Snow albedo plays a critical role in the surface energy budget in snow-covered regions and is subject to large uncertainty due to variable physical and optical characteristics of snow. We develop an optically and microphysically consistent snow grain habit mixture (SGHM) model, aiming at an improved representation of bulk snow properties in conjunction with considering the particle size distribution, particle shape, and internally mixed black carbon (BC). Spectral snow albedos computed with two snow layers with the SGHM model implemented in an adding–doubling radiative transfer model agree with observations. Top-snow-layer optical properties essentially determine spectral snow albedo when the top-layer snow water equivalent (SWE) is large. When the top-layer SWE is less than 1 mm, the second-snow-layer optical properties have nonnegligible impacts on the albedo of the snow surface. Snow albedo enhancement with increasing solar zenith angle (SZA) largely depends on snow particle effective radius and also internally mixed BC. Based on the SGHM model and various sensitivity studies, single- and two-layer snow albedos are parameterized for six spectral bands used in NASA Langley Research Center’s modified Fu–Liou broadband radiative transfer model. Parameterized albedo is expressed as a function of snow particle effective radii of the two layers, SWE in the top layer, internally mixed BC mass fraction in both layers, and SZA. Both single-layer and two-layer parameterizations provide band-mean snow albedo consistent with rigorous calculations, achieving correlation coefficients close to 0.99 for all bands.

Satellite remote sensing of the macroscopic, microphysical, and optical properties of clouds are useful for studying spatial and temporal variations of clouds at various scales and constraining cloud physical processes in climate and weather prediction models. Instead of using separate independent algorithms for different cloud properties, a unified, optimal estimation-based cloud retrieval algorithm is developed and applied to moderate resolution imaging spectroradiometer (MODIS) observations using ten thermal infrared bands. The model considers sensor configurations, background surface and atmospheric profile, and microphysical and optical models of ice and liquid cloud particles and radiative transfer in a plane-parallel, multilayered atmosphere. Measurement and model errors are thoroughly quantified from direct comparisons of clear-sky observations over the ocean with model calculations. Performance tests by retrieval simulations show that ice cloud properties are retrieved with high accuracy when cloud optical thickness (COT) is between 0.1 and 10. Cloud-top pressure is inferred with uncertainty lower than 10 % when COT is larger than 0.3. Applying the method to a tropical cloud system and comparing the results with the MODIS Collection 6 cloud product shows good agreement for ice cloud optical thickness when COT is less than about 5. Cloud-top height agrees well with estimates obtained by the CO 2 slicing method used in the MODIS product. The present algorithm can detect optically thin parts at the edges of high clouds well in comparison with the MODIS product, in which these parts are recognized as low clouds by the infrared window method. The cloud thermodynamic phase in the present algorithm is constrained by cloud-top temperature, which tends not to produce results with an ice cloud that is too warm and liquid cloud that is too cold.

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.

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