputs from the two algorithms, analysis of sampling, data training, performance measurements, fuzzy linear discriminants, defuzzification, error propagation, and key parameters in feature type discrimination with the FKM method are further discussed in order to better understand the utility and limits of the application of clustering algorithms to space lidar measurements. In general, we find that both FKM and COCA classification uncertainties are only minimally affected by noise in the CALIPSO measurements, though both algorithms can be challenged by especially complex scenes containing mixtures of discrete layer types. Our analysis results show that attenuated backscatter and color ratio are the driving factors that separate water clouds from aerosols; backscatter intensity, depolarization, and mid-layer altitude are most useful in discriminating between aerosols and ice clouds; and the joint distribution of backscatter intensity and depolarization ratio is critically important for distinguishing ice clouds from water clouds.

Thin sections are widely used in industry and need surface hardening to improve their anti-fatigue properties. This study proposes an improved single-sided LP as an effective way for the surface hardening of thin sections with self-occlusion on one side to overcome the difficulty in double-sided LP. Controlled experiments are conducted on a few specimens with thin sections to investigate the distortion behavior after LP treatment with different process parameters. Finite element method is used to analyze the generation of geometry distortion and explain the difference between LP treatment with several process parameters. The results show that both laser intensity and spot size are of great concern in the geometry distortion after single-sided LP, generating different distortion directions. Single-sided LP with a suitable process parameter set of laser intensity and spot size can enhance specimens without distortion. Numerical model shows the generation of different distortion behaviors is highly related to the bending moment, which is calculated from plastic strain field. Besides, part of plastic strain produced by local deformation varies after LP treatment with different spot sizes, which generates different distortion profiles with the same laser intensity. Furthermore, compressive residual stress is induced on both sides of thin sections by single-sided LP treatment, which ensures the effectivity for the treatment of thin sections.

Snow surfaces can be composed of ice crystals, melt-form snow or melt-freeze snow. Solar radiation scattered by such surfaces is polarized and the amount of polarization depends on the surface composition. This can be a source of measurement errors in satellite data if a non-polarimetric radiometric sensor is sensitive to the polarization state of light. To obtain highly accurate spectral solar radiation data from the Earth-atmosphere system for the space-borne inter-calibration studies as proposed in NASA's Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission and the CLARREO Pathfinder (CPF) mission, the spectral polarization state of the reflected solar light at the top of atmosphere (TOA) must be known with sufficient accuracy. The degree of polarization (DOP) and the angle of linear polarization (AOLP) of the light at the TOA as functions of incident and viewing geometry and scene type construct the Polarization Distribution Models (PDMs) for correction of polarization-induced error of satellite data [3] . In this work, algorithms for modeling the spectral polarization state of reflected sunlight from various types of snow, including particulate snow, melt-form snow, and melt-freeze snow crusts are developed. A particulate snow surface model based on a mixture of spherical and hexagonal-column particle shapes is used. A rough-surface ice/water facet model is introduced to approximate the melt-form snow and melt-freeze snow crusts. The area fraction of facets for typical (mostly particulate) snow, and for melt-form and melt-freeze snow crusts, is estimated by comparing the model results with the PARASOL satellite data and with ground measurements conducted in Hokkaido, Japan, and in the northwest Greenland ice sheet. Our numerical results demonstrate that the model can provide a reliable approach for making the spectral PDMs for wavelengths between 320 and 2300 nm for satellite inter-calibration applications as proposed in the CLARREO and the CLARREO CPF missions for all kinds of snow surfaces.

Quantitative evaluation of geometry effect on the bending in laser peen forming (LPF) is of particular importance to achieve complex shaping and distortion correction accurately. In this study, analytical modeling, numerical simulation and experiments are carried out to investigate the geometry effect with a simplified single-curved shape characterized by the generatrix length, the sectional arc length, the sheet thickness and the sectional radius. The normalized deviation of arc height is defined to describe the difference of bending deformation between a flat specimen and a specimen with certain geometry. The normalized deviation is insensitive to the specimen length when the generatrix length is twice as the arc length. The proposed analytical model can provide a consistent prediction of geometry effect with the results from both simulation and experiments under its applicable condition. Bending deformation is increased by increasing sectional radius. A dimensionless quantity, composed of overall geometry parameters, is proposed to quantitatively evaluate the geometry effect. The relationship between the normalized deviation of arc height and the proposed dimensionless quantity is revealed with a single functional curve, which is well validated by the simulation and experimental results. The proposed simple functional relationship can provide an effective way to quantitatively evaluate the geometry effect on bending deformation for process planning of LPF for shape control.

Remote sensing from Earth‐observing satellites is now providing valuable information about ocean phytoplankton distributions. This paper presents the new ocean subsurface optical properties obtained from two space‐based lidars: the Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations satellite and the Advanced Topographic Laser Altimeter System (ATLAS) aboard the Ice, Cloud, and land Elevation Satellite‐2 satellite. Obtaining reliable estimates of subsurface biomass necessitates removing instrument artifacts peculiar to each sensor, that is, polarization crosstalk artifacts in the CALIOP signals and after pulsing effects arising from the ATLAS photodetectors. We validate the optical properties derived from the corrected lidar backscatter signals using MODerate‐resolution Imaging Spectroradiometer ocean color measurements and autonomous biogeochemical Argo float profiles. Our results support the continued use of present and future spaceborne lidars to study the global plankton system and characterize its vertical structures in the upper ocean.

The change in planetary albedo due to aerosol-cloud interactions (aci) during the industrial era is the leading source of uncertainty in inferring Earth's climate sensitivity to increased greenhouse gases from the historical record. Examining pristine environments such as the Southern Ocean (SO) helps us to understand the pre-industrial state and constrain the change in cloud brightness over the industrial period associated with aci. This study presents two methods of utilizing observations of pristine environments to examine climate models and our understanding of the pre-industrial state. First, cloud droplet number concentration (Nd) is used as an indicator of aci. Global climate models (GCMs) show that the hemispheric contrast in liquid cloud Nd between the pristine SO and the polluted Northern Hemisphere observed in the present-day can be used as a proxy for the increase in Nd from the pre-industrial. A hemispheric difference constraint developed from MODIS satellite observations indicates that pre-industrial Nd may have been higher than previously thought and provides an estimate of radiative forcing associated with aci between -1.2 and -0.6 Wm-2. Comparisons with MODIS Nd highlight significant GCM discrepancies in pristine, biologically active regions. Second, aerosol and cloud microphysical observations from a recent SO aircraft campaign are used to identify two potentially important mechanisms that are incomplete or missing in GCMs: i) production of new aerosol particles through synoptic uplift, and ii) buffering of Nd against precipitation removal by small, Aitken mode aerosols entrained from the free troposphere. The latter may significantly contribute to the high, summertime SO Nd levels which persist despite precipitation depletion associated with mid-latitude storm systems. Observational comparisons with nudged Community Atmosphere Model version 6 (CAM6) hindcasts show low-biased SO Nd is linked to under-production of free-tropospheric Aitken aerosol which drives low-biases in cloud condensation nuclei number and likely discrepancies in composition. These results have important implications for the ability of current GCMs to capture aci in pristine environments.

A neural network nonlinear regression algorithm is developed for retrieving ocean surface wind speed from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar measurements. The neural network is trained with CALIPSO ocean surface and atmospheric backscatter measurements together with collocated Advanced Microwave Scanning Radiometer for EOS (AMSR-E) ocean surface wind speed. Ocean surface wind speeds are derived by applying the neural network algorithm to CALIPSO measurements between 2008 and 2020. CALIPSO wind speed measurements of 2015 are also compared with Advanced Microwave Scanning Radiometer 2 (AMSR-2) measurements on the Global Change Observation Mission–Water “Shizuku” (GCOM-W) satellite. Aerosol optical depths are then derived from CALIPSO’s ocean surface backscatter signal and theoretical ocean surface reflectance calculated from CALIPSO wind speed and Cox-Munk wind–surface slope variance relation. This CALIPSO wind speed retrieval technique is an improvement from our previous studies, as it can be applied to most clear skies with optical depths up to 1.5 without making assumptions about aerosol lidar ratio.

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

We use photon counting and a sweeping high-Q microring on chip to obtain the atmospheric carbon dioxide absorption spectrum around 1572.022 nm.

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The Cloud&ndash, Aerosol Lidar with Orthogonal Polarization (CALIOP), on-board the Cloud&ndash, Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) platform, is an elastic backscatter lidar that has been providing vertical profiles of the spatial, optical, and microphysical properties of clouds and aerosols since June 2006. Distinguishing between feature types (i.e., clouds vs. aerosol) and subtypes (e.g., ice clouds vs. water clouds and dust aerosols from smoke) in the CALIOP measurements is currently accomplished using layer-integrated measurements acquired by co-polarized (parallel) and cross-polarized (perpendicular) 532 nm channels and a single 1064 nm channel. Newly developed deep machine learning (DML) semantic segmentation methods now have the ability to combine observations from multiple channels with texture information to recognize patterns in data. Instead of focusing on a limited set of layer integrated values, our new DML feature classification technique uses the full scope of range-resolved information available in the CALIOP attenuated backscatter profiles. In this paper, one of the convolutional neural networks (CNN), SegNet, a fast and efficient DML model, is used to distinguish aerosol subtypes directly from the CALIOP profiles. The DML method is a 2D range bin-to-range bin aerosol subtype classification algorithm. We compare our new DML results to the classifications generated by CALIOP&rsquo, s 1D layer-to-layer operational retrieval algorithm. These two methods, which take distinctly different approaches to aerosol classification, agree in over 60% of the comparisons. Higher levels of agreement are found in homogeneous scenes containing only a single aerosol type (i.e., marine, stratospheric aerosols). Disagreement between the two techniques increases in regions containing mixture of different aerosol types. The multi-dimensional texture information leveraged by the DML method shows advantages in differentiating between aerosol types based on their classification scores, as well as in distinguishing vertical distributions of aerosol types within individual layers. However, untangling mixtures of aerosol subtypes is still challenging for both the DML and operational algorithms.

This paper describes a method to accurately identify the melt and freeze onset of sea ice using polar-orbiting Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar measurements from the Cloud and Aerosol Lidar and Infrared Pathfinder Observation (CALIPSO) mission. The proposed method is based on the integrated depolarization ratio and attenuated backscatter to separate surface melting and freezing from open water and snow/ice covered surfaces. The method has been applied to CALIOP data over different Arctic regions to study the annual variability of melt and freeze onset. The > 10 years of CALIOP continuous observations of the sea ice melt and freeze onset will benefit the scientific research communities to better assess and quantify global environmental changes.

Accurate determination of thermodynamic cloud phase is critical for establishing the radiative impact of clouds on climate and weather. Depolarization of the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) 532 nm signal provides a useful addition to other methods of thermodynamic phase discrimination that rely on temperature, cloud top altitude or a temperature-based cloud phase climatology. Active detection of the thermodynamic phase of multiple cloud layers in a vertical column using cloud layer-integrated depolarization and backscatter also alleviates ambiguities in cloud phase determination by passive radiometers. The CALIOP phase algorithm primarily uses vertically integrated cloud layer depolarization and attenuated backscatter to determine the dominant thermodynamic phase of hydrometeors present in a cloud layer segment, at horizontal resolutions for cloud layer detection varying between 333 m and 80 km, with cloud layer vertical resolutions between 60 m and 8 km. CALIOP ice cloud backscatter observations taken with a 0.3∘ near-nadir view between June 2006 and November 2007 include a significant amount of specular reflection from hexagonal smooth crystal faces that are oriented perpendicularly to the incident lidar beam (horizontally oriented ice – HOI). These specular reflections from HOI are shown here to occur between 0 and −40 ∘C, with a peak in the CALIOP distribution observed globally at −15 ∘C. Recent viewing angle testing occurring during 2017 at 1, 1.5 and 2∘ and reported here quantifies the impact of changing the viewing angle on these specular reflections and verifies earlier observations by POLDER. These viewing angle tests show that at the −15 ∘C peak of the HOI distribution the mean backscatter from all ice clouds decreases by 50 % and depolarization increases by a factor of 5 as the viewing angle increases from 0.3 to 3∘. To avoid these specular reflections, the CALIOP viewing angle was changed from 0.3 to 3∘ in November 2007, and since then CALIOP has been observing clouds almost continuously for 12–13 more years. This has provided more data for a thorough re-evaluation of phase determination and has motivated changes to the CALIOP cloud phase algorithm for Version 4 (V4). The V4 algorithm now excludes over-identification of HOI at 3∘, particularly in cold clouds. The V4 algorithm also considers cloud layer temperature at the 532 nm centroid and has been streamlined for more consistent identification of water and ice clouds. In V4 some cloud layer boundaries have changed because 532 nm layer-integrated attenuated backscatter in V4 has increased due to improved calibration and extended layer boundaries, while the corresponding depolarization has stayed about the same. There are more V4 cloud layers detected and, combined with increasing cloud edges, the V4 total atmospheric cloud volume increases by 6 %–9 % over V3 for high-confidence cloud phases and by 1 %–2 % for all cloudy bins. Collocated CALIPSO Imaging Infrared Radiometer (IIR) observations of ice and water cloud particle microphysical indices complement the CALIOP ice and water cloud phase determinations.

Significance Enhancement of aerosol that can nucleate cloud droplets increases the droplet number concentration and albedo of clouds. This increases the amount of sunlight reflected to space. Uncertainty in how aerosol−cloud interactions over the industrial period have increased planetary albedo by this mechanism leads to significant uncertainty in climate projections. Our work presents a method for observationally constraining the change in albedo due to anthropogenic aerosol emissions: a hemispheric difference in remotely sensed cloud droplet number between the pristine Southern Ocean (a preindustrial proxy) and the polluted Northern Hemisphere. Application of this constraint to climate models reduces the range of estimated albedo change since industrialization and suggests current models underpredict cloud droplet number concentration in the preindustrial era., The change in planetary albedo due to aerosol−cloud interactions during the industrial era is the leading source of uncertainty in inferring Earth’s climate sensitivity to increased greenhouse gases from the historical record. The variable that controls aerosol−cloud interactions in warm clouds is droplet number concentration. Global climate models demonstrate that the present-day hemispheric contrast in cloud droplet number concentration between the pristine Southern Hemisphere and the polluted Northern Hemisphere oceans can be used as a proxy for anthropogenically driven change in cloud droplet number concentration. Remotely sensed estimates constrain this change in droplet number concentration to be between 8 cm−3 and 24 cm−3. By extension, the radiative forcing since 1850 from aerosol−cloud interactions is constrained to be −1.2 W⋅m−2 to −0.6 W⋅m−2. The robustness of this constraint depends upon the assumption that pristine Southern Ocean droplet number concentration is a suitable proxy for preindustrial concentrations. Droplet number concentrations calculated from satellite data over the Southern Ocean are high in austral summer. Near Antarctica, they reach values typical of Northern Hemisphere polluted outflows. These concentrations are found to agree with several in situ datasets. In contrast, climate models show systematic underpredictions of cloud droplet number concentration across the Southern Ocean. Near Antarctica, where precipitation sinks of aerosol are small, the underestimation by climate models is particularly large. This motivates the need for detailed process studies of aerosol production and aerosol−cloud interactions in pristine environments. The hemispheric difference in satellite estimated cloud droplet number concentration implies preindustrial aerosol concentrations were higher than estimated by most models.

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.

The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) version 4.10 (V4) level 2 aerosol data products, released in November 2016, include substantial improvements to the aerosol subtyping and lidar ratio selection algorithms. These improvements are described along with resulting changes in aerosol optical depth (AOD). The most fundamental change in the V4 level 2 aerosol products is a new algorithm to identify aerosol subtypes in the stratosphere. Four aerosol subtypes are introduced for stratospheric aerosols: polar stratospheric aerosol (PSA), volcanic ash, sulfate/other, and smoke. The tropospheric aerosol subtyping algorithm was also improved by adding the following enhancements: (1) all aerosol subtypes are now allowed over polar regions, whereas the version 3 (V3) algorithm allowed only clean continental and polluted continental aerosols; (2) a new dusty marine aerosol subtype is introduced, representing mixtures of dust and marine aerosols near the ocean surface; and (3) the polluted continental and smoke subtypes have been renamed polluted continental/smoke and elevated smoke, respectively. V4 also revises the lidar ratios for clean marine, dust, clean continental, and elevated smoke subtypes. As a consequence of the V4 updates, the mean 532 nm AOD retrieved by CALIOP has increased by 0.044 (0.036) or 52 % (40 %) for nighttime (daytime). Lidar ratio revisions are the most influential factor for AOD changes from V3 to V4, especially for cloud-free skies. Preliminary validation studies show that the AOD discrepancies between CALIOP and AERONET–MODIS (ocean) are reduced in V4 compared to V3.

Every night across the world’s oceans, numerous marine animals arrive at the surface of the ocean to feed on plankton after an upward migration of hundreds of metres. Just before sunrise, this migration is reversed and the animals return to their daytime residence in the dark mesopelagic zone (at a depth of 200–1,000 m). This daily excursion, referred to as diel vertical migration (DVM), is thought of primarily as an adaptation to avoid visual predators in the sunlit surface layer1,2 and was first recorded using ship-net hauls nearly 200 years ago3. Nowadays, DVMs are routinely recorded by ship-mounted acoustic systems (for example, acoustic Doppler current profilers). These data show that night-time arrival and departure times are highly conserved across ocean regions4 and that daytime descent depths increase with water clarity4,5, indicating that animals have faster swimming speeds in clearer waters4. However, after decades of acoustic measurements, vast ocean areas remain unsampled and places for which data are available typically provide information for only a few months, resulting in an incomplete understanding of DVMs. Addressing this issue is important, because DVMs have a crucial role in global ocean biogeochemistry. Night-time feeding at the surface and daytime metabolism of this food at depth provide an efficient pathway for carbon and nutrient export6–8. Here we use observations from a satellite-mounted light-detection-and-ranging (lidar) instrument to describe global distributions of an optical signal from DVM animals that arrive in the surface ocean at night. Our findings reveal that these animals generally constitute a greater fraction of total plankton abundance in the clear subtropical gyres, consistent with the idea that the avoidance of visual predators is an important life strategy in these regions. Total DVM biomass, on the other hand, is higher in more productive regions in which the availability of food is increased. Furthermore, the 10-year satellite record reveals significant temporal trends in DVM biomass and correlated variations in DVM biomass and surface productivity. These results provide a detailed view of DVM activities globally and a path for refining the quantification of their biogeochemical importance. Satellite-derived analysis of daily vertical migrations of ocean animals shows that the relative abundance and total biomass of these animals differ between different regions globally, depending on the availability of food and necessity to avoid predators.

This study presents a new 10 year of liquid water cloud droplet number concentration (Nd) climatology, and analyzes its long-term variation on both regional and global scales based on accurate depolarization ratio measurement from CALIPSO and 3.7 μm cloud effective radius retrieval from MODIS. Compared with the widely used passive retrieval method (e.g., MODIS retrieval), which considers Nd as function of cloud optical depth, geometry thickness and effective radius, retrieval method of the new Nd dataset has a weak dependence upon the cloud adiabatic assumption and eliminates the possible bias caused by multilayer clouds. Statistical results show that the annual cycle and long-term variability of Nd retrieved by CALIPSO agree reasonably well with those obtained from MODIS retrieval method, especially over the stratocumulus regions (correlation coefficient >0.9). Multiple regression models and contribution calculation verify that the variability of sulfate mass concentration dominates the long-term variation of Nd over most regions, even though the contribution factors and rates vary with different regions, temperatures and methods. In addition, our study also indicates that the impact of BC and OC on Nd should not be ignored, especially for supercooled water clouds over those important biomass burning regions. These results demonstrate the temperature-dependent Nd climatology derived from CALIOP has potential to be beneficial to climate research and reduce the uncertainties in estimates of the aerosol indirect effect in the model simulations.

We present comparisons of cloud droplet size distributions (DSDs) retrieved from the research scanning polarimeter (RSP) data with correlative in situ measurements made during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). The airborne portion of this field experiment was based out of St. John's airport, Newfoundland, Canada with the focus of this paper being on the deployment in May–June 2016. RSP was onboard the NASA C-130 aircraft together with an array of in situ and other remote sensing instrumentation. The RSP is an along-track scanner measuring the polarized and total reflectance in 9 spectral channels. Its uniquely high angular resolution allows for characterization of liquid water droplet sizes using the rainbow structure observed in the polarized reflectance over the scattering angle range from 135° to 165°. The rainbow is dominated by single scattering of light by cloud droplets, so its structure is characteristic specifically of the droplet sizes at cloud top (within unit optical depth into the cloud, equivalent to approximately 50 m). A parametric fitting algorithm applied to the polarized reflectance provides retrievals of the droplet effective radius and variance assuming a prescribed size distribution shape (gamma distribution). In addition to this, we use a non-parametric method, the Rainbow Fourier Transform (RFT), which allows us to retrieve the droplet size distribution itself. The latter is important in the case of clouds with complex microphysical structure, or multiple layers of cloud, which result in multi-modal DSDs. During NAAMES the aircraft performed a number of flight patterns specifically designed for comparisons between remote sensing retrievals and in situ measurements. These patterns consisted of two flight segments above the same straight ground track. One of these segments was flown above clouds allowing for remote sensing measurements, while the other was near the cloud top where cloud droplets were sampled. We compare the DSDs retrieved from the RSP data with in situ measurements made by the Cloud Droplet Probe (CDP). The comparisons generally show good agreement (better than 1 μm for effective radius and in most cases better than 0.02 for effective variance) with deviations explainable by the position of the aircraft within the cloud, or by the presence of additional cloud layers between the cloud being sampled by the in situ instrumentation and the altitude of the remote sensing segment. In the latter case, the multi-modal DSDs retrieved from the RSP data were consistent with the multi-layer cloud structures observed in the correlative High Spectral Resolution Lidar (HSRL) profiles. The results of these comparisons provide a rare validation of polarimetric droplet size retrieval techniques, demonstrating their accuracy and robustness and the potential of satellite data of this kind on a global scale.

Comprehensive polarimetric closure is demonstrated using observations from two in-situ polarimeters and Vector Radiative Transfer (VRT) modeling. During the Ship-Aircraft Bio-Optical Research (SABOR) campaign, the novel CCNY HyperSAS-POL polarimeter was mounted on the bow of the R/V Endeavor and acquired hyperspectral measurements from just above the surface of the ocean, while the NASA GISS Research Scanning Polarimeter was deployed onboard the NASA LaRC’s King Air UC-12B aircraft. State-of-the-art, ancillary measurements were used to characterize the atmospheric and marine contributions in the VRT model, including those of the High Spectral Resolution Lidar (HSRL), the AErosol RObotic NETwork for Ocean Color (AERONET-OC), a profiling WETLabs ac-9 spectrometer and the Multi-spectral Volume Scattering Meter (MVSM). An open-ocean and a coastal scene are analyzed, both affected by complex aerosol conditions. In each of the two cases, it is found that the model is able to accurately reproduce the Stokes components measured simultaneously by each polarimeter at different geometries and viewing altitudes. These results are mostly encouraging, considering the different deployment strategies of RSP and HyperSAS-POL, which imply very different sensitivities to the atmospheric and ocean contributions, and open new opportunities in above-water polarimetric measurements. Furthermore, the signal originating from each scene was propagated to the top of the atmosphere to explore the sensitivity of polarimetric spaceborne observations to changes in the water type. As expected, adding polarization as a measurement capability benefits the detection of such changes, reinforcing the merits of the full-Stokes treatment in modeling the impact of atmospheric and oceanic constituents on remote sensing observations.

Photon sieves (PS) have many applications and various designs in focusing light. However, a traditional PS only has a light transmissivity up to ∼25% and a focusing efficiency up to ∼7%, which hinder the application of them in many fields, especially for satellite remote sensing. To overcome these inherent drawbacks of traditional PSs, a concept of reflective photon sieve is developed in this work. This reflective photon sieve is based on a transparent membrane backed by a mirror. The transparent membrane is optimally a fully transparent material sheet with given refractive index and designed geometric thickness which has an optical thickness of a quarter incident wavelength (i.e. an anti-reflective coating). The PS-patterned pinholes are made on the transparent membrane. The design makes the light reflected from pinholes and that from zones of membrane material have 180° phase difference. Thus, light incident on this optical device is reflected and focused on its focal point. This device can have a reflectivity of ∼100% and a focusing efficiency of ∼50% based on numerical simulation. This device functions similar to a concave focusing mirror but can preserve the phase feature of light (such as that for the light with orbital angular momentum). It also has excellent wavelength-dependent property, which can exclude most of the undesired light from the focal point. A thin sheet of this component can perform the joint function of lenses and gratings/etalons in the optical path of a remote sensing system, thus is suitable for controling/filtering light in compact instruments such as satellite sensors. This concept is validated by the finite-difference time domain (FDTD) modeling and a lab prototype in this study.

Laser peening of components with thin section is of great concern in industry to improve anti-fatigue properties. This study proposes alternate double-sided laser peening (ADLP) as an optional way for the surface treatment of thin section component to overcome the risk of internal spallation and facility cost in simultaneous double-sided laser peening (SDLP). Experiments are conducted on the special-designed samples with thin section to investigate the distortion behavior and residual stress under laser peening of both sides sequentially. Numerical model are developed to analyze the generation and recover of geometry distortion for ADLP process. The results shows that the geometry distortion after single-sided laser peening is observed to be sensitive to laser pulse energy to generate different profile directions. The feasibility of ADSP is proved for the treatment of thin section with distortion control because the second laser peening on the other side of thin section can eliminate the distortion. The generation of different distortion behaviors is found to be closely related to the plastic strain field generated by the transmission of shock-induced stress wave across the thickness section. The numerical simulation shows that a symmetric tensile plastic strain field in plane section is generated across the thickness section for each laser pulse energy after completing laser peening on both sides. Furthermore, both experiments and model prediction propose that compressive residual stress is induced on both sides of thin section by ADLP process, which ensures the ADLP process effective for the treatment of thin section.

The interaction properties between metal droplet and substrate play a crucial role in additive manufacturing technology based on depositing microdroplet. However, few studies have focused on the wettability and slippage between them due to the experimental observation challenge. In this work, molecular dynamics simulation is employed to investigate the effects of metal type, temperature, feature size, and driving force on contact angle and slip length and clarify their influence mechanisms. The results suggest that Ag has the smallest contact angle and slip length among Cu, Ag, and Au, and the more intense interaction between metal and quartz results in a smaller contact angle and slip length. The temperature has a weak influence on contact angle and slip length, and both of them decrease slightly with increasing temperature. The slip length rises rapidly and then trends to a constant when the feature size becomes large, which can be interpreted by the change in apparent viscosity of molten metal near the quartz. Furthermore, the slip length is proportional to the driving force, while it will reach a plateau when the driving force is considerably large. These conclusions can provide theoretical guidance and reference for continuous medium simulation.

This study investigated the influence of multiple laser shock peening on microstructure, crystallographic texture, and pitting corrosion behavior of Aluminum-Lithium alloy 2060-T8 (in 3.5% NaCl) using EBSD, SEM and XPS techniques. Results show that microstructure and crystallographic texture were noticeably influenced by the peening passes. At single peening, plastic strain accommodation was homogenous, resulting in higher fraction of low Σ CSL boundaries, and significant crystallographic texture transformation from fibre (unpeened) into Cube {001}〈100〉 component (corresponding to dynamic recovery as plastic deformation mode) was induced. In contrast, repetitive peening (3 & 5 passes) induced heterogeneous plastic strain accommodation, developed random and low angle grain boundaries, and texture transformation from fibre to Goss {011}〈100〉, implying recrystallization as corresponding plastic deformation mode. As a result of dense atomic structured microstructures/textures, pitting corrosion after single peening was significantly lowered than that of multiple peening. Major reason for the improved corrosion behavior at single peening pass was the formation of dense and uniform α-Al2O3 type oxide film, which significantly lowered the materials dissolution. Whereas, multiple peening led to the formation of porous γ-Al2O3, θ-Al2O3 oxides and Cu-rich regions, which activated the deeper and wider pitting. Besides, the effect of precipitates (Al2CuLi, θ′ (Al2Cu) and β′ (Al3Zr)) on pitting corrosion is quantitatively examined.