A database (TAMUdust2020) of the optical properties of irregular aerosol particles is developed for applications to radiative transfer simulations involving aerosols, particularly dust and volcanic ash particles. The particle shape model assumes an ensemble of irregular hexahedral geometries to mimic complex aerosol particle shapes in nature. State-of-the-art light scattering computational capabilities are employed to compute the single-scattering properties of these particles for wide ranges of values of the size parameter, the index of refraction, and the degree of sphericity. The database therefore is useful for various radiative transfer applications over a broad spectral region from ultraviolet to infrared. Overall, agreement between simulations and laboratory/in-situ measurements is achieved for the scattering phase matrix and backscattering of various dust aerosol and volcanic ash particles. Radiative transfer simulations of active and passive spaceborne sensor signals for dust plumes with various aerosol optical depths and the effective particle sizes clearly demonstrate the applicability of the database for aerosol studies. In particular, the present database includes, for the first time, robust backscattering of nonspherical particles spanning the entire range of aerosol particle sizes, which shall be useful to appropriately interpret lidar signals related to the physical properties of aerosol plumes. Furthermore, thermal infrared simulations based on in-situ measured refractive indices of dust aerosol particles manifest the effects of the regional variations of aerosol optical properties. This database includes a user-friendly interface to obtain user-customized aerosol single-scattering properties with respect to spectrally dependent complex refractive index, size, and the degree of sphericity.

An algorithm for retrieving the macroscopic, physical, and optical properties of clouds from thermal infrared measurements is applied to the Himawari-8 multiband observations. A sensitivity study demonstrates that the addition of the single CO2 band of Himawari-8 is effective for the estimation of cloud top height. For validation, retrieved cloud properties are compared systematically with collocated active remote sensing counterparts with small time lags. While retrievals agree reasonably for single-layer clouds, multilayer cloud systems with optically thin upper clouds overlying lower clouds are the major source of error in the present algorithm. Validation of cloud products is critical for identifying the characteristics, advantages, and limitation of each product and should be continued in the future. As an application example, data are analyzed for eight days in the vicinity of the New Guinea to study the diurnal cycle of the cloud system. The present cloud property analysis investigates cloud evolution through separation of different cloud types and reveals typical features of diurnal cycles related to the topography. Over land, middle clouds increase from 0900 to 1200 local solar time (LST), deep convective clouds develop rapidly during 1200-1700 LST with a subsequent increase in cirrus and cirrostratus cloud amounts. Over the ocean near coastlines, a broad peak of convective cloud fraction is seen in the early morning. The present study demonstrates the utility of frequent observations by Himawari-8 for life cycle study of cloud systems, owing to the ability to capture their continuous temporal variations.

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.

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 CO2 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.

Sensitivities of the backscattering properties to the microphysical properties (in particular, size and shape) of mineral dust aerosols are examined based on TAMUdust2020, a comprehensive single‐scattering property database of irregular aerosol particles. We develop the bulk mineral dust particle models based on size‐resolved particle ensembles with randomly distorted shapes and spectrally resolved complex refractive indices, which are constrained by using in situ observations reported in the literature. The light detection and ranging (lidar) ratio is more sensitive to particle shape than particle size, while the depolarization ratio depends strongly on particle size. The simulated bulk backscattering properties (i.e., the lidar ratio and the depolarization ratio) of typical mineral dust particles with effective radii of 0.5–3 µm are reasonably consistent with lidar observations made during several field campaigns. The present dust bulk optical property models are applicable to lidar‐based remote sensing of dust aerosol properties.

Most global aerosol models approximate dust as spherical particles, whereas most remote sensing retrieval algorithms approximate dust as spheroidal particles with a shape distribution that conflicts with measurements. These inconsistent and inaccurate shape assumptions generate biases in dust single-scattering properties. Here, we obtain dust single-scattering properties by approximating dust as triaxial ellipsoidal particles with observationally constrained shape distributions. We find that, relative to the ellipsoidal dust optics obtained here, the spherical dust optics used in most aerosol models underestimate dust single-scattering albedo, mass extinction efficiency, and asymmetry parameter for almost all dust sizes in both the shortwave and longwave spectra. We further find that the ellipsoidal dust optics are in substantially better agreement with observations of the scattering matrix and linear depolarization ratio than the spheroidal dust optics used in most retrieval algorithms. However, relative to observations, the ellipsoidal dust optics overestimate the lidar ratio by underestimating the backscattering intensity by a factor of ∼2. This occurs largely because the computational method used to simulate ellipsoidal dust optics (i.e., the improved geometric optics method) underestimates the backscattering intensity by a factor of ∼2 relative to other computational methods (e.g., the physical geometric optics method). We conclude that the ellipsoidal dust optics with observationally constrained shape distributions can help improve global aerosol models and possibly remote sensing retrieval algorithms that do not use the backscattering signal.