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Investigations of the effects of clouds on Earth’s radiation budget demand accurate representations of cloud top parameters, which can be efficiently obtained by large-scale satellite remote sensing approaches. However, the insufficient utilization of multiband information is one of the major sources of uncertainty in cloud top products derived from geostationary satellites. In this study, we developed a new algorithm to estimate Cloud, Atmospheric Radiation and renewal Energy application (CARE) version 1.0 cloud top properties [cloud top height (CTH), cloud top pressure (CTP), and cloud top temperature (CTT)]. The algorithm is constructed from ten thermal spectral measurements in Himawari-8 observations by using the random forest (RF) method to comprehensively consider the contribution of each band to the cloud top parameters. We chose the highly accurate Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) products in 2018 as the true values. The sensitivity analysis demonstrated that the products can be fully reproduced by using multiple Himawari-8 channels with the addition of the digital elevation model (DEM) data. The validation results of the 2019 CALIOP data confirm that the new algorithm shows an effective performance, with correlation coefficients ($R$) of 0.89, 0.89, and 0.90 for CTH, CTP, and CTT, respectively. Moreover, a significant improvement in the ice cloud estimation is achieved, in which the CTT $R$ value increased from 0.46 to 0.70, as well as an improvement in the sea area, where the CTT $R$ value increased from 0.71 to 0.84 compared with the Himawari-8 products of the Japan Aerospace Exploration Agency (JAXA) P-tree system. The further analyses performed here capture the diurnal cycle of cloud top parameters well in different temporal scales over the Asia–Pacific region. [ABSTRACT FROM AUTHOR]

Visible and near-infrared reflected radiances have been used to estimate the cloud optical depth and effective radius of cloud-filled global area coverage (GAC) pixels from the Advanced Very High Resolution Radiometer (AVHRR) for two cases in the North Atlantic Ocean. One is representative of clouds having low concentrations of cloud condensation nuclei (CCN), while the other is an example of maritime clouds forming in continental air, in this case, intruding from Europe around a cutoff low pressure system. It is shown that an estimate of the cloud drop concentration can be obtained from remotely sensed cloud radiative properties and standard meteorological analyses. These concentrations show very clearly the influence of enhanced CCN on cloud microphysics. However, conclusions regarding the indirect radiative effect of aerosol on cloud must wait for the development of a framework for analyzing changes in cloud liquid water path (LWP). It is shown that estimates of LWP are greatly influenced by the scheme that is used to identify cloudy pixels at the AVHRR GAC resolution. Application of a very strict thermal channel spatial coherence criterion for identifying cloud-filled pixels yields mean LWP estimates for cloudy pixels alone that are 40%-75% higher than mean LWP estimates for the much larger sample of possibly cloudy pixels identified by a reflectance threshold criterion.

The Japan Meteorological Agency (JMA) successfully launched the Himawari-8 (H-8) new-generation geostationary meteorological satellite with the Advanced Himawari Imager (AHI) sensor on October 7, 2014. The H-8/AHI level-2 (L2) operational cloud property products were released by the Japan Aerospace Exploration Agency during September 2016. The Voronoi light scattering model, which is a fractal ice particle habit, was utilized to develop the retrieval algorithm called “Comprehensive Analysis Program for Cloud Optical Measurement” (CAPCOM-INV)-ice for the AHI ice cloud product. In this paper, we describe the CAPCOM-INV-ice algorithm for ice cloud products from AHI data. To investigate its retrieval performance, retrieval results were compared with 2000 samples of the ice cloud optical thickness and effective particle radius values. Furthermore, AHI ice cloud products are evaluated by comparing them with the MODIS collection-6 (C6) products. As an experiment, cloud property retrievals from AHI measurements, with an observation interval time of 2.5 min and ground-based rainfall observation radar data (the latter of which is supplied by the JMA, with a 1-km grid mesh), are used to investigate the generation processes of deep convective (DC) cloud in the vicinity of the Kyushu island, Japan. It revealed that AHI measurements have the capability of monitoring the growth processes, including variation of the cloud properties and the precipitation in the DC cloud. [ABSTRACT FROM AUTHOR]

This study examines the impact of in-cloud vertical inhomogeneity on cloud droplet effective radii (CDERs) of water-phase cloud retrieved from 1.6-, 2.1-, and 3.7- μm-band measurements (denoted by r1.6, r2.1, and r3.7, respectively). Discrepancies between r1.6, r2.1, and r3.7 due to in-cloud vertical inhomogeneity are simulated by using a spectral bin microphysics cloud model and one-dimensional (1D) remote sensing simulator under assumptions that cloud properties at the subpixel scale have horizontal homogeneity and 3D radiative transfer effects can be ignored. Two-dimensional weighting functions for the retrieved CDERs with respect to cloud optical depth and droplet size are introduced and estimated by least squares fitting to the relation between the model-simulated droplet size distribution functions and the retrieved CDERs. The results show that the 2D weighting functions can explain CDER discrepancies due to in-cloud vertical inhomogeneity and size spectrum characteristics. The difference between r1.6 and r2.1 is found to primarily depend on the vertical difference in droplet size distribution because the peak widths of their weighting functions differ in terms of cloud optical depth. The difference between r3.7 and r2.1, in contrast, is highly dependent on r2.1 because the magnitude of its weighting function is always greater than that of r3.7 over the entire range of optical depths and droplet sizes, except for the cloud top. The overestimation of retrieved CDER compared with in situ CDER in a typical adiabatic cloud case is also interpreted in terms of in-cloud vertical inhomogeneity based on the 2D weighting functions and simulation results. [ABSTRACT FROM AUTHOR]

This paper investigates the vertical structure of warm-cloud microphysical properties using a three-dimensional (3D) spectral bin microphysical model. A time series of contoured frequency by optical depth diagrams (CFODDs), which were proposed by previous studies, are calculated for the first time by a 3D model assuming two types of aerosol conditions (i.e., polluted and pristine). This contrasts with previous studies that obtained CFODDs using either a two-dimensional model or an accumulation of monthly and global observation data. The results show that the simulated CFODDs are characterized by distinctive patterns of radar reflectivities, similar to the patterns often observed by satellite remote sensing, even though the calculation domain of this study is limited to an area of 30 × 30 km2, whereas the satellite observations are of a global scale. A cloud microphysical box model is then applied to the simulated cloud field at each time step to identify the dominant process for each of the patterns. The results reveal that the wide variety of satellite-observed CFODD patterns can be attributed to different microphysical processes occurring in multiple cloud cells at various stages of the cloud life cycle. [ABSTRACT FROM AUTHOR]

This study describes an approach for combining CloudSat and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations to investigate the microphysical processes of warm clouds on the global scale. MODIS column optical thickness is vertically distributed between the cloud top and cloud bottom according to adiabatic and condensational growth assumptions and used as a vertical coordinate system to analyze profiles of CloudSat-observed radar reflectivity. The reflectivity profiles thus rescaled as a function of the in-cloud optical depth clearly depict how the cloud-to-rain particle growth processes take place within the cloud layer and how these processes vary systematically with variations in MODIS-derived effective particle radius. It is also found that the effective radii retrieved using two different wavelengths of MODIS tend to trace the microphysical change of reflectivity profiles in a different way because of the difference in the layer depth that characterizes these two effective radii. The reflectivity profiles as a function of optical depth are also interpreted in terms of drop collection processes based on the continuous collection model. The slope of the reflectivity change with optical depth provides a gross measure of the collection efficiency factor. The systematic changes of reflectivity profiles with MODIS-derived particle sizes are then interpreted as demonstrating a strong dependency of the collection efficiency on particle size. These results provide a quantitative insight into the drop collection process of warm clouds in the real atmosphere. [ABSTRACT FROM AUTHOR]

Hydrometeor droplet growth processes are inferred from a combination of Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) cloud particle size observations and CloudSat/Cloud Profiling Radar (CPR) observations of warm water clouds. This study supports the inferences of a related paper (Part I) (i) that MODIS-retrieved cloud droplet radii (CDR) from the 3.7- μm channel (R37) are influenced by the existence of small droplets at cloud top and (ii) that the CDR obtained from 1.6- (R16) and 2.1- μm (R21) channels contain information about drizzle droplets deeper into the cloud as well as cloud droplets. This interpretation is shown to be consistent with radar reflectivities when matched to CDR that were retrieved from MODIS data. This study demonstrates that the droplet growth process from cloud to rain via drizzle proceeds monotonically with the evolution of R16 or R21 from small cloud drops (on the order of 10–12 μm) to drizzle (CDR greater than 14 μm) to rain (CDR greater than 20 μm). Thus, R16 or R21 is an indicator of hydrometeor droplet growth processes whereas R37 does not contain information about coalescence. A new composite analysis, the contoured frequency diagram, is introduced to combine CloudSat/CPR reflectivity profiles and reveals a distinct trimodal population of reflectivities corresponding to cloud, drizzle, and rain modes. [ABSTRACT FROM AUTHOR]

This study examines the sensitivity of the retrieved cloud droplet radii (CDR) to the vertical inhomogeneity of droplet radii, including the existence of a drizzle mode in clouds. The focus of this study is warm water-phase clouds. Radiative transfer simulations of three near-infrared Moderate Resolution Imaging Spectroradiometer (MODIS) channels centered on wavelengths of 1.6, 2.1, and 3.7 μm reveal that the retrieved CDR are strongly influenced by the vertical inhomogeneity of droplet size including (i) the existence of small cloud droplets at the cloud top and (ii) the existence of the drizzle mode. The influence of smaller droplets at cloud top affects the 3.7- μm channel most, whereas the presence of drizzle influences radiances of both the 2.1- and 1.6- μm channels more than the 3.7- μm channel. Differences in the CDR obtained from MODIS 1.6-, 2.1-, and 3.7- μm channels that appear in global analysis of MODIS retrievals and the CDR derived from data collected during the First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE) intensive observation period in 1987 can be explained by the results obtained from the sensitivity experiments of this study. [ABSTRACT FROM AUTHOR]

This study investigates the correlation patterns between cloud droplet effective radius (CDR) and cloud optical thickness (COT) of warm clouds with a nonhydrostatic spectral bin microphysics cloud model. Numerical experiments are performed with the model to simulate low-level warm clouds. The results show a positive and negative correlation pattern between CDR and COT for nondrizzling and drizzling stages of cloud development, respectively, consistent with findings of previous observational studies. Only a positive correlation is simulated when the collection process is switched off in the experiment, whereas both the positive and negative correlations are reproduced in the simulation with collection as well as condensation processes. The positive and negative correlations can also be explained in terms of an evolution pattern of the size distribution function due to condensation and collection processes, respectively. Sensitivity experiments are also performed to examine how the CDR–COT correlation patterns are influenced by dynamical and aerosol conditions. The dynamical effect tends to change the amplitude of the CDR–COT plot mainly through changing the liquid water path, whereas the aerosol amount significantly modifies the correlation pattern between CDR and COT mainly through changing the cloud particle number concentration. These results suggest that the satellite-observed relationships between CDR and COT can be interpreted as being formed through microphysical particle growth processes under various dynamical and aerosol conditions in the real atmosphere. [ABSTRACT FROM AUTHOR]

Describes a method for satellite remote sensing of cloud optical thickness and effective particle radius. Determination of ground-reflected solar radiation and thermal radiations; Use of cloud feedback mechanisms to assess global climate change; General circulation models.

Common requirements for cloud detection methods including the adjustability with respect to incorrect results are clarified, and a method is proposed that satisfies the requirements by applying the support vector machine (SVM). Because the conditions of clouds and Earth's surfaces vary widely, incorrect results in actual cloud detection operations are unavoidable. Cloud detection methods therefore should be adjustable to easily reduce the frequency of incorrect results under certain conditions, without causing new incorrect results under other conditions. Cloud detection methods are also required to resolve a characteristic issue: the boundary between clear-sky and cloudy-sky areas in nature is vague, because the density of the cloud particles continuously varies. This vagueness makes the cloud definition subjective. Furthermore, the training dataset preparation for machine learning should avoid circular arguments. The SVM learning is generally less likely to result in overfitting: this study suggests that only typical data are sufficient for the SVM training dataset. By incorporating the discriminant analysis (DA), it is possible to subjectively determine the definition of typical cloudy and clear sky and to obtain typical cloud data without direct cloud detection. In an approach to adjust the classifier, data typical of certain conditions that lead to incorrect results are added to the training dataset. In this study, an adjustment procedure is proposed, which quantitatively judges, whether an addition is actually effective for reduction of the frequency of incorrect results. Another approach for the adjustment is improving feature space used for cloud detection. Indices as quantitative guidance to estimate whether an addition or elimination of a feature actually reduces the frequency of incorrect results can be obtained from the analysis of the support vectors. The cloud detection method incorporating the SVM is therefore able to integrate practical adjustment procedures. Applications of this method to Moderate Resolution Imaging Spectroradiometer (MODIS) data demonstrate that the concept of the method satisfies the requirements and the adjustability to various conditions can be realized. [ABSTRACT FROM AUTHOR]