Retrieval Of Ice Cloud Properties Research Articles

On the Consistency of Ice Cloud Optical Models for Spaceborne Remote Sensing Applications and Broadband Radiative Transfer Simulations

AbstractAqua satellite Moderate Resolution Imaging Spectroradiometer (MODIS) 1‐km observations are collocated with Clouds and the Earth's Radiant Energy System (CERES) fields of view taken during July 2008 afternoon satellite passes over the equatorial western Pacific Ocean. Radiation simulations are compared with collocated CERES observations to better understand the sensitivity of computed fluxes to two ice cloud broadband radiation parameterization schemes and inferred ice cloud characteristics. In particular, the radiation computational schemes and ice cloud property retrievals are based on two respective ice particle models, the MODIS Collection 6 (MC6) aggregate model and a more microphysically consistent two‐habit model (THM). The simulation results show that both MC6 and THM overestimate the shortwave (SW) and longwave (LW) cloud radiative effects at the top of the atmosphere, as compared to the CERES observations; the difference between the MC6 and THM‐based ice cloud retrievals is too small to compensate for the differences between the two model‐based radiation schemes. Therefore, the present finding suggests that broadband radiative simulations are more sensitive to the radiation parameterization scheme than to the input cloud properties retrieved using the corresponding ice cloud particle optical property model.

VLIDORT-QS: A quasi-spherical vector radiative transfer model

We present a new VLIDORT-based quasi-spherical vector radiative transfer (RT) model (VLIDORT-QS) that was developed for forward-model simulations of long-path measurements in limb- or nadir-viewing spherical-atmosphere configurations. This paper provides a theoretical description of the RT model, in which the single-scatter radiation field is treated accurately in spherical geometry, while the diffuse-scatter source terms at points along the line-of-sight are approximated locally through pseudo-spherical simulations based on the VLIDORT RT code; these multiple scatter source terms are then folded into the source function integration to yield the radiation field at any line-of-sight observation point in the atmosphere. In addition, the VLIDORT-QS model has a complete linearization facility (ability to generate analytic Jacobians of the radiation field with respect to any atmospheric or surface property). Validation of the model has been carried out against a fully-spherical Monte Carlo code, and also against the standard VLIDORT model for nadir-view scenarios with relatively low sphericity effects. A sample application of VLIDORT-QS for a biomass-burning plume scenario is demonstrated. A second paper will present a detailed analysis of the retrieval of ice-cloud properties from high-altitude aircraft measurements taken as part of the NASA Airborne Tropical Tropopause Experiment (ATTREX) campaign, with VLIDORT-QS providing forward-model simulations of the radiation fields and associated Jacobian functions.

Spaceborne Middle‐ and Far‐Infrared Observations Improving Nighttime Ice Cloud Property Retrievals

AbstractTwo upcoming missions are scheduled to provide novel spaceborne observations of upwelling far‐infrared spectra. In this study, the accuracy of ice cloud property retrievals using spaceborne middle‐to‐far‐infrared (MIR‐FIR) measurements is examined toward a better understanding of retrieval biases and uncertainties. Theoretical sensitivity studies demonstrate that the MIR‐FIR spectra are sensitive to ice cloud properties, thereby providing a robust means for retrieving cloud properties under nighttime conditions. However, the temperature dependence of the ice refractive index and relevant ice particle shape models need to be incorporated into the retrieval procedure to avoid systematic biases in inferring cloud optical thickness and effective particle radius. Furthermore, prior information of subpixel cloud fractions is essential to mitigation of substantial systematic retrieval biases due to inconsistent subpixel cloud fractions.

Information Content of Ice Cloud Properties from Multi-Spectral, -Angle and -Polarization Observations

Ice clouds play an important role in the Earth’s radiation budget, while their microphysical and optical properties remain one of the major uncertainties in remote sensing and atmospheric studies. Many satellite-based multi-spectral, -angle and -polarization instruments have been launched in recent years, and it is unclear how these observations can be used to improve the understanding of ice cloud properties. This study discusses the impacts of multi-spectral, -angle and -polarization observations on ice cloud property retrievals by performing a theoretical information content (IC) analysis. Ice cloud properties, including the cloud optical thickness (COT), particle effective radius (Re) and particle habit (defined by the aspect ratio (AR) and the degree of surface roughness level (σ)), are considered. An accurate polarized radiative transfer model is used to simulate the top-of-atmosphere intensity and polarized observations at the cloud-detecting wavelengths of interest. The ice cloud property retrieval accuracy should be improved with the additional information from multi-spectral, -angle and -polarization observations, which is verified by the increased degrees of freedom for signal (DFS). Polarization observations at spectral wavelengths (i.e., 0.87 and 2.13 µm) are helpful in the improvement of ice cloud property retrievals, especially for small-sized particles. An optimal scheme to retrieve ice cloud properties is to comprise radiance intensity information at the 0.87, 1.24, 1.64 and 2.13 µm channels and polarization information (the degree of linear polarization, DOLP) at the 0.87 and 2.13 µm channels. As observations from multiple angles added, DFS clearly increases, while it becomes almost saturated when the number of angles reaches three. Besides, the retrieval of Re exhibits larger uncertainties, and the improvement in total DFS by adding multi-spectral, -angle and -polarization observations is mainly attributed to the improvement of Re retrieval. Our findings will benefit the future instrument design and the improvement in cloud property retrieval algorithms based on multi-spectral, -angle, and -polarization imagers.

An Improved Beta Method for Ice Cloud Property Retrievals: Theory

AbstractThe beta method has long been used for ice cloud property retrievals with the utilization of thermal infrared observations. The method assumes an idealized cloud that is isothermal and does not scatter radiation, which results in systematic biases in ice cloud property retrievals. We improve the beta method by taking into account, for the first time since the beta method was developed, a nonisothermal cloud layer and the cloud scattering effects. The improved beta method provides almost unbiased cloud optical thickness retrievals for typical cirrus clouds and reduces the systematic biases of effective particle radius retrievals for optically thin clouds. The improved beta method does not involve either Jacobian or iterative radiative transfer calculations. As a result, this method is computationally efficient, robustly applicable to any historical and current spaceborne thermal infrared observations, and capable of processing a large amount of satellite data.

Dependence of Mass–Dimensional Relationships on Median Mass Diameter

Retrievals of ice cloud properties require accurate estimates of ice particle mass. Empirical mass–dimensional (m–D) relationships in the form m = a D b are widely used and usually universally applied across the complete range of particle sizes. For the first time, the dependence of a and b coefficients in m–D relationships on median mass diameter (Dmm) is studied. Using combined cloud microphysical data collected during the Olympic Mountains Experiment and coincident observations from Airborne Precipitation Radar Third Generation, Dmm-dependent (a, b) coefficients are derived and represented as surfaces of equally plausible solutions determined by some tolerance in the chi-squared difference χ 2 that minimizes the difference between observed and retrieved radar reflectivity. Robust dependences of a and b on Dmm are shown with both parameters significantly decreasing with Dmm, leading to smaller effective densities for larger Dmm ranges. A universally applied constant m–D relationship overestimates the mass of large aggregates when Dmm is between 3–6 mm and temperatures are between −15–0 °C. Multiple m–D relations should be applied for different Dmm ranges in retrievals and simulations to account for the variability of particle sizes that are responsible for the mass and thus for the variability of particle shapes and densities.

Sensitivity Analyses for the Retrievals of Ice Cloud Properties From Radiometric and Polarimetric Measurements in Sub‐mm/mm and Infrared Bands

AbstractSimulations of the radiometric and polarimetric properties of ice clouds based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 ice cloud model (aggregates in conjunction with the gamma distribution) are performed at wavelengths in the Infrared (IR) and Submillimeter/Millimeter (sub‐mm/mm) regimes. The scattering/absorption/polarization properties from the precalculated database are incorporated into a radiative transfer model, the Atmospheric Radiative Transfer Model (ARTS). A number of sub‐mm/mm bands have been considered from radiometric and polarimetric studies for potential applications to retrieving ice cloud properties at shorter sub‐mm/mm wavelengths (higher frequencies). The IR atmospheric window has also been thoroughly investigated for its feasibility to observe ice clouds. However, in the literature there is a lack of studies utilizing these high frequency bands or polarimetric IR observations to infer ice cloud properties. This study focuses on performing simulations of ice clouds between 680 and 183 GHz (sub‐mm/mm regime) and between 8.6 and 12 μm wavelengths (IR‐window regime) toward determining potential applications to simultaneous retrievals of ice water path (IWP) and effective diameter (Deff) with a primary focus on optically thin cirrus clouds. IWP and Deff isoline plots (lookup tables) are created to conduct sensitivity analyses and to perform preliminary retrievals. Results reveal that the utilization of both sub‐mm/mm and IR wavelengths combined with linearly polarized and unpolarized measurements leads to the most accurate IWP and Deff retrievals with errors lower than 10%.

Footprint-scale cloud type mixtures and their impacts on Atmospheric Infrared Sounder cloud property retrievals

Abstract. A method is described to classify cloud mixtures of cloud top types, termed cloud scenes, using cloud type classification derived from the CloudSat radar (2B-CLDCLASS). The scale dependence of the cloud scenes is quantified. For spatial scales at 45 km (15 km), only 18 (10) out of 256 possible cloud scenes account for 90 % of all observations and contain one, two, or three cloud types. The number of possible cloud scenes is shown to depend on spatial scale with a maximum number of 210 out of 256 possible scenes at a scale of 105 km and fewer cloud scenes at smaller and larger scales. The cloud scenes are used to assess the characteristics of spatially collocated Atmospheric Infrared Sounder (AIRS) thermodynamic-phase and ice cloud property retrievals within scenes of varying cloud type complexity. The likelihood of ice and liquid-phase detection strongly depends on the CloudSat-identified cloud scene type collocated with the AIRS footprint. Cloud scenes primarily consisting of cirrus, nimbostratus, altostratus, and deep convection are dominated by ice-phase detection, while stratocumulus, cumulus, and altocumulus are dominated by liquid- and undetermined-phase detection. Ice cloud particle size and optical thickness are largest for cloud scenes containing deep convection and cumulus and are smallest for cirrus. Cloud scenes with multiple cloud types have small reductions in information content and slightly higher residuals of observed and modeled radiance compared to cloud scenes with single cloud types. These results will help advance the development of temperature, specific humidity, and cloud property retrievals from hyperspectral infrared sounders that include cloud microphysics in forward radiative transfer models.

Multi-Channel Regression Inversion Method for Passive Remote Sensing of Ice Water Path in the Terahertz Band

Retrieval of ice cloud properties using passive terahertz wave radiometer from space has gained increasing attention currently. A multi-channel regression inversion method for passive remote sensing of ice water path (IWP) in the terahertz band is presented. The characteristics of the upward terahertz radiation in the clear-sky and cloudy-sky are first analyzed using the Atmospheric Radiative Transfer Simulator (ARTS). Nine representative center frequencies with different offsets are selected to study the changes of terahertz radiation caused by microphysical parameters of ice clouds. Then, multiple linear regression method is applied to the inversion of IWP. Combinations of different channels are selected for regression to eliminate the influence of other factors (i.e., particle size and cloud height). The optimal fitting equation are obtained by the stepwise regression method using two oxygen absorption channels (118.75 ± 1.1 GHz, 118.75 ± 3.0 GHz), two water vapor absorption channels (183.31 ± 1.0 GHz, 183.31 ± 7.0 GHz), and two window channels (243.20 ± 2.5 GHz, 874.4 ± 6.0 GHz). Finally, the errors of the proposed inversion method are evaluated. The simulation results show that the absolute errors of this method for the low IWP cases are below 7 g/m2, and the relative errors for the high IWP cases are generally ranging from 10 to 30%, indicating that the multi-channel regression inversion method can achieve satisfactory accuracy.

A Review of Ice Cloud Optical Property Models for Passive Satellite Remote Sensing

The current wealth of spaceborne passive and active measurements from ultraviolet to the infrared wavelengths provides an unprecedented opportunity to construct ice cloud bulk optical property models that lead to consistent ice cloud property retrievals across multiple sensors and platforms. To infer the microphysical and radiative properties of ice clouds from these satellite measurements, the general approach is to assume an ice cloud optical property model that implicitly assumes the habit (shape) and size distributions of the ice particles in these clouds. The assumption is that this ice optical property model will be adequate for global retrievals. In this review paper, we first summarize the key optical properties of individual particles and then the bulk radiative properties of their ensemble, followed by a review of the ice cloud models developed for application to satellite remote sensing. We illustrate that the random orientation condition assumed for ice particles is arguably justified for passive remote sensing applications based on radiometric measurements. The focus of the present discussion is on the ice models used by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Clouds and Earth’s Radiant Energy System (CERES) science teams. In addition, we briefly review the ice cloud models adopted by the Polarization and Directionality of the Earth’s Reflectance (POLDER) and the Himawari-8 Advanced Himawari Imager (AHI) for ice cloud retrievals. We find that both the MODIS Collection 6 ice model and the CERES two-habit model result in spectrally consistent retrievals.

Inference of an Optimal Ice Particle Model through Latitudinal Analysis of MISR and MODIS Data

The inference of ice cloud properties from remote sensing data depends on the assumed forward ice particle model, as they are used in the radiative transfer simulations that are part of the retrieval process. The Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 (MC6) ice cloud property retrievals are produced in conjunction with a single-habit ice particle model with a fixed degree of ice particle surface roughness (the MC6 model). In this study, we examine the MC6 model and five other ice models with either smoother or rougher surface textures to determine an optimal model to reproduce the angular variation of the radiation field sampled by the Multi-angle Imaging Spectroradiometer (MISR) as a function of latitude. The spherical albedo difference (SAD) method is used to infer an optimal ice particle model. The method is applied to collocated MISR and MODIS data over ocean for clouds with temperatures ≤233 K during December solstice from 2012–2015. The range of solar zenith angles covered by the MISR cameras is broader at the solstices than at other times of the year, with fewer scattering angles associated with sun glint during the December solstice than the June solstice. The results suggest a latitudinal dependence in an optimal ice particle model, and an additional dependence on the solar zenith angle (SZA) at the time of the observations. The MC6 model is one of the most optimal models on the global scale. In further analysis, the results are filtered by a cloud heterogeneity index to investigate cloudy scenarios that are less susceptible to potential 3D effects. Compared to results for global data, the consistency between measurements and a given model can be distinguished in both the tropics and extra-tropics. The SAD analysis suggests that the optimal model for thick homogeneous clouds corresponds to more roughened ice particles in the tropics than in the extra-tropics. While the MC6 model is one of the models most consistent with the global data, it may not be the most optimal model for the tropics.

The retrieval of ice cloud parameters from multi-spectral satellite observations of reflectance using a modified XBAER algorithm

Retrieval of ice cloud properties from passive remote satellite instruments using multispectral observations of the upwelling radiance at the top of atmosphere is challenging. This requires an accurate separation of the surface spectral reflectance below the cloud and that coming from the ice cloud particle. In this paper, a weighting-function based method is described, which retrieves ice cloud optical thickness (COT) and ice cloud crystal effective radius (CER). The retrieval algorithm belongs to the newly developed eXtensible Bremen Aerosol Retrieval (XBAER) algorithm family, and is called XBAER-ice. The XBAER-ice retrieval uses a surface reflectance parameterization minimizing the impact of surface scattering on the retrieval of optically thin ice cloud properties. A fractal model is used to describe the ice cloud optical properties. The comparison of COT and CER between XBAER-ice and latest Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products shows good agreement. We note that ice cloud property retrievals are particularly sensitive to assumptions about the ice particle shape. The comparison between satellite derived COTs using both the MODIS C6 product and XBAER-ice algorithm agree well with Atmospheric Radiation Measurement (ARM) in-situ measurements (with correlation coefficient above 0.9) whereas CERs show poorer agreement (~0.6). The global spatial distribution patterns and values of COT from ice clouds retrieved by using the MODIS C6 and XBAER-ice algorithms show a high degree of consistency. The XBAER-ice derived ice cloud CER values are systematically smaller than the MODIS C6 product, but both products have similar spatial distribution patterns. The XBAER-ice offers a potential alternative algorithm for the synergistic operational retrieval of ice cloud properties from the observations of Ocean and Land Colour Instrument (OLCI) and Sea and Land Surface Temperature Radiometer (SLSTR) onboard Sentinel-3.

Impact of Ice Cloud Microphysics on Satellite Cloud Retrievals and Broadband Flux Radiative Transfer Model Calculations

Ice cloud particles exhibit a range of shapes and sizes affecting a cloud’s single-scattering properties. Because they cannot be inferred from passive visible/infrared imager measurements, assumptions about the bulk single-scattering properties of ice clouds are fundamental to satellite cloud retrievals and broadband radiative flux calculations. To examine the sensitivity to ice particle model assumptions, three sets of models are used in satellite imager retrievals of ice cloud fraction, thermodynamic phase, optical depth, effective height, and particle size, and in top-of-atmosphere (TOA) and surface broadband radiative flux calculations. The three ice particle models include smooth hexagonal ice columns (SMOOTH), roughened hexagonal ice columns, and a two-habit model (THM) comprising an ensemble of hexagonal columns and 20-element aggregates. While the choice of ice particle model has a negligible impact on daytime cloud fraction and thermodynamic phase, the global mean ice cloud optical depth retrieved from THM is smaller than from SMOOTH by 2.3 (28%), and the regional root-mean-square difference (RMSD) is 2.8 (32%). Effective radii derived from THM are 3.9 μm (16%) smaller than SMOOTH values and the RMSD is 5.2 μm (21%). In contrast, the regional RMSD in TOA and surface flux between THM and SMOOTH is only 1% in the shortwave and 0.3% in the longwave when a consistent ice particle model is assumed in the cloud property retrievals and forward radiative transfer model calculations. Consequently, radiative fluxes derived using a consistent ice particle model assumption throughout provide a more robust reference for climate model evaluation compared to ice cloud property retrievals.

Investigation of ice particle habits to be used for ice cloud remote sensing for the GCOM-C satellite mission

Abstract. 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.

Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations. Part I: Forward model, error analysis, and information content.

An optimal estimation (OE) retrieval method is developed to infer three ice cloud properties simultaneously: optical thickness (τ), effective radius (reff ), and cloud-top height (h). This method is based on a fast radiative transfer (RT) model and infrared (IR) observations from the MODerate resolution Imaging Spectroradiometer (MODIS). This study conducts thorough error and information content analyses to understand the error propagation and performance of retrievals from various MODIS band combinations under different cloud/atmosphere states. Specifically, the algorithm takes into account four error sources: measurement uncertainty, fast RT model uncertainty, uncertainties in ancillary datasets (e.g., atmospheric state), and assumed ice crystal habit uncertainties. It is found that the ancillary and ice crystal habit error sources dominate the MODIS IR retrieval uncertainty and cannot be ignored. The information content analysis shows that, for a given ice cloud, the use of four MODIS IR observations is sufficient to retrieve the three cloud properties. However, the selection of MODIS IR bands that provide the most information and their order of importance varies with both the ice cloud properties and the ambient atmospheric and the surface states. As a result, this study suggests the inclusion of all MODIS IR bands in practice since little a priori information is available.

Retrieval of Ice Cloud Properties from AIRS and MODIS Observations Based on a Fast High-Spectral-Resolution Radiative Transfer Model

AbstractA computationally efficient high-spectral-resolution cloudy-sky radiative transfer model (HRTM) in the thermal infrared region (700–1300 cm−1, 0.1 cm−1 spectral resolution) is advanced for simulating the upwelling radiance at the top of atmosphere and for retrieving cloud properties. A precomputed transmittance database is generated for simulating the absorption contributed by up to seven major atmospheric absorptive gases (H2O, CO2, O3, O2, CH4, CO, and N2O) by using a rigorous line-by-line radiative transfer model (LBLRTM). Both the line absorption of individual gases and continuum absorption are included in the database. A high-spectral-resolution ice particle bulk scattering properties database is employed to simulate the radiation transfer within a vertically nonisothermal ice cloud layer. Inherent to HRTM are sensor spectral response functions that couple with high-spectral-resolution measurements in the thermal infrared regions from instruments such as the Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer. When compared with the LBLRTM and the discrete ordinates radiative transfer model (DISORT), the root-mean-square error of HRTM-simulated single-layer cloud brightness temperatures in the thermal infrared window region is generally smaller than 0.2 K. An ice cloud optical property retrieval scheme is developed using collocated AIRS and Moderate Resolution Imaging Spectroradiometer (MODIS) data. A retrieval method is proposed to take advantage of the high-spectral-resolution instrument. On the basis of the forward model and retrieval method, a case study is presented for the simultaneous retrieval of ice cloud optical thickness τ and effective particle size Deff that includes a cloud-top-altitude self-adjustment approach to improve consistency with simulations.

Simulations of Infrared Radiances over a Deep Convective Cloud System Observed during TC4: Potential for Enhancing Nocturnal Ice Cloud Retrievals

Retrievals of ice cloud properties using infrared measurements at 3.7, 6.7, 7.3, 8.5, 10.8, and 12.0 mm can provide consistent results regardless of solar illumination, but are limited to cloud optical thicknesses t < ~6. This paper investigates the variations in radiances at these wavelengths over a deep convective cloud system for their potential to extend retrievals of t and ice particle size De to optically thick clouds. Measurements from an imager, an interferometer, the Cloud Physics Lidar (CPL), and the Cloud Radar System (CRS) aboard the NASA ER-2 aircraft during the NASA TC4 (Tropical Composition, Cloud and Climate Coupling) experiment flight during 5 August 2007, are used to examine the retrieval potential of infrared radiances over optically thick ice clouds. Simulations based on coincident in situ measurements and combined cloud t from CRS and CPL measurements are comparable to the observations. They reveal that brightness temperatures at these bands and their differences (BTD) are sensitive to t up to ~20 and that for ice clouds having t > 20, the 3.7–10.8 µm and 3.7–6.7 µm BTDs are the most sensitive to De. Satellite imagery appears to be consistent with these results suggesting that t and De could be retrieved for greater optical thicknesses than previously assumed. But, because of sensitivity of the BTDs to uncertainties in the atmospheric profiles of temperature, humidity, and ice water content, and sensor noise, exploiting the small BTD signals in retrieval algorithms will be very challenging.

Determination of ice cloud models using MODIS and MISR data

Representation of ice clouds in radiative transfer simulations is subject to uncertainties associated with the shapes and sizes of ice crystals within cirrus clouds. In this study, we examined several ice cloud models consisting of smooth, roughened, homogeneous and inhomogeneous hexagonal ice crystals with various aspect ratios. The sensitivity of the bulk scattering properties and solar reflectances of cirrus clouds to specific ice cloud models is investigated using the improved geometric optics method (IGOM) and the discrete ordinates radiative transfer (DISORT) model. The ice crystal habit fractions in the ice cloud model may significantly affect the simulations of cloud reflectances. A new algorithm was developed to help determine an appropriate ice cloud model for application to the satellite-based retrieval of ice cloud properties. The ice cloud particle size retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) data, collocated with Multi-angle Imaging Spectroradiometer (MISR) observations, is used to infer the optical thicknesses of ice clouds for nine MISR viewing angles. The relative differences between view-dependent cloud optical thickness and the averaged value over the nine MISR viewing angles can vary from −0.5 to 0.5 and are used to evaluate the ice cloud models. In the case for 2 July 2009, the ice cloud model with mixed ice crystal habits is the best fit to the observations (the root mean square (RMS) error of cloud optical thickness reaches 0.365). This ice cloud model also produces consistent cloud property retrievals for the nine MISR viewing configurations within the measurement uncertainties.

Improved Representation of Ice Particle Masses Based on Observations in Natural Clouds

Abstract The mass–dimensional relationship put forth by Brown and Francis has been widely used for developing parameterizations for representing ice cloud microphysical properties. This relationship forms the cornerstone for past and forthcoming retrievals of ice cloud properties from ground-based and spaceborne active and passive sensors but has yet to be rigorously evaluated. This study uses data from six field campaigns to evaluate this mass–dimensional relationship in a variety of ice cloud types and temperatures and to account for the deviations observed with temperature and size, based on properties of the ice particle ensembles. Although the Brown and Francis relationship provides a good match to the observations in a mean sense, it fails to capture dependences on temperature and particle size that are a result of the complex microphysical processes operative within most ice cloud layers. Mass–dimensional relationships that provide a better fit to the observations are developed.

Effects of ice particle size vertical inhomogeneity on the passive remote sensing of ice clouds

The solar reflectance bi‐spectral (SRBS) and infrared split‐window (IRSpW) methods are two of the most popular techniques for passive ice cloud property retrievals from multispectral imagers. Ice clouds are usually assumed to be vertically homogeneous in global operational algorithms based on these methods, although significant vertical variations of ice particle size are typically observed in ice clouds. In this study we investigate uncertainties in retrieved optical thickness, effective particle size, and ice water path introduced by a homogeneous cloud assumption in both the SRBS and IRSpW methods, and focus on whether the assumption can lead to significant discrepancies between the two methods. The study simulates the upwelling spectral radiance associated with vertically structured clouds and passes the results through representative SRBS and IRSpW retrieval algorithms. Cloud optical thickness is limited to values for which IRSpW retrievals are possible (optical thickness less than about 7). When the ice cloud is optically thin and yet has a significant ice particle size vertical variation, it is found that both methods tend to underestimate the effective radius and ice water path. The reason for the underestimation is the nonlinear dependence of ice particle scattering properties (extinction and single scattering albedo) on the effective radius. Because the nonlinearity effect is stronger in the IRSpW than the SRBS method, the IRSpW‐based IWP tends to be smaller than the SRBS counterpart. When the ice cloud is moderately optically thick, the IRSpW method is relatively insensitive to cloud vertical structure and effective radius retrieval is weighted toward smaller ice particle size, while the weighting function makes the SRBS method more sensitive to the ice particle size in the upper portion of the cloud. As a result, when ice particle size increases monotonically toward cloud base, the two methods are in qualitative agreement; in the event that ice particle size decreases toward cloud base, the effective radius and ice water path retrievals based on the SRBS method are substantially larger than those from the IRSpW. The main findings of this study suggest that the homogenous cloud assumption can affect the SRBS and IRSpW methods to different extents and, consequently, can lead to significantly different retrievals. Therefore caution should be taken when comparing and combining the ice cloud property retrievals from these two methods.