Cloud Properties Simulated by a Single-Column Model. Part II: Evaluation of Cumulus Detrainment and Ice-phase Microphysics Using a Cloud Resolving Model

This paper is the second in a series in which kilometer-scale-resolving observations from the Atmospheric Radiation Measurement program and a cloud-resolving model (CRM) are used to evaluate the single-column model (SCM) version of the National Centers for Environmental Prediction Global Forecast System model. Part I demonstrated that kilometer-scale cirrus properties simulated by the SCM significantly differ from the cloud radar observations while the CRM simulation reproduced most of the cirrus properties as revealed by the observations. The present study describes an evaluation, through a comparison with the CRM, of the SCM's representation of detrainment from deep cumulus and ice-phase microphysics in an effort to better understand the findings of Part I. It is found that detrainment occurs too infrequently at a single level at a time in the SCM, although the detrainment rate averaged over the entire simulation period is somewhat comparable to that of the CRM simulation. Relatively too much detrained ice is sublimated when first detrained. Snow falls over too deep of a layer due to the assumption that snow source and sink terms exactly balance within one time step in the SCM. These characteristics in the SCM parameterizations may explain many of the differences in the cirrus properties between the SCM and the observations (or between the SCM and the CRM). A possible improvement for the SCM consists of the inclusion of multiple cumulus cloud types as in the original Arakawa-Schubert scheme, prognostically determining the stratiform cloud fraction and snow mixing ratio. This would allow better representation of the detrainment from deep convection, better coupling of the volume of detrained air with cloud fraction, and better representation of snow field

Intelligent Model of Home Furnishing and Transportation Based on Improved RFID Web Fuzzy Clustering

This paper uses the fuzzy clustering method based on clustering path division, according to user access path. In the cluster, according to the access time and Agent classification of user, this method is then according to the user to all pages in order to visit division. The hardware of intelligent home furnishing controller by the advanced ARM9 embedded system, mobile phone module and RFID module. Intelligent transportation system through the sharing of traffic information, can realize the coordinated traffic signal control, effective traffic prediction and grooming. The paper presents intelligent model of home furnishing and transportation based on improved RFID web fuzzy clustering. Experiments show that RFID and fuzzy clustering can improve reliability of intelligent traffic and home furnishing and effectiveness

Arctic Mixed-phase Clouds Simulated by a Cloud-Resolving Model: Comparison with ARM Observations and Sensitivity to Microphysics Parameterizations

Single-layer mixed-phase stratiform (MPS) Arctic clouds, which formed under conditions of large surface heat flux combined with general subsidence during a subperiod of the Atmospheric Radiation Measurement (ARM) Program Mixed-Phase Arctic Cloud Experiment (M-PACE), are simulated with a cloud resolving model (CRM). The CRM is implemented with either an advanced two-moment (M05) or a commonly used one-moment (L83) bulk microphysics scheme and a state-of-the-art radiative transfer scheme. The CONTROL simulation, that uses the M05 scheme and observed aerosol size distribution and ice nulei (IN) number concentration, reproduces the magnitudes and vertical structures of cloud liquid water content (LWC), total ice water content (IWC), number concentration and effective radius of cloud droplets as suggested by the M-PACE observations. It underestimates ice crystal number concentrations by an order of magnitude and overestimates effective radius of ice crystals by a factor of 2-3. The OneM experiment, that uses the L83 scheme, produces values of liquid water path (LWP) and ice plus snow water path (ISWP) that were about 30% and 4 times, respectively, of those produced by the CONTROL. Its vertical profile of IWC exhibits a bimodal distribution in contrast to the constant distribution of IWC produced in the CONTROL and observations

Enhanced cycling stability of Li–O2 batteries by using a polyurethane/SiO2/glass fiber nanocomposite separator

A considerable improvement in the cycle performance of aprotic Li–O2 batteries was achieved by using a polyurethane/SiO2 gel nanoparticles/glass fiber (PU/SiO2/GF) nanocomposite separator, where a persistent capability of 1000 mA h g−1 was maintained for at least 300 charge/discharge cycles in a DMSO electrolyte with 1 M LiClO4 and 0.05 M LiI. In comparison, the cell with a conventional GF separator in the same experimental setup only run for 60 cycles. SEM, XRD and FT-IR analyses indicate that the corrosion and dendritic growth of the Li anode were significantly inhibited during the charge/discharge cycling, and the eventual failure of the Li–O2 batteries was attributed to the cathode passivation caused by the accumulation of the discharge product, which blocked the transfer of oxygen and electrolyte to the MWNTs cathode

Multi-Layer Arctic Mixed-Phase Clouds Simulated by a Cloud-Resolving Model: Comparison with ARM Observations and Sensitivity Experiments

A cloud-resolving model (CRM) is used to simulate the multiple-layer mixed-phase stratiform (MPS) clouds that occurred during a three-and-a-half day subperiod of the Department of Energy-Atmospheric Radiation Measurement Program s Mixed-Phase Arctic Cloud Experiment (M-PACE). The CRM is implemented with an advanced two-moment microphysics scheme, a state-of-the-art radiative transfer scheme, and a complicated third-order turbulence closure. Concurrent meteorological, aerosol, and ice nucleus measurements are used to initialize the CRM. The CRM is prescribed by time-varying large-scale advective tendencies of temperature and moisture and surface turbulent fluxes of sensible and latent heat. The CRM reproduces the occurrences of the single- and double-layer MPS clouds as revealed by the M-PACE observations. However, the simulated first cloud layer is lower and the second cloud layer thicker compared to observations. The magnitude of the simulated liquid water path agrees with that observed, but its temporal variation is more pronounced than that observed. As in an earlier study of single-layer cloud, the CRM also captures the major characteristics in the vertical distributions and temporal variations of liquid water content (LWC), total ice water content (IWC), droplet number concentration and ice crystal number concentration (nis) as suggested by the aircraft observations. However, the simulated mean values differ significantly from the observed. The magnitude of nis is especially underestimated by one order of magnitude. Sensitivity experiments suggest that the lower cloud layer is closely related to the surface fluxes of sensible and latent heat; the upper cloud layer is probably initialized by the large-scale advective cooling/moistening and maintained through the strong longwave (LW) radiative cooling near the cloud top which enhances the dynamical circulation; artificially turning off all ice-phase microphysical processes results in an increase in LWP by a factor of 3 due to interactions between the excessive LW radiative cooling and extra cloud water; heating caused by phase change of hydrometeors could affect the LWC and cloud top height by partially canceling out the LW radiative cooling. It is further shown that the resolved dynamical circulation appears to contribute more greatly to the evolution of the MPS cloud layers than the parameterized subgrid-scale circulation

Pharmacokinetics and bioequivalence evaluation of acamprosate calcium tablets in healthy Chinese volunteers

AbstractBackgroundFew pharmacokinetic data of acamprosate were available in Chinese population and no medication is approved for alcohol dependence in China.Purpose1. Investigate the pharmacokinetic properties of acamprosate calcium in healthy Chinese male volunteers on single- and multiple-dose administration. 2. Compare the bioequivalence of two formulations of acamprosate calcium tablets both under fasting and fed conditions.MethodsThis open-label, randomized study included 3 stages. In each stage, a 2-way crossover bioequivalence study was conducted to study the pharmacokinetic properties and bioequivalence of acamprosate calcium tablets on multiple dosing after standardized meals, single dosing under fasting conditions and fed conditions, respectively. The washout period between each treatment in a stage and between each stage was 1week. Plasma acamprosate calcium was quantified by a validated liquid chromatography-tandem mass spectrometry (LC-MS/MS) method. Tolerability was evaluated by monitoring adverse events, physical examinations, 12-lead ECG, and laboratory tests.ResultsTotally, 36 male subjects were enrolled in the study and all of them completed the whole 3 study stages. Main pharmacokinetic parameters of test and reference formulations were as follows: multiple dosing, Tmax 9.94±6.59 and 9.47±5.47h, Cmax 435.74±348.10 and 346.54±155.66ng·mL−1, AUC0-t 8600.52±5264.77 and 9315.10±6820.03ng·mL−1·h, AUC0–∞ 8845.38±5838.18 and 9669.24±7326.53ng·mL−1·h, t1/2 10.06±8.83 and 9.87±10.35h; single dosing under fasting conditions, Tmax 7.29±4.87 and 6.57±1.85h, Cmax 247.85±110.05 and 244.64±132.43ng·mL−1, AUC0-t 3385.41±1418.92 and 3496.24±1767.29ng·mL−1·h, AUC0–∞ 3781.53±1556.96 and 3829.56±1981.25ng·mL−1·h, t1/2 13.07±17.24 and 10.26±7.78h; single dosing under fed conditions, Tmax 17.72±9.42 and 19.50±9.84h, Cmax 183.90±74.52 and 168.14±60.67ng·mL−1, AUC0-t 3181.71±1368.24 and 3575.11±1416.39ng·mL−1·h, AUC0–∞3442.39±2002.53 and 3624.44±1418.12ng·mL−1·h, t1/2 8.76±12.28 and 6.67±4.84h, respectively. In all three stages, 90% CIs for the test/reference ratio of AUC0–t and AUC0–∞ were located within 80%–125%, 90% CI for Cmax was within 70%–143%.ConclusionsSimilar pharmacokinetic results of acamprosate calcium tablets in healthy Chinese volunteers were found as those in Caucasic population. In all three stages, the two formulations met the regulatory criteria for bioequivalence.Chictr.org identifier: ChiCTR-TTRCC-14004853

Statistical Analyses of Satellite Cloud Object Data from CERES

The present study evaluates the ability of a cloud-resolving model (CRM) to simulate the physical properties of tropical deep convective cloud objects identified from a Clouds and the Earth s Radiant Energy System (CERES) data product. The emphasis of this study is the comparisons among the small-, medium- and large-size categories of cloud objects observed during March 1998 and between the large-size categories of cloud objects observed during March 1998 (strong El Ni o) and March 2000 (weak La Ni a). Results from the CRM simulations are analyzed in a way that is consistent with the CERES retrieval algorithm and they are averaged to match the scale of the CERES satellite footprints. Cloud physical properties are analyzed in terms of their summary histograms for each category. It is found that there is a general agreement in the overall shapes of all cloud physical properties between the simulated and observed distributions. Each cloud physical property produced by the CRM also exhibits different degrees of disagreement with observations over different ranges of the property. The simulated cloud tops are generally too high and cloud top temperatures are too low except for the large-size category of March 1998. The probability densities of the simulated top-of-the-atmosphere (TOA) albedos for all four categories are underestimated for high albedos, while those of cloud optical depth are overestimated at its lowest bin. These disagreements are mainly related to uncertainties in the cloud microphysics parameterization and inputs such as cloud ice effective size to the radiation calculation. Summary histograms of cloud optical depth and TOA albedo from the CRM simulations of the large-size category of cloud objects do not differ significantly between the March 1998 and 2000 periods, consistent with the CERES observations. However, the CRM is unable to reproduce the significant differences in the observed cloud top height while it overestimates the differences in the observed outgoing longwave radiation and cloud top temperature between the two periods. Comparisons between the CRM results and the observations for most parameters in March 1998 consistently show that both the simulations and observations have larger differences between the large- and small-size categories than between the large- and medium-size, or between the medium- and small-size categories. However, the simulated cloud properties do not change as much with size as observed. These disagreements are likely related to the spatial averaging of the forcing data and the mismatch in time and in space between the numerical weather prediction model from which the forcing data are produced and the CERES observed cloud systems

Harvest Video Foundation Models via Efficient Post-Pretraining

Building video-language foundation models is costly and difficult due to the redundant nature of video data and the lack of high-quality video-language datasets. In this paper, we propose an efficient framework to harvest video foundation models from image ones. Our method is intuitively simple by randomly dropping input video patches and masking out input text during the post-pretraining procedure. The patch dropping boosts the training efficiency significantly and text masking enforces the learning of cross-modal fusion. We conduct extensive experiments to validate the effectiveness of our method on a wide range of video-language downstream tasks including various zero-shot tasks, video question answering, and video-text retrieval. Despite its simplicity, our method achieves state-of-the-art performances, which are comparable to some heavily pretrained video foundation models. Our method is extremely efficient and can be trained in less than one day on 8 GPUs, requiring only WebVid-10M as pretraining data. We hope our method can serve as a simple yet strong counterpart for prevalent video foundation models, provide useful insights when building them, and make large pretrained models more accessible and sustainable. This is part of the InternVideo project \url{https://github.com/OpenGVLab/InternVideo}