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Structural patterns of cloud effective radius (ER) and liquid water content (LWC) profiles are essential variables of cloud lifecycle and precipitation processes, while observing cloud profiles from passive remote-sensing sensors remains highly challenging. Understanding whether there are typical structural patterns of ER and LWC profiles in liquid clouds and how they are linked to cloud entrainment or precipitating status is critical in developing algorithms to derive cloud profiles from passive satellite sensors. This study aims to address these questions and provide a preliminary foundation for the development of liquid cloud profile retrievals for the Multi-viewing, Multi-channel and Multi-polarization Imaging (3MI) sensor aboard the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Polar System-Second Generation (EPS-SG) satellite, which is scheduled to be launched in 2025. Firstly, we simulate a large ensemble of stratocumulus cloud profiles using the Colorado State University (CSU) Regional Atmospheric Modeling System (RAMS). The empirical orthogonal function (EOF) analysis is adopted to describe the shape of simulated profiles with a limited number of elemental profile variations. Our results indicate that the first three EOFs of LWC and ER profiles can explain >90 % of LWC and ER profiles. The profiles are divided into four prominent patterns and all of these patterns can be simplified as triangle-shaped polylines. The frequency of these four patterns is found to relate to intensities of the cloud-top entrainment and precipitation. Based on these analyses, we propose a simplified triangle-shaped cloud profile parameterization scheme allowing us to represent these main patterns of LWC and ER. This simple yet physically realistic analytical model of cloud profiles is expected to facilitate the representation of cloud properties in advanced retrieval algorithms such as those developed for the 3MI/EPS-SG.

In this study, various ice particle habits are investigated in conjunction with inferring the optical properties of ice clouds for use in the Global Change Observation Mission-Climate (GCOM-C) satellite programme. We develop a database of the single-scattering properties of five ice habit models: plates, columns, droxtals, bullet rosettes, and Voronoi. The database is based on the specification of the Second Generation Global Imager (SGLI) sensor on board the GCOM-C satellite, which is scheduled to be launched in 2017 by the Japan Aerospace Exploration Agency. A combination of the finite-difference time-domain method, the geometric optics integral equation technique, and the geometric optics method is applied to compute the single-scattering properties of the selected ice particle habits at 36 wavelengths, from the visible to the infrared spectral regions. This covers the SGLI channels for the size parameter, which is defined as a single-particle radius of an equivalent volume sphere, ranging between 6 and 9000 µm. The database includes the extinction efficiency, absorption efficiency, average geometrical cross section, single-scattering albedo, asymmetry factor, size parameter of a volume-equivalent sphere, maximum distance from the centre of mass, particle volume, and six nonzero elements of the scattering phase matrix. The characteristics of calculated extinction efficiency, single-scattering albedo, and asymmetry factor of the five ice particle habits are compared. Furthermore, size-integrated bulk scattering properties for the five ice particle habit models are calculated from the single-scattering database and microphysical data. Using the five ice particle habit models, the optical thickness and spherical albedo of ice clouds are retrieved from the Polarization and Directionality of the Earth's Reflectances-3 (POLDER-3) measurements, recorded on board the Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) satellite. The optimal ice particle habit for retrieving the SGLI ice cloud properties is investigated by adopting the spherical albedo difference (SAD) method. It is found that the SAD is distributed stably due to the scattering angle increases for bullet rosettes with an effective diameter (Deff) of 10 µm and Voronoi particles with Deff values of 10, 60, and 100 µm. It is confirmed that the SAD of small bullet-rosette particles and all sizes of Voronoi particles has a low angular dependence, indicating that a combination of the bullet-rosette and Voronoi models is sufficient for retrieval of the ice cloud's spherical albedo and optical thickness as effective habit models for the SGLI sensor. Finally, SAD analysis based on the Voronoi habit model with moderate particle size (Deff = 60 µm) is compared with the conventional general habit mixture model, inhomogeneous hexagonal monocrystal model, five-plate aggregate model, and ensemble ice particle model. The Voronoi habit model is found to have an effect similar to that found in some conventional models for the retrieval of ice cloud properties from space-borne radiometric observations.

The degree of surface roughness of ice particles within thick, cold ice clouds is inferred from multi-directional, multi-spectral satellite polarimetric observations over oceans, assuming a column-aggregate particle habit. An improved roughness inference scheme is employed that provides a more noise-resilient roughness estimate than the conventional best-fit approach. The improvements include the introduction of a quantitative roughness parameter based on empirical orthogonal function analysis and proper treatment of polarization due to atmospheric scattering above clouds. A global 1-month data sample supports the use of a severely roughened ice habit to simulate the polarized reflectivity associated with ice clouds over ocean. The density distribution of the roughness parameter inferred from the global 1-month data sample and further analyses of a few case studies demonstrate the significant variability of ice cloud single-scattering properties. However, the present theoretical results do not agree with observations in the tropics. In the extratropics, the roughness parameter is inferred but 74 % of the sample is out of the expected parameter range. Potential improvements are discussed to enhance the depiction of the natural variability on a global scale.

The properties of low-level liquid clouds in the Arctic can be altered by long-range pollution transport to the region. Satellite, tracer transport model, and meteorological data sets are used here to determine a net aerosol–cloud interaction (ACInet) parameter that expresses the ratio of relative changes in cloud microphysical properties to relative variations in pollution concentrations while accounting for dry or wet scavenging of aerosols en route to the Arctic. For a period between 2008 and 2010, ACInet is calculated as a function of the cloud liquid water path, temperature, altitude, specific humidity, and lower tropospheric stability. For all data, ACInet averages 0.12 ± 0.02 for cloud-droplet effective radius and 0.16 ± 0.02 for cloud optical depth. It increases with specific humidity and lower tropospheric stability and is highest when pollution concentrations are low. Carefully controlling for meteorological conditions we find that the liquid water path of arctic clouds does not respond strongly to aerosols within pollution plumes. Or, not stratifying the data according to meteorological state can lead to artificially exaggerated calculations of the magnitude of the impacts of pollution on arctic clouds.

Cloud droplet number concentration (CDNC) is an important microphysical property of liquid clouds that impacts radiative forcing, precipitation and is pivotal for understanding cloud–aerosol interactions. Current studies of this parameter at global scales with satellite observations are still challenging, especially because retrieval algorithms developed for passive sensors (i.e., MODerate Resolution Imaging Spectroradiometer (MODIS)/Aqua) have to rely on the assumption of cloud adiabatic growth. The active sensor component of the A-Train constellation (i.e., Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)/CALIPSO) allows retrievals of CDNC from depolarization measurements at 532 nm. For such a case, the retrieval does not rely on the adiabatic assumption but instead must use a priori information on effective radius (re), which can be obtained from other passive sensors. In this paper, re values obtained from MODIS/Aqua and Polarization and Directionality of the Earth Reflectance (POLDER)/PARASOL (two passive sensors, components of the A-Train) are used to constrain CDNC retrievals from CALIOP. Intercomparison of CDNC products retrieved from MODIS and CALIOP sensors is performed, and the impacts of cloud entrainment, drizzling, horizontal heterogeneity and effective radius are discussed. By analyzing the strengths and weaknesses of different retrieval techniques, this study aims to better understand global CDNC distribution and eventually determine cloud structure and atmospheric conditions in which they develop. The improved understanding of CDNC can contribute to future studies of global cloud–aerosol–precipitation interaction and parameterization of clouds in global climate models (GCMs).

Ice clouds are an important element in the radiative balance of the earth's climate system, but their microphysical and optical properties still are not well constrained, especially ice particle habit and the degree of particle surface roughness. In situ observations have revealed common ice particle habits and evidence for surface roughness, but these observations are limited. An alternative is to infer the ice particle shape and surface roughness from satellite observations of polarized reflectivity since they are sensitive to both particle shape and degree of surface roughness. In this study an adding–doubling radiative transfer code is used to simulate polarized reflectivity for nine different ice habits and one habit mixture, along with 17 distinct levels of the surface roughness. A lookup table (LUT) is constructed from the simulation results and used to infer shape and surface roughness from PARASOL satellite polarized reflectivity data over the ocean. Globally, the retrievals yield a compact aggregate of columns as the most commonly retrieved ice habit. Analysis of PARASOL data from the tropics results in slightly more aggregates than in midlatitude or polar regions. Some level of surface roughness is inferred in nearly 70% of PARASOL data, with mean and median roughness near σ = 0.2 and 0.15, respectively. Tropical region analyses have 20% more pixels retrieved with particle surface roughness than in midlatitude or polar regions. The global asymmetry parameter inferred at a wavelength of 0.865 μm has a mean value of 0.77 and a median value of 0.75.

Total and polarized radiances provided by the Polarization and Directionality of Earth Reflectances (POLDER) satellite sensor are used to retrieve the microphysical and optical properties of the volcanic plume observed during the Eyjafjallajökull volcano eruption in 2010, over cloud-free and cloudy ocean scenes. We selected two plume conditions, fresh aerosols near the sources (three cases) and a downwind volcanic plume observed over the North Sea 30 h after its injection into the atmosphere (aged aerosols). In the near-source conditions, the aerosol properties depend on the distance to the plume. Within the near-source plume, aerosols are mainly non-spherical and in the coarse mode with an effective radius equal to 1.75 (±0.25) μm and an Ångström exponent (AE) close to 0.0. Far from the plume, in addition to the coarse mode, there are particles retrieved in the accumulation mode, suggesting a mixture of sulfate aerosols and volcanic dust, resulting in an AE around 0.8. The properties of the aerosols also depend on whether the plume is fresh or aged. For the downwind (aged) plume, if non-spherical coarse particles as well as some fine mode particles are retrieved, the AE is higher, around ~0.4. In addition, rather low values for the real part of the refractive index (RR) were retrieved for the fresh plume (1.38 < RR < 1.48). Single scattering albedo (SSA) values ranging between 0.92 and 0.98 were retrieved over some parts of the near-source plume; despite the low accuracy of our retrievals, the derived SSA values suggest that the ash particles are rather absorbing. To consider the particle shape, a combination of spheroid models was used. Although the employed model enabled accurate modeling of the POLDER signal in the case of non-spherical ash, our approach failed to model the signal over the optically thickest parts of the near-source plume. The most probable reason for this is the presence of ice crystals within the plume. For the aerosol above clouds (AAC) scenes, polarized measurements allowed the retrieval of the optical thickness (OT) and the AE of optically thin volcanic ash. We found that all the cloud parameters retrieved by passive sensors were biased due to the presence of the elevated volcanic plumes. Finally, thermal infrared measurements were used to identify the type of multilayer scene (cirrus clouds or volcanic dust above liquid clouds) and the retrieval method also provided the OT of thin cirrus layers above the clouds near Iceland.

Cloud optical thickness (COT) is one of the most important parameter for the characterization of cloud in the Earth radiative budget. Its retrieval strongly depends on instrument characteristics and on many cloud and environment factors. Using coincident observations from POLDER/PARASOL and MODIS/AQUA in the A-Train constellation, geographical distributions and seasonal changes of COT are presented, in good agreement with general cloud climatology characteristics. Retrieval uncertainties mainly associated to sensor spatial resolution, cloud inhomogeneity and microphysical assumptions are discussed. Comparisons of COT derived from POLDER and MODIS illustrate that as the primary factor, the sensor spatial resolution impacts COT retrievals and statistics through both cloud detection and sub-pixel cloud inhomogeneity sensitivity. The uncertainties associated to cloud microphysics assumptions, namely cloud phase, particle size and shape, also impact significantly COT retrievals. For clouds with unambiguous cloud phase, strong correlations exist between the two COTs, with MODIS values comparable to POLDER ones for liquid clouds and MODIS values larger than POLDER ones for ice clouds. The large differences observed in ice phase cases are due to the use of different microphysical models in the two retrieval schemes. In cases when the two sensors disagree on cloud phase decision, COT retrieved assuming liquid phase is systematically larger. The angular biases related to specific observation geometries are also quantified and discussed in particular based on POLDER observations. Those exhibit a clear increase of COT with decreasing sun elevation and a decrease of COT in forward scattering directions due to sub-pixel inhomogeneities and shadowing effects, this especially for lower sun. It also demonstrates unrealistic COT variations in the cloudbow and backward directions due to inappropriate cloud optical properties representation and an important increase of COT in the sun-glint directions in case of broken cloud.

This study explores the indirect effects of anthropogenic and biomass burning aerosols on Arctic clouds by co-locating a combination of MODIS and POLDER cloud products with output from the FLEXPART tracer transport model. During the activities of the International Polar Year for the Spring and Summer of 2008, we find a high sensitivity of Arctic cloud radiative properties to both anthropogenic and biomass burning pollution plumes, particularly at air temperatures near freezing or potential temperatures near 286 K. However, the sensitivity is much lower at both colder and warmer temperatures, possibly due to increases in the wet and dry scavenging of cloud condensation nuclei: the pollution plumes remain but the component that influences Arctic clouds has been removed along transport pathways. The analysis shows that, independent of local temperature, cloud optical depth is approximately four times more sensitive to changes in pollution levels than is cloud effective radius. This suggests that some form of feedback mechanism amplifies the radiative response of Arctic clouds to pollution through changes in cloud liquid water path.

The global spatial and diurnal distribution of cloud properties is a key issue for understanding the hydrological cycle, and critical for advancing efforts to improve numerical weather models and general circulation models. Satellite data provides the best way of gaining insight into global cloud properties. In particular, the determination of cloud thermodynamic phase is a critical first step in the process of inferring cloud optical and microphysical properties from satellite measurements. It is important that cloud phase be derived together with an estimate of the confidence of this determination, so that this information can be included with subsequent retrievals (optical thickness, effective particle radius, and ice/liquid water content). In this study, we combine three different and well documented approaches for inferring cloud phase into a single algorithm. The algorithm is applied to data obtained by the MODIS (MODerate resolution Imaging Spectroradiometer) and POLDER3 (Polarization and Directionality of the Earth Reflectance) instruments. It is shown that this synergistic algorithm can be used routinely to derive cloud phase along with an index that helps to discriminate ambiguous phase from confident phase cases. The resulting product provides a semi-continuous index ranging from confident liquid to confident ice instead of the usual discrete classification of liquid phase, ice phase, mixed phase (potential combination of ice and liquid particles), or simply unknown phase clouds. The index value provides simultaneously information on the phase and the associated confidence. This approach is expected to be useful for cloud assimilation and modeling efforts while providing more insight into the global cloud properties derived from satellite data.

The influence is investigated of the assumed ice particle microphysical and optical model on inferring ice cloud optical thickness (τ) from satellite measurements of the Earth's reflected shortwave radiance. Ice cloud τ are inferred, and subsequently compared, using products from MODIS (MODerate resolution Imaging Spectroradiometer) and POLDER (POLarization and Directionality of the Earth's Reflectances). POLDER τ values are found to be substantially smaller than those from collocated MODIS data. It is shown that this difference is caused primarily by the use of different ice particle bulk scattering models in the two retrievals, and more specifically, the scattering phase function. Furthermore, the influence of the ice particle model on the derivation of ice cloud radiative forcing (CRF) from satellite retrievals is studied. Three sets of shortwave CRF are calculated using different combinations of the retrieval and associated ice particle models. It is shown that the uncertainty associated with an ice particle model may lead to two types of errors in estimating CRF from satellite retrievals. One stems from the retrieval itself and the other is due to the optical properties, such as the asymmetry factor, used for CRF calculations. Although a comparison of the CRFs reveals that these two types of errors tend to cancel each other, significant differences are still found between the three CRFs, which indicates that the ice particle model affects not only optical thickness retrievals but also CRF calculations. In addition to CRF, the effect of the ice particle model on the derivation of seasonal variation of τ from satellite measurements is discussed. It is shown that optical thickness retrievals based on the same MODIS observations, but derived using different assumptions of the ice particle model, can be substantially different. These differences can be divided into two parts. The first-order difference is mainly caused by the differences in the asymmetry factor. The second-order difference is related to seasonal changes in the sampled scattering angles and therefore dependent on the sun-satellite viewing geometry. Because of this second-order difference, the use of different ice particle models may lead to a different understanding of the seasonal variation of τ.