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This study proposes a methodology for analyzing the precipitation process in mixed‐phase clouds using multisensor satellite data, including radar, lidar, and imager. By leveraging a specific multivariate statistic, we elucidate the vertical microphysical structures of mixed‐phase clouds and their transitions associated with cloud particle growth and phase change. Expanding upon previous warm rain process diagnostics, we integrate cloud thermodynamic phase information from lidar and imager, representing the phase near the cloud top and column, respectively, to classify the vertical microphysical structures obtained from radar. Our global composite analysis reveals a systematic transition from non‐precipitating to precipitating characteristics with increasing ice phase fraction of the cloud column, rather than near the cloud top, and increasing cloud‐top particle size. These findings offer valuable observational references for evaluating numerical models in precipitation physics. Plain Language Summary To ensure a robust assessment of precipitation physics within global climate and numerical weather prediction models, it is imperative to diagnose the precipitation process using satellite observations on a global scale. Here, we propose a new methodology to address this requirement using multisensor satellite measurements to extend our previously developed method for warm liquid‐phase rain into more general ice‐containing mixed‐phase precipitation. For this purpose, satellite‐based information on the cloud thermodynamic phase obtained from the lidar and imager was exploited to statistically classify the vertical profile characteristics of precipitation observed by radar. The results showed that precipitation tended to occur more efficiently with an increasing ice‐phase fraction of the cloud‐column and the cloud‐top particle size. The statistics derived from observations provide a benchmark for evaluating model precipitation physics, facilitating process‐oriented assessments of numerical models. Key Points The mixed‐phase precipitation was diagnosed using a combination of multisensor satellite measurements by radar, lidar, and imager Multivariate statistics were constructed to display the radar reflectivity classified by cloud thermodynamic phase and cloud particle size The statistics show more precipitating character with higher ice‐phase fraction of the cloud optical thickness and cloud‐top particle size

We developed a new aerosol satellite retrieval algorithm combining a numerical aerosol forecast. In the retrieval algorithm, the short-term forecast from an aerosol data assimilation system was used as an a priori estimate instead of spatially and temporally constant values. This method was demonstrated using observation of the Advanced Himawari Imager onboard the Japan Meteorological Agency's geostationary satellite Himawari-8. Overall, the retrieval results incorporated strengths of the observation and the model and complemented their respective weaknesses, showing spatially finer distributions than the model forecast and less noisy distributions than the original algorithm. We validated the new algorithm using ground observation data and found that the aerosol parameters detectable by satellite sensors were retrieved more accurately than an a priori model forecast by adding satellite information. Further, the satellite retrieval accuracy was improved by introducing the model forecast instead of the constant a priori estimates. By using the assimilated forecast for an a priori estimate, information from previous observations can be propagated to future retrievals, leading to better retrieval accuracy. Observational information from the satellite and aerosol transport by the model are incorporated cyclically to effectively estimate the optimum field of aerosol.

Measurements with a 763 nm channel, located within the oxygen A-band and equipped on the Second-generation Global Imager (SGLI) on board the Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission – Climate (GCOM-C) satellite, have the potential to retrieve cloud-base height (CBH) and cloud geometric thickness (CGT) through passive remote sensing. This study implemented an algorithm to retrieve the CBH using the SGLI 763 nm channel in combination with several other SGLI channels in the visible, shortwave infrared, and thermal infrared regions. In addition to CBH, the algorithm can simultaneously retrieve other key cloud properties, including cloud optical thickness (COT), cloud effective radius, ice COT fraction as the cloud thermodynamic phase, cloud-top height (CTH), and CGT. Moreover, the algorithm can be seamlessly applied to global clouds comprised of liquid, ice, and mixed phases. The SGLI-retrieved CBH exhibited quantitative consistency with CBH data obtained from the ground-based ceilometer network, shipborne ceilometer, satellite-borne radar, and lidar observations, as evidenced by sufficiently high correlations and small biases. These results provide practical evidence that the retrieval of CBH is indeed possible using the SGLI 763 nm channel. Moreover, the results lend credence to the future use of SGLI CBH data, including the estimation of the surface downward longwave radiative flux from clouds. Nevertheless, issues remain that must be addressed to enhance the value of SGLI-derived cloud retrieval products. These include the bias of SGLI CTH related to cirrus clouds and the bias of SGLI CBH caused by multi-layer clouds.

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/).

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.

This study developed an algorithm for the Level 2 (L2) atmospheric radiation flux and heating rate product by a Japanese team for Earth Cloud, Aerosol and Radiation Explorer (EarthCARE). This product offers vertical profiles of downward and upward longwave (LW) and shortwave (SW) radiative fluxes and their atmospheric heating rates. This paper describes the algorithm developed for generating products, including the atmospheric radiative transfer model and input datasets, and its validation against measurement data of radiative fluxes. In the testing phase before the EarthCARE launch, we utilized A-Train data that provided input and output variables analogous to EarthCARE, so that the developed algorithm could be directly applied to EarthCARE after its launch. The results include comparisons of radiative fluxes between radiative transfer simulations and satellite and ground-based observations that quantify errors in computed radiative fluxes at the top of the atmosphere against Clouds and the Earth's Radiant Energy System (CERES) observations and their dependence on cloud type with varying thermodynamic phases. For SW fluxes, the bias was 24.4 W m−2, and the root mean square error (RMSE) was 36.3 W m−2 relative to the CERES observations at spatial and temporal scales of 5° and 1 month, respectively. On the other hand, LW exhibits a bias of −10.7 W m−2 and an RMSE of 14.2 W m−2. When considering different cloud phases, the SW water cloud exhibited a bias of −11.7 W m−2 and an RMSE of 46.2 W m−2, while the LW showed a bias of 0.8 W m−2 and an RMSE of 6.0 W m−2. When ice clouds were included, the SW bias ranged from 58.7 to 81.5 W m−2 and the RMSE from 72.8 to 91.6 W m−2 depending on the ice-containing cloud types, while the corresponding LW bias ranged from −8.8 to −28.4 W m−2 and the RMSE from 25.9 to 31.8 W m−2, indicating that the primary source of error was ice-containing clouds. The comparisons were further extended to various spatiotemporal scales to investigate the scale dependency of the flux errors. The SW component of this product exhibited an RMSE of approximately 30 W m−2 at spatial and temporal scales of 40° and 40 d, respectively, whereas the LW component did not show a significant decrease in RMSE with increasing spatiotemporal scale. Radiative transfer simulations were also compared with ground-based observations of the surface downward SW and LW radiative fluxes at selected locations. The results show that the bias and RMSE for SW are −17.6 and 172.0 W m−2, respectively, which are larger than those for LW that are −5.6 and 19.0 W m−2, respectively.

In recent years, a number of mammalian zinc transporters have been identified, and candidate genes are rapidly growing. These transporters are classified into two families: ZIP (ZRT, IRT-like protein) and CDF (cation diffusion facilitator). ZIP members facilitate zinc influx into the cytosol, while CDF members facilitate its efflux from the cytosol. Molecular characterization of the transporters has brought about major advances in our understanding of their physiological functions. Zinc metabolism is regulated primarily through zinc-dependent control of transcription, translation, and intracellular trafficking of transporters. Analyses of mice whose zinc transporter genes have been genetically disrupted and of the naturally occurring mutant mice with symptoms related to abnormal zinc metabolism have provided compelling evidence that some zinc transporters play critical roles in zinc homeostasis. In this review, we review the literature of mammalian zinc transporters with emphasis on very recent findings and elicit integrative knowledge of zinc homeostasis.

BACKGROUND/OBJECTIVES:\\n\\nAlthough high or low (no) meat consumption was associated with elevated or reduced mortality from cardiovascular disease, respectively, few studies have investigated the association between moderate meat consumption and cardiovascular disease. We aimed to evaluate the associations between moderate meat consumption and cardiovascular disease mortality.\\nSUBJECTS/METHODS:\\n\\nWe conducted a prospective cohort study of 51,683 Japanese (20,466 men and 31,217 women) aged 40-79 years living in all of Japan (The Japan Collaborative Cohort Study; JACC Study). Consumptions of meat (beef, pork, poultry, liver and processed meat) were assessed via a food frequency questionnaire administrated at baseline survey. Hazard ratios (HRs) of mortality from cardiovascular disease were estimated from Cox proportional hazards regression models according to quintiles of meat consumption after adjustment for potential confounding variables.\\nRESULTS:\\n\\nDuring 820,076 person-years of follow-up, we documented 2685 deaths due to total cardiovascular disease including 537 ischemic heart diseases and 1209 strokes. The multivariable HRs (95% confidence interval) for the highest versus lowest quintiles of meat consumption (77.6 versus 10.4 g/day) among men were 0.66 (0.45-0.97) for ischemic heart disease, 1.10 (0.84-1.43) for stroke and 1.00 (0.84-1.20) for total cardiovascular disease. The corresponding HRs (59.9 versus 7.5 g/day) among women were 1.22 (0.81-1.83), 0.91 (0.70-1.19) and 1.07 (0.90-1.28). The associations were similar when the consumptions of red meat, poultry, processed meat and liver were examined separately.\\nCONCLUSION:\\n\\nModerate meat consumption, up to ~100 g/day, was not associated with increased mortality from ischemic heart disease, stroke or total cardiovascular disease among either gender.

The shortwave infrared (SWIR) channels commonly accommodated in satellite‐borne passive sensors contain information on cloud thermodynamic phase as well as cloud optical thickness (COT) and cloud effective radius (CER). This study develops algorithms for simultaneous retrieval of COT, CER, and cloud thermodynamic phase to estimate the fractional probability of the cloud phase as an alternative to discrete discrimination into liquid and ice typical of operational cloud retrievals. Two algorithms were developed and applied to the SWIR channels centered at 1.05, 1.63, and 2.21 μm of Second‐generation Global Imager (SGLI). The first algorithm retrieves COT, CER, and ice COT fractions relative to the total COT, which is a continuous quantity representing the partitioning into liquid and ice phases. The second represents the cloud phase partitioning in the form of differences between radiances observed and simulated under the assumptions of either liquid or ice clouds when retrieving COT and CER. The cloud phases from these algorithms agreed quantitatively with each other, and were able to characterize the phase occurrence on a global scale. The two types of cloud phase characterization were further compared against CALIPSO to find that the zonal‐mean occurrences of the cloud phase from the first algorithm were broadly consistent with those from CALIPSO, except for significantly smaller occurrences of supercooled water clouds in SGLI than in CALIPSO over middle‐to‐high latitude oceans. The cloud phase occurrences were also found to systematically vary with CER and cloud‐top temperature on a global scale in a manner significantly different between SGLI and CALIPSO. Plain Language Summary Remote sensing from space is the only way to observe the cloud thermodynamic phase on a global scale. This study introduces algorithms to identify the cloud phase using short‐wavelength infrared channels commonly used in Earth‐observing satellites. The algorithms developed have the unique feature that they represent the cloud phase continuously in the form of the fractional probability of liquid and ice phases determined independent of temperature, contrary to traditional discrete discriminations into fixed categories of, “liquid,” “ice” and “mixed‐phase,” often relying on temperature information. The cloud phase characterization obtained by applying the algorithms to sensors onboard polar‐orbiting satellites provides fundamental data for the occurrence of different cloud phases on a global scale that can be utilized for a meaningful analysis of the temperature‐dependent phase transition in the context of climate. A remarkable finding of this study is that occurrences of supercooled water and their relationship to temperature obtained by our methodologies are different from those obtained by another type of satellite measurement technique with lidar over mid‐to‐high latitude ocean. This difference highlights the uncertainty and potential use of satellite‐based cloud thermodynamic phase information from various measurement wavelengths to better characterize the microphysical structures of mixed‐phase clouds. Key Points Two quasi‐analytical cloud phase retrieval algorithms using shortwave infrared measurements from space are introduced The cloud phase is retrieved as a continuous, not discrete, quantity correlated with the partitioning of cloud water into liquid and ice The occurrence of cloud phase was found to vary systematically with cloud effective radius and cloud‐top temperature on a global scale

To identify the predictive and prognostic factors associated with ampicillin-resistant enterococcal bacteraemia, we retrospectively reviewed demographic, microbiological and clinical data of patients attending the Kyoto University Hospital, Japan, between 2009 and 2015. Logistic regression and Cox regression analyses were performed to determine the predictive and prognostic factors, respectively. In total, 235 episodes of enterococcal bacteraemia were identified. As ampicillin susceptibility was uniform for Enterococcus faecalis isolates and almost all ampicillin-resistant isolates were E. faecium , bacteraemia due to these species was investigated separately. E. faecalis and E. faecium accounted for 41.7% (98/235) and 48.1% (113/235) of the isolates, respectively and 91.2% of all E. faecium were ampicillin resistant. Nosocomial E. faecium bacteraemia acquisition (odds ratio (OR), 13.6; 95% confidence intervals, 3.16–58.3) was associated with ampicillin-resistant isolates. Bacteraemia from an unknown source (hazard ratio (HR), 2.91; 95% CI 1.36–6.21) and an increased Pitt bacteraemia score (PBS) (HR, 1.36; 95% CI 1.21–1.52) were associated with 30-day mortality in E. faecium infections. Likewise, bacteraemia from an unknown source (HR, 4.17; 95% CI 1.25–13.9) and increased PBS (HR, 1.27; 95% CI 1.09–1.48) were associated with 30-day mortality in patients with E. faecalis bacteraemia. The empirical therapeutic administration of glycopeptides is recommended for patients with bacteraemia from an unknown source in whom severe E. faecium bacteraemia is suspected.