Search

Surface downward solar radiation compositions (SSRC), including photosynthetically active radiation (PAR), ultraviolet-A (UVA), ultraviolet-B (UVB), and shortwave radiation (SWR), with high spatial–temporal resolutions and precision are essential for applications including solar power, vegetation photosynthesis, and environmental health. In this study, an optimal algorithm was developed to calculate SSRC, including their direct and diffuse components. Key features of the algorithm include combining the radiative transfer model with machine learning techniques, including full consideration of the effects of aerosol types, cloud phases, and gas components. A near-real-time monitoring system was developed based on this algorithm, with SSRC products generated from Himawari-8/9 and Fengyun-4 series data. Validation with ground-based data shows that the accuracy of the SWR and PAR compositions (daily mean RMSEs of 19.7 and 9.2 W m−2, respectively) are significantly better than those of state-of-the-art products from CERES, ERA5, and GLASS. The accuracy of UVA and UVB measurements is comparable with CERES. Characteristics of aerosols, clouds, gases, and their impacts on SSRC are investigated before, during, and post COVID-19; in particular, significant SSRC variations due to the reduction of aerosols and increase of ozone are identified in the Chinese central and eastern areas during that period. The spatial–temporal resolution of data products [up to 0.05° (10 min)−1 for the full-disk region] is one of the most important advantages. Data for the East Asia–Pacific region during 2016–20 is available from the CARE home page (www.slrss.cn/care/sp/pc/). [ABSTRACT FROM AUTHOR]

This paper shows the CFODD of the regional dependence of cloud growth processes in low-level clouds obtained by the combined use of the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite and the Cloud Profiling Radar (CPR) on the CloudSat satellite. This enabled the evaluation of the cloud growth process based on the cloud optical thickness (t) and the effective radius of cloud particle (Re), similar to previous studies that performed statical analysis on low-level clouds over the globe. Our targets were regions in East Asian, Californian, and Peruvian. In all analysis areas, our results showed that the internal structure of clouds changed as Re increased, indicating cloud growth. In the East Asian region, the maximum t remained relatively constant even when cloud droplet size grew. In contrast, in the regions of Californian and Peruvian, the maximum t increased with Re during the condensation growth process and then decreased as drizzle particles transformed into rain. It was also found that Re was smaller in the East Asian region unlike in Californian and Peruvian. This indicates that there are more aerosols in the East Asian region, which is consistent with its geographical characteristics. [ABSTRACT FROM AUTHOR]

Currently, the terahertz remote sensing technology is one of the best ways to detect the microphysical properties of ice clouds. Influenced by the representativeness of the ice crystal scattering (ICS) model, the existing terahertz ice cloud remote sensing inversion algorithms still have significant uncertainties. In this study, we developed a terahertz remote sensing inversion algorithm of the ice water path (IWP) and effective particle radius (Re) of ice clouds based on the Voronoi ICS model. This study utilized the single-scattering properties (extinction efficiency, single-scattering albedo and asymmetry factor) of the Voronoi and Sphere ICS models in the terahertz region. Combined with 14,408 groups of particle size distributions obtained from aircraft-based measurements, we completed the Voronoi and Sphere ICS schemes based on the Voronoi and Sphere ICS models. The two schemes were applied to the RSTAR radiative transfer model to carry out the sensitivity analysis of the top of cloud (TOC) terahertz brightness temperature differences (BTDs) on the IWP and Re. The sensitivity results showed that the TOC BTDs between 640 and 874 GHz are functions of the IWP, and the TOC BTDs between 380, 640 and 874 GHz are functions of the Re. The Voronoi ICS scheme possesses stronger sensitivity to the Re than the Sphere ICS scheme. Based on the sensitivity results, we built a multi-channel look-up table for BTDs. The IWP and Re were searched from the look-up table using an optimal estimation algorithm. We used 2000 BTD test data randomly generated by the RSTAR model to assess the algorithm’ s accuracy. Test results showed the correlation coefficients of the retrieved IWP and Re reach 0.99 and 0.98, respectively. As an application, we used the inversion algorithm to retrieve the ice cloud IWP and Re based on the CoSSIR airborne terahertz radiation measurements. Validation against the retrievals of the Bayesian algorithm reveals that the Voronoi ICS model performs better than the Sphere ICS model, with the enhancement of the Mean absolute error of 5.0 % and 12.8 % for IWP and Re, respectively. In summary, the results of this study confirmed the practicality and effectiveness of the Voronoi ICS model in the terahertz remote sensing inversion of ice cloud microphysical properties. [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. DOI: 10.1175/2010JAS3463.1

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-[micro]m 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-[micro]m (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 [micro]m) to drizzle (CDR greater than 14 [micro]m) to rain (CDR greater than 20 [micro]m). 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. DOI: 10.1175/2010JAS3276.1

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 [micro]m 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-[micro]m channel most, whereas the presence of drizzle influences radiances of both the 2.1- and 1.6-[micro]m channels more than the 3.7-[micro]m channel. Differences in the CDR obtained from MODIS 1.6-, 2.1-, and 3.7-[micro]m 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. DOI: 10.1175/2009JAS3280.1

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. DOI: 10.1175/2009JAS3283.1

We develop a numerical algorithm for calculating the light-scattering properties of small particles of arbitrary shape on the basis of a method involving surface integral equations. The calculation error was estimated by performing a comparison between the proposed method and the exact Mie method with regard to the extinction efficiency factor, and the results show that the error is less than 1% when four or more nodes per wavelength are set on the surface of a spherical particle. The accuracy fluctuates in accordance with the distribution of nodal points on the particle surface with respect to the direction of propagation of the incident light. From our examinations, it is shown that the polar incidence alignment yields higher accuracy than equator incidence when a 'latitude--longitude' type of mesh generation is adopted. The electric currents on the particle surface and the phase functions of all scattering directions are shown for particles shaped as spheres or hexagonal columns. It is shown that the phase function for a hexagonal column has four or eight cold spots. The phase function of a randomly oriented hexagonal column shows halolike peaks with size parameters of up to 20. This method can be applied to particles with a size parameter of up to about 20 without using the symmetry characteristic of the particle. OCIS codes: 010.0010, 290.5850, 290.5825, 280.0280, 280.1310.

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.

Two new extension modules that give the water-leaving radiance from the ocean and the snow bidirectional reflectance distribution function were implemented in the latest radiative transfer code. In addition, to simulate the near-global distributions of satellite-measured radiances by using the improved radiative transfer code, we tested and applied the look-up table method together with the process-separation technique of the radiative transfer calculation. The computing time was reduced from 1 year to 20 s to simulate one channel, one scene of the Global Imager image by use of an Alpha 21164A-2 (600-MHz) machine. The error analyses showed that the radiances were simulated with less than 1% error for the nonabsorbing visible channels and ~2% error for absorbing channels by use of this method. OCIS codes: 010.1310, 120.0280.

Surface downward radiation (SDR), including shortwave downward radiation (SWDR) and longwave downward radiation (LWDR), is of great importance to energy and climate studies. Considering the lack of reliable SDR data with a high spatiotemporal resolution in the East Asia–Pacific (EAP) region, we derived SWDR and LWDR at 10-min and 0.05° resolutions for this region from 2016 to 2020 based on the next-generation geostationary satellite Himawari-8 (H-8). The SDR product is unique in terms of its all-sky features, high accuracy, and high-resolution levels. The cloud effect is fully considered in the SDR product, and the influence of high aerosol loadings and topography on the SWDR are considered. Compared to benchmark products of the radiation, such as Clouds and the Earth's Radiant Energy System (CERES) and the European Centre for Medium-Range Weather Forecasts (ECMWF) next-generation reanalysis (ERA5), and the Global Land Surface Satellite (GLASS), not only is the resolution of the new SDR product notably much higher, but the product accuracy is also higher than that of those products. In particular, hourly and daily root-mean-square errors of the new SWDR are 104.9 and 31.5 W m−2, respectively, which are much smaller than those of CERES (at 121.6 and 38.6 W m−2, respectively), ERA5 (at 176.6 and 39.5 W m−2, respectively), and GLASS (daily of 36.5 W m−2). Meanwhile, RMSEs of hourly and daily values of the new LWDR are 19.6 and 14.4 W m−2, respectively, which are comparable to that of CERES and ERA5, and even better over high-altitude regions. [ABSTRACT FROM AUTHOR]

The channel specifications of the Global Imager onboard the Advanced Earth Observing Satellite II have been determined by extensive numerical experiments. The results show that there is an optimum feasible position for each ocean color channel. The bandwidth of the 0.763-[[micro]meter] channel should be less than 10 nm for good sensitivity to the cloud top height and geometric thickness of the cloud layer; a 40-nm bandwidth is suitable for the 1.38-[[micro]meter] channel to have the strongest contrast between cloudy and clear radiance with a sufficient radiant energy; and a 3.7-[[micro]meter] channel is better than a 3.95-[[micro]meter] channel for estimation of the sea surface temperature (SST) and determination of the cloud particle size when the bandwidth of the channel is 0.33 [[micro]meter]. A three-wavelength combination of 6.7, 7.3, and 7.5 [[micro]meter] is an optimized choice for water vapor profiling. The combination of 8.6, 10.8, and 12.0 [[micro]meter] is suitable for cloud microphysics and SST retrievals with the split-window technique.

The Greenhouse Gases Observing Satellite (GOSAT) was launched in 2009 to measure global atmospheric CO2 and CH4 concentrations. GOSAT is equipped with two sensors: the thermal and near-infrared sensor for carbon observation (TANSO)-Fourier transform spectrometer (FTS) and TANSO-cloud and aerosol imager (CAI). The presence of clouds in the instantaneous field of view of the FTS leads to incorrect estimates of the concentrations. Thus, the FTS data suspected to have cloud contamination must be identified by a CAI cloud discrimination algorithm and rejected. Conversely, overestimating clouds reduces the amount of FTS data that can be used to estimate greenhouse gases concentrations. This is a serious problem in tropical rainforest regions, such as the Amazon, where the amount of useable FTS data is small because of cloud cover. Preparations are continuing for the launch of the GOSAT-2 in fiscal year 2018. To improve the accuracy of the estimates of greenhouse gases concentrations, we need to refine the existing CAI cloud discrimination algorithm: Cloud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA1). A new cloud discrimination algorithm using a support vector machine (CLAUDIA3) was developed and presented in another paper. Visual inspection can use the locally optimized standards for judging, although CLAUDIA1 and CLAUDIA3 use common thresholds all over the world. Thus, the accuracy of visual inspection is better than that of these algorithms in most regions, with the exception of snow and ice covered surfaces, where there is not enough spectral contrast to distinguish cloud. For the reason visual inspection can be used for the truth metric for the verification exercise. In this study, we compared between CLAUDIA1-CAI and CLAUDIA3-CAI for various land cover types, and evaluated the accuracy of CLAUDIA3-CAI by comparing the both of CLAUDIA1-CAI and CLAUDIA3-CAI against visual inspection of the same CAI images in tropical rainforests. Comparative results between CLAUDIA1-CAI and CLAUDIA3-CAI for various land cover types indicated that CLAUDIA3-CAI had tendency to identify bright surface and optically thin clouds, however, misjudge the edges of clouds as compared with CLAUDIA1-CAI. The accuracy of CLAUDIA3-CAI was approximately 89.5 % in tropical rainforests, which is greater than that of CLAUDIA1-CAI (85.9 %) for the test cases presented here. [ABSTRACT FROM AUTHOR]