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Author(s): Yang, T; Gao, X; Sorooshian, S; Li, X | Abstract: The controlled outflows from a reservoir or dam are highly dependent on the decisions made by the reservoir operators, instead of a natural hydrological process. Difference exists between the natural upstream inflows to reservoirs and the controlled outflows from reservoirs that supply the downstream users. With the decision maker's awareness of changing climate, reservoir management requires adaptable means to incorporate more information into decision making, such as water delivery requirement, environmental constraints, dry/wet conditions, etc. In this paper, a robust reservoir outflow simulation model is presented, which incorporates one of the well-developed data-mining models (Classification and Regression Tree) to predict the complicated human-controlled reservoir outflows and extract the reservoir operation patterns. A shuffled cross-validation approach is further implemented to improve CART's predictive performance. An application study of nine major reservoirs in California is carried out. Results produced by the enhanced CART, original CART, and random forest are compared with observation. The statistical measurements show that the enhanced CART and random forest overperform the CART control run in general, and the enhanced CART algorithm gives a better predictive performance over random forest in simulating the peak flows. The results also show that the proposed model is able to consistently and reasonably predict the expert release decisions. Experiments indicate that the release operation in the Oroville Lake is significantly dominated by SWP allocation amount and reservoirs with low elevation are more sensitive to inflow amount than others.

Despite the advantage of global coverage at high spatiotemporal resolutions, satellite remotely sensed precipitation estimates still suffer from insufficient accuracy that needs to be improved for weather, climate, and hydrologic applications. This paper presents a framework of a deep neural network (DNN) that improves the accuracy of satellite precipitation products, focusing on reducing the bias and false alarms. The state-of-the-art deep learning techniques developed in the area of machine learning specialize in extracting structural information from a massive amount of image data, which fits nicely into the task of retrieving precipitation data from satellite cloud images. Stacked denoising autoencoder (SDAE), a widely used DNN, is applied to perform bias correction of satellite precipitation products. A case study is conducted on the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) with spatial resolution of 0.08° × 0.08° over the central United States, where SDAE is used to process satellite cloud imagery to extract information over a window of 15 × 15 pixels. In the study, the summer of 2012 (June–August) and the winter of 2012/13 (December–February) serve as the training periods, while the same seasons of the following year (summer of 2013 and winter of 2013/14) are used for validation purposes. To demonstrate the effectiveness of the methodology outside the study area, three more regions are selected for additional validation. Significant improvements are achieved in both rain/no-rain (R/NR) detection and precipitation rate quantification: the results make 33% and 43% corrections on false alarm pixels and 98% and 78% bias reductions in precipitation rates over the validation periods of the summer and winter seasons, respectively.

The methionine choline-deficient (MCD) diet leads to severe liver injury similar to human nonalcoholic steatohepatitis (NASH). Autophagy has emerged as a critical lysosomal pathway that maintains cell function and survival through the degradation of cellular components such as organelles and proteins. The goal of this study was to elucidate the role of autophagy in MCD-induced steatosis, fibrosis, inflammation, mitochondrial dysfunction, and endoplasmic reticulum (ER) stress in mice. Mice were fed with MCD diet and treated with rapamycin (an autophagy enhancer) or chloroquine (an autophagy inhibitor) for 10 weeks. Liver injury was evaluated biochemically and histologically together with hepatic gene expression analysis. Autophagic flux was impaired in livers of mice fed with MCD diet, evidenced by reduced ratio of LC3-II/LC3-I and increased protein expression of p62. It was found that autophagy activation by rapamycin attenuated MCD-induced steatosis, fibrosis, inflammation, mitochondrial dysfunction, and ER stress. By contrast, MCD mice treated with chloroquine developed more liver injury. In conclusions, the autophagic pathway plays an important protective role in MCD-induced advanced NASH. Thus, pharmacological promotion of autophagy may provide a novel therapeutic strategy for treatment of NASH.

Methyltrimethoxysilane (MTMS) was used as the precursor and the organosilica aerogel was prepared by an acid-base catalyzed two stage process. To the aqueous system of acid-catalyzed hydrolyzate of MTMS, a kind of hydrophobic organic solvent, cyclohexane, which is insoluble in the aqueous phase was added and dispersed into small droplets with the aid of a high speed homogenizer. Then the reaction mixture was transformed into a gel through base catalyzed condensation. The hydrophobic solvent was pre-embedded in the gel in the form of microcapsules with the condensed MTMS hydrolyzate forming the shell. The pre-embedded hydrophobic solvent reduced the interface tension between the pore liquid and the skeleton solid. Ambient pressure drying of the wet gels produced low shrinkage and gave low density monolithic aerogels consisting of hollow microspheres.

Due to high theoretical specific capacity, energy density, and low-cost lithium-sulfur (Li-S) batteries became the most potential energy storage devices for the next generation. The harsh shuttling effect, poor electroconductibility of sulfur, and volume change affect the Li-S battery’s practical application. Herein, the rational design of porous hollow carbon aerogel (PHCA) with an ultra-high specific surface area of 2054.5 m2 g−1 and outstanding adsorption ability is firstly fabricated to restrict the shuttling effect of lithium polysulfides (LiPSs) by using commercialized nano-CaCO3 as a template. As a sulfur host, the S/PHCA cathode delivers initially discharge specific capacity of 992.7 mAh g−1 with an average Coulombic efficiency of 97.6%. After 200 cycles, it obtains the reversible specific capacities of 600.1 mAh g−1 for S/PHCA cathode at 0.1C (2.2 mg cm−2) corresponding to 60.45% of their initial discharge specific capacity. As a PHCA interlayer, the Li-S battery with the PHCA interlayer exhibits initially discharge specific capacity of 1267.7 mAh g−1 with an average Coulombic efficiency of 99.36% at 0.1C (3.2 mg cm−2) and maintains at 863.3 mAh g−1 with the discharge specific capacity retention rate of 69.2%. This is an effective strategy to improve the outstanding electrochemical performance which is beyond reported carbon materials, which is attributed to stronger adsorption of LiPSs by the micropore. This work provides a new direction for the modification of a sulfur host and interlayer and simultaneously reveals the relationship between the heteroatom distribution and LiPSs.

Uniform Ag and Si layers were successfully prepared on the surface of graphite films (GF) to improve thermal conductivity (TC) by physical vapor deposition method (PVD). The results demonstrate that the unique cubic crystal structure of Ag layer with different deposition time show good interface bonding and no carbide product of Ag/GF composites. Meanwhile, the dense Si coating contribute to the enhancement of the thermal diffusion ability. The Ag/GF composites with the deposition time of 2 h possess the best comprehensive property with the TC of 9.93 (W/m·K), which is approximately 73.30% higher than untreated GF. The TC of Si/GF composites reach the maximum TC of 11.12 W/(m·K) and increase about 101.81% due to the nano-Si layer comparing with untreated GF. Those composites with high performance make them promising thermal management laminated materials.

Because of its high specific surface area and high conductivity, carbon aerogel has become one of an ideal sulfur hosts for lithium-sulfur (Li-S) batteries. However, the microstructure of carbon aerogel perplexed by many factors, such as solvents, catalysts. In this work, carbon aerogel (CA) is respectively fabricated through phenol-formaldehyde reaction under two different catalysts of hexamethylenetetramine and sodium hydroxide, and carbonized under high temperature, and loaded with sulfur via diffusion-melting. The physical properties of resulting CA via hexamethylenetetramine catalyst (CAHE) and the electrochemical performances of the obtaining CAHE/S (sulfur) are more outstanding than that of carbon aerogel via sodium hydroxide catalyst (CASH) by scanning electron microscopy, Brunauer-Emmett-Teller characterization, and galvanostatic discharge/charge test and electrochemical impedance spectroscopy. With a comparison of common CASH, and sulfur loaded in CASH (CASH/S), CAHE/S electrode exhibits a relatively high initial discharge specific capacity of 1085.6 mAh g−1, increasing 11% in capacity retention rate at 0.1 C after 100 cycles. After 50 cycles, the capacity attenuation rate is 0.08% per cycle, which displays the excellent performance of the CAHE/S electrode. It is found that the improved performance of CAHE/S is attributed to finer particles and larger specific surface area of CAHE which rapidly improves lithium ion diffusion. This work not only modifies the structure of carbon aerogelbut also provides an important reference value for the potential application of the carbon aerogel in Li-S batteries.

Studies have been reported about the efficacy of satellites for measuring precipitation and about quantifying their errors. Based on these studies, the errors are associated with a number of factors, among them, intensity, location, climate, and season of the year. Several error models have been proposed to assess the relationship between the error and the rainfall intensity. However, it is unknown whether these models are adaptive to different seasons, different regions, or different types of satellite-based estimates. Therefore, how the error–intensity relationship varies with the season or region is unclear. To investigate these issues, a parametric joint pdf model is proposed to analyze and study the 9-yr satellite-derived precipitation datasets of Climate Prediction Center (CPC) morphing technique (CMORPH); PERSIANN; and the real-time TRMM product 3B42, version 7 (TRMM-3B42-RTV7). The NEXRAD Stage IV product is the ground reference. The adaptability of the proposed model is verified by applying it to three locations (Oklahoma, Montana, and Florida) and by applying it to cold season, warm season, and the entire year. Then, the heteroscedasticities in the errors of satellite-based precipitation measurements are investigated using the proposed model under those scenarios. The results show that the joint pdfs have the same formulation under these scenarios, whereas their parameter sets were adaptively adjusted. This parametric model reveals detailed information about the spatial and seasonal variations of the satellite-based precipitation measurements. It is found that the shape of the conditional pdf shifts across the intensity ranges. At the ~10–20 mm day−1 range, the conditional pdf is L shaped, while at the ~40–60 mm day−1 range, it becomes more bell shaped. It is also concluded that no single satellite-based precipitation product outperforms others with respect to the different scenarios (i.e., seasons, regions, and climates).

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville-Thermalito Complex (OTC) - a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation-storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California. A new multi objective optimization algorithm, entitled MOSPD, is developed.Comparison study is carried out over eight complex test functions.MOSPD is effective and efficient in searching global Pareto optimal.A reservoir system model is built for Oroville-Thermalito complex in California.MOSPD provides flexible reservoir release strategies to support decision making.

This manuscript introduces a novel computational science approach for studying the impact of climate variability on precipitation. The approach uses an object-oriented connectivity algorithm that segments gridded near-global satellite precipitation data into four-dimensional (4D) objects (longitude, latitude, time, and intensity). These precipitation systems have distinct spatiotemporal properties that are counted, tracked, described, and stored in a searchable database. A case study of western United States precipitation systems is performed, demonstrating the unique properties and capabilities of this object-oriented database. The precipitation dataset used in the case study is the University of California, Irvine, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) from 1 March 2000 to 1 January 2011. A search of the database for all western United States precipitation systems during this time period returns 626 precipitation systems as objects. By analyzing these systems as segmented objects, joint interactions of the selected climate phenomena 1) Arctic Oscillation (AO), 2) Madden–Julian oscillation (MJO), and 3) El Niño–Southern Oscillation (ENSO) on precipitation can be shown. They directly show the increased/decreased likelihood of having precipitation systems occurring over the western United States (monthly count) during phases of these climate phenomena. It is found that specific climate phenomena impact the monthly count of the events differently, and that the joint interaction of climate phenomena of the AO–MJO and AO–ENSO is important, especially during certain months of the year. It is also found that these interactions impact the physical features of the precipitation systems themselves.

MiRNAs are a class of endogenous, short, single-stranded, non-coding RNAs, which are tightly linked to cardiac disorders such as myocardial ischemia/reperfusion (I/R) injury. MiR-34a is known to be involved in the hypoxia-induced cardiomyocytes apoptosis. However, the molecular mechanisms are unclear. In the present study, we demonstrate that under low glucose supply, rat cardiomyocytes are susceptible to hypoxia. Under short-time hypoxia, cellular glucose uptake and lactate product are induced but under long-time hypoxia, the cellular glucose metabolism is suppressed. Interestingly, an adaptive up-regulation of miR-34a by long-time hypoxia was observed both in vitro and in vivo, leading to suppression of glycolysis in cardiomyocytes. We identified lactate dehydrogenase-A (LDHA) as a direct target of miR-34a, which binds to the 3′-UTR region of LDHA mRNA in cardiomyocytes. Moreover, inhibition of miR-34a attenuated hypoxia-induced cardiomyocytes dysfunction through restoration of glycolysis. The present study illustrates roles of miR-34a in the hypoxia-induced cardiomyocytes dysfunction and proposes restoration of glycolysis of dysfunctional cardiomyocytes by inhibiting miR-34a during I/R might be an effectively therapeutic approach against I/R injury.

This paper investigates the application of deep neural networks to precipitation estimation from remotely sensed information. Specifically, a stacked denoising auto-encoder is used to automatically extract features from the infrared cloud images and estimate the amount of precipitation, referred as PERSIANN-SDAE. Due to the challenging imbalance in precipitation data, a Kullback-Leibler divergence is incorporated in the objective function to preserve the distribution of it. PERSIANN-SDAE is compared with a shallow neural network with hand designed features and an operational satellite-based precipitation estimation product. The experimental results demonstrate the effectiveness of PERSIANN-SDAE in estimating precipitation accurately while preserving its distribution. It outperforms both the shallow neural network and the operational product.

Global optimization of high-dimensional problems in practical applications remains a major challenge to the research community of evolutionary computation. The weakness of randomization-based evolutionary algorithms in searching high-dimensional spaces is demonstrated in this paper. A new strategy, SP-UCI is developed to treat complexity caused by high dimensionalities. This strategy features a slope-based searching kernel and a scheme of maintaining the particle population's capability of searching over the full search space. Examinations of this strategy on a suite of sophisticated composition benchmark functions demonstrate that SP-UCI surpasses two popular algorithms, particle swarm optimizer (PSO) and differential evolution (DE), on high-dimensional problems. Experimental results also corroborate the argument that, in high-dimensional optimization, only problems with well-formative fitness landscapes are solvable, and slope-based schemes are preferable to randomization-based ones.

Three popular evolutionary optimization algorithms are tested on high-dimensional benchmark functions. An important phenomenon responsible for many failures - “population degeneration” - is discovered. That is, through evolution, the population of searching particles degenerates into a subspace of the search space, and the global optimum is exclusive from the subspace. Subsequently, the search will tend to be confined to this subspace and eventually miss the global optimum. Principal components analysis (PCA) is introduced to discover population degeneration and to remedy its adverse effects. The experiment results reveal that an algorithm's efficacy and efficiency are closely related to the population degeneration phenomenon. Guidelines for improving evolutionary algorithms for high-dimensional global optimization are addressed. An application to highly nonlinear hydrological models demonstrates the efficacy of improved evolutionary algorithms in solving complex practical problems.

Objective: This work was designed to investigate the protection of cannabidiol (CBD) against palmitic acid (PA)-induced injury in cultured hepatocytes and the underlying mechanism associated with autophagic flux. Methods: Experiment 1: Primary cultured hepatocytes were stimulated with PA (800 μmol/L) and treated with CBD (5 μmol/L) and chloroquine (CQ, 50 nmol/L) or not for 24 hours (1: control group; 2: PA-stimulated group; 3: PA-stimulated group treated with CBD; 4: PA-stimulated group treated with CBD and CQ). Autophagic flux was evaluated by Western blot analysis. Apoptosis was measured by flow cytometry. The mRNA expression of genes involved in endoplasmic reticulum stress was determined by reverse transcription PCR. The mitochondrial function was determined by using fluorescent probe including Rh123 and lucigenin. Experiment 2: Primary cultured hepatocytes were treated with CBD alone for 24 h (1: control group; 2: lower-dose CBD-treated group; 3: higher-dose CBD-treated group). Then, the autophagic flux was evaluated by Western blot analysis. Results: When compared to control group, exposure to PA significantly led to impaired autopagic flux (evidenced by increased ratio of LC3-II/LC3-I and protein expression of p62), increased apoptosis, endoplasmic reticulum stress (evidenced by increased mRNA expression of C/EBP homologous protein, glucose-regulated protein 78, and X-box protein 1), and mitochondrial dysfunction (evidenced by reduced mitochondrial membrane potential and enhanced formation of mitochondrial reactive oxygen species). When compared to PA-stimulated group, CBD treatment significantly attenuated PA-induced impaired autophagic flux, apoptosis, endoplasmic reticulum stress, and mitochondrial dysfunction in cultured hepatocytes. The protection of CBD against PA was abolished by co-incubation with CQ. In addition, treatment with CBD alone had no significant effect on autophagic flux in cultured hepatocytes Conclusion: Cannabidiol attenuates palmitic acid-induced impaired autophagic flux, apoptosis, endoplasmic reticulum stress, and mitochondrial dysfunction in cultured hepatocytes through promoting autophagic flux.

This study downscaled more than five years of data (1999–2004) for hydrometeorological fields over the upper Rio Grande basin (URGB) to a 4-km resolution using a regional model [fifth-generation Pennsylvania State University–National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5, version 3)] and two forcing datasets that include National Centers for Environmental Prediction (NCEP)–NCAR reanalysis-1 (R1) and North America Regional Reanalysis (NARR) data. The long-term high-resolution simulation results show detailed patterns of hydroclimatological fields that are highly related to the characteristics of the regional terrain; the most important of these patterns are precipitation localization features caused by the complex topography. In comparison with station observational data, the downscaling processing, on whichever forcing field is used, generated more accurate surface temperature and humidity fields than the Eta Model and NARR data, although it still included marked errors, such as a negative (positive) bias toward the daily maximum (minimum) temperature and overestimated precipitation, especially in the cold season. Comparing the downscaling results forced by the NARR and R1 with both the gridded and station observational data shows that under the NARR forcing, the MM5 model produced generally better results for precipitation, temperature, and humidity than it did under the R1 forcing. These improvements were more apparent in winter and spring. During the warm season, although the use of NARR improved the precipitation estimates statistically at the regional (basin) scale, it substantially underestimated them over the southern upper Rio Grande basin, partly because the NARR forcing data exhibited warm and dry biases in the monsoon-active region during the simulation period and improper domain selection. Analyses also indicate that over mountainous regions, both the Climate Prediction Center’s (CPC’s) gridded (0.25°) and NARR forcings underestimate precipitation in comparison with station gauge data.

A feed-forward neural network is configured to calibrate the bias of a high-resolution probabilistic quantitative precipitation forecast (PQPF) produced by a 12-km version of the NCEP Regional Spectral Model (RSM) ensemble forecast system. Twice-daily forecasts during the 2002–2003 cool season (1 November–31 March, inclusive) are run over four U.S. Geological Survey (USGS) hydrologic unit regions of the southwest United States. Calibration is performed via a cross-validation procedure, where four months are used for training and the excluded month is used for testing. The PQPFs before and after the calibration over a hydrological unit region are evaluated by comparing the joint probability distribution of forecasts and observations. Verification is performed on the 4-km stage IV grid, which is used as “truth.” The calibration procedure improves the Brier score (BrS), conditional bias (reliability) and forecast skill, such as the Brier skill score (BrSS) and the ranked probability skill score (RPSS), relative to the sample frequency for all geographic regions and most precipitation thresholds. However, the procedure degrades the resolution of the PQPFs by systematically producing more forecasts with low nonzero forecast probabilities that drive the forecast distribution closer to the climatology of the training sample. The problem of degrading the resolution is most severe over the Colorado River basin and the Great Basin for relatively high precipitation thresholds where the sample of observed events is relatively small.

Estimating the water budgets in a small-scale basin is a challenge, especially in the mountainous western United States, where the terrain is complex and observational data in the mountain areas are sparse. This manuscript reports on research that downscaled 5-yr (1999–2004) hydrometeorological fields over the upper Rio Grande basin from a 2.5° NCEP–NCAR reanalysis to a 4-km local scale using a regional climate model [fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), version 3]. The model can reproduce the terrain-related precipitation distribution—the trend of diurnal, seasonal, and interannual precipitation variability—although poor snow simulation caused it to overestimate precipitation and evapotranspiration in the cold season. The outcomes from the coupled model are also comparable to offline Variable Infiltration Capacity (VIC) and Land Data Assimilation System (LDAS)/Mosaic land surface simulations that are driven by observed and/or analyzed surface meteorological data.

This study examines the capabilities and limitations of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) in predicting the precipitation and circulation features that accompanied the 2004 North American monsoon (NAM). When the model is reinitialized every 5 days to restrain the growth of modeling errors, its results for precipitation checked at subseasonal time scales (not for individual rainfall events) become comparable with ground- and satellite-based observations as well as with the NAM’s diagnostic characteristics. The modeled monthly precipitation illustrates the evolution patterns of monsoon rainfall, although it underestimates the rainfall amount and coverage area in comparison with observations. The modeled daily precipitation shows the transition from dry to wet episodes on the monsoon onset day over the Arizona–New Mexico region, and the multiday heavy rainfall (>1 mm day−1) and dry periods after the onset. All these modeling predictions agree with observed variations. The model also accurately simulated the onset and ending dates of four major moisture surges over the Gulf of California during the 2004 monsoon season. The model reproduced the strong diurnal variability of the NAM precipitation, but did not predict the observed diurnal feature of the precipitation peak’s shift from the mountains to the coast during local afternoon to late night. In general, the model is able to reproduce the major, critical patterns and dynamic variations of the NAM rainfall at intraseasonal time scales, but still includes errors in precipitation quantity, pattern, and timing. The numerical study suggests that these errors are due largely to deficiencies in the model’s cumulus convective parameterization scheme, which is responsible for the model’s precipitation generation.

The National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM) is used to produce twice-daily (0000 and 1200 UTC), high-resolution ensemble forecasts to 24 h. The forecasts are performed at an equivalent horizontal grid spacing of 12 km for the period 1 November 2002 to 31 March 2003 over the southwest United States. The performance of 6-h accumulated precipitation is assessed for 32 U.S. Geological Survey hydrologic catchments. Multiple accuracy and skill measures are used to evaluate probabilistic quantitative precipitation forecasts. NCEP stage-IV precipitation analyses are used as “truth,” with verification performed on the stage-IV 4-km grid. The RSM ensemble exhibits a ubiquitous wet bias. The bias manifests itself in areal coverage, frequency of occurrence, and total accumulated precipitation over every region and during every 6-h period. The biases become particularly acute starting with the 1800–0000 UTC interval, which leads to a spurious diurnal cycle and the 1200 UTC cycle being more adversely affected than the 0000 UTC cycle. Forecast quality and value exhibit marked variability over different hydrologic regions. The forecasts are highly skillful along coastal California and the windward slopes of the Sierra Nevada Mountains, but they generally lack skill over the Great Basin and the Colorado basin except over mountain peaks. The RSM ensemble is able to discriminate precipitation events and provide useful guidance to a wide range of users over most regions of California, which suggests that mitigation of the conditional biases through statistical postprocessing would produce major improvements in skill.

Non-alcoholic steatohepatitis (NASH) is one of the most common causes of chronic liver disease and is considered to be a causative factor of cryptogenic cirrhosis and hepatocellular carcinoma. The aim of this work was to investigate whether treatment with geraniol (a monoterpene) attenuated NASH induced by methionine-choline-deficient (MCD) diet in rats.Rats were fed with MCD diet to induce NASH and treated with geraniol (200 mg/kg/day) for 10 weeks.Treatment with geraniol reduced histological scores, fibrosis, and apoptosis in livers, lowered activities of alanine aminotransferase and aspartate aminotransferase in serum, and attenuated hepatic fat accumulation in rats fed with MCD diet. Treatment with geraniol preserved hepatic mitochondrial function, evidenced by reduced mitochondrial reactive oxygen species formation, enhanced adenosine triphosphate formation and membrane integrity, restored mitochondrial electron transport chain enzyme activity, and increased mitochondrial DNA content in rats fed with MCD diet. Treatment with geraniol reduced uncoupling protein 2 protein expression, and enhanced protein expression of prohibitin, mRNA expression of peroxisome proliferator-activated receptor α, and activity of mitochondrial carnitine palmitoyl transferase-I in livers of rats fed with MCD diet. Treatment with geraniol abated oxidative stress, evidenced by reduced malondialdehyde and 3-nitrotyrosine formation, enhanced activity of glutathione S-epoxide transferase, and down-regulated expression of inducible nitric oxide synthase and cytochrome P450 2E1 in livers of rats fed with MCD diet. Treatment with geraniol reduced myeloperoxidase activity and protein expression of tumor necrosis factor alpha and IL-6 in livers of rats fed with MCD diet.Treatment with geraniol attenuated MCD-induced NASH in rats.

The aim of this study was to validate a criteria-specific long-term survival prediction model (MHCAT) in a large cohort of hepatocellular carcinoma (HCC) patients after liver transplantation (LT) in China. Independent risk factors in MHCAT were retrospectively analysed for HCC patients recorded in the China Liver Transplant Registry. Survival predictions for each patient were calculated using MHCAT scores and the Metroticket formula separately and the prediction efficacy of MHCAT and Metroticket was compared using the area under ROC curve (c-statistic). A total of 1371 LTs for HCC were analysed in the study, with a median follow-up of 22.2 months (IQR 6.1–72.4 months). The proportions meeting the Milan, UCSF, Fudan and Hangzhou criteria were 34.4%, 39.7%, 44.2% and 51.9%, respectively. The c-statistics for MHCAT predictions of 3- and 5-year survival rates of HCC recipients were 0.712–0.727 and 0.726–0.741, respectively. Among these patients, 1298 LTs for HCC were ultimately selected for the comparison analysis for prediction efficacy. The c-statistic of MHCAT for predictions of 3-year survival with reference to the Milan, UCSF and Fudan criteria was significantly increased compared with that for Metroticket (p

Galectin-9 (Gal-9), a member of the galectin family, has a variety of biologic activities. However, its role in allografts is not fully clarified yet. The relationship between interleukin-17 (IL-17) and Gal-9 and the role of Gal-9 in T(H)17-cell differentiation also remain unclear. We built a murine cardiac transplantation model, which we treated with Gal-9 and/or EX-527, a specific Sirtuin-1 inhibitor. Afterwards, flow-cytometric analysis and reverse-transcription polymerase chain reaction were used to detect the number of T(H)17 cells and the expression of key factors involved in the differentiation of T(H)17 cells; in addition, the survival times of cardiac transplanted mice in different groups were recorded. The levels of circulating T(H)17 cells were found to increase in the peripheral blood of mice that exhibited acute rejection (AR) after heart transplantation, which was determined to be correlated with the rejection grade. Gal-9 or EX-527 can inhibit the activation and differentiation of T(H)17 cells and effectively suppress T(H)17-cell-mediated AR. These data provide new evidence for the potential regulatory effects of Gal-9 in alloimmune responses in a murine model of heart transplantation, and suggest the combined use of galectin-9 and EX-527 may be a powerful method to induce tolerance of fully mismatched murine cardiac allografts.

Weather radar systems provide detailed information on spatial rainfall patterns known to play a significant role in runoff generation processes. In the current study, we present an innovative approach to exploit spatial rainfall information of air mass thunderstorms and link it with a watershed hydrological model. Observed radar data are decomposed into sets of rain cells conceptualized as circular Gaussian elements and the associated rain cell parameters, namely, location, maximal intensity and decay factor, are input into a hydrological model. Rain cells were retrieved from radar data for several thunderstorms over southern Arizona. Spatial characteristics of the resulting rain fields were evaluated using data from a dense rain gauge network. For an extreme case study in a semi-arid watershed, rain cells were derived and fed as input into a hydrological model to compute runoff response. A major factor in this event was found to be a single intense rain cell (out of the five cells decomposed from the storm). The path of this cell near watershed tributaries and toward the outlet enhanced generation of high flow. Furthermore, sensitivity analysis to cell characteristics indicated that peak discharge could be a factor of two higher if the cell was initiated just a few kilometers aside.

Snow anomalies in the western USA have a significant impact on water availability, and hence have been widely investigated by many researchers. This study focuses on how anomalous atmospheric circulation affects snowpack accumulation in the western USA. Our results indicate that the mid-latitude atmospheric circulation anomalies induced by the El Nino–Southern Oscillation (ENSO) tend to drive winter precipitation shifts, leading to an anomalous snowpack distribution in the western USA. The warm phase of ENSO produces increased snowpack in the Southwest, whereas the cold phase generates increased snowpack in the Northwest. The results also indicate that temperature has a secondary impact on the anomalous snowpack distribution during ENSO episodes. Additionally, the atmospheric internal variability-related Pacific–North American (PNA) pattern is found to affect snow anomalies strongly in the western USA independent from ENSO. The positive phase of the PNA pattern produces colder temperature and stronger precipitation due to the lower pressure in the region, leading to an above-average snowpack. Conversely, the negative phase of the PNA pattern generates warmer temperature and weaker precipitation resulting from the higher pressure, producing below-average snowpack in the western USA. The significant findings that result from this study strengthen our understanding of climate and water supply variability in the western USA. Copyright © 2006 John Wiley & Sons, Ltd.

In this article, four continually processed sea surface temperature (SST) datasets, including the Reynolds SST (RYD), the global final analysis of skin temperature at oceans (FNL), and two Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua SSTs retrieved from thermal infrared imagery (TIR) and midinfrared imagery (MIR), were compared. The results show variations from each other. In comparison with the RYD SST, the FNL data have −0.5° ∼ 0.5°C perturbations, while the TIR and MIR SSTs possess larger deviations of −2° ∼ 1°C, mainly due to algorithm and/or sensor differences in these SST datasets. A regional model, the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (Penn State–NCAR) Mesoscale Model (MM5), was used to investigate whether model atmospheric predictions, especially those concerning precipitation during the North American monsoon season, are sensitive to these SST variations. A comparison of rainfall, atmospheric height, temperature, and wind fields produced by model results, reanalysis data, and observations indicates that, at monthly scale, the model shows changes in the simulations for three consecutive years; in particular, rainfall amounts, timing, and even patterns vary at some specific regions. Forced by the MODIS Aqua midinfrared SST (MIR), which includes large regions with SST values lower than the conventional Reynolds SST, the MM5 rain field predictions show reduced errors over land and oceans compared to when the model is forced by other SST data. Specifically, rainfall estimates are improved over the offshore of southern Mexico, the Gulf of Mexico, the coastal regions of southern and eastern Mexico, and the southwestern U.S. monsoon active region, but only slightly improved over the monsoon core and the high-elevated Great Plains. Using MIR SST data, one is also capable of improving geopotential height and temperature fields in comparison with the reanalysis data.

A spatial rainfall model was applied to radar data of air mass thunderstorms to yield a rainstorm representation as a set of convective rain cells. The modeled rainfall was used as input into hydrological model, instead of the standard radar-grid data. This approach allows a comprehensive linkage between runoff responses and rainfall structures. Copyright  2005 Royal Meteorological Society

Rainfall evolution and diurnal variation are important components in the North American monsoon system (NAMS). In this study these components are numerically studied using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) with high resolution (12-km grids) in contrast to most previous model studies that used relatively coarse spatial resolutions (>25 km grids). The model was initialized at the start of each month and allowed to run for 31 days. The study shows that, in general, the model results broadly matched the patterns of satellite-retrieved rainfall data for monthly rainfall accumulation. The rainfall timing evolution in the monsoon core region predicted by the model generally matched the gauge observations. However, the differences among the three precipitation estimates (model, satellite, and gauge) are obvious, especially in July. The rainfall diurnal cycle pattern was reproduced in the monsoon core region of west...

In this study, the Pennsylvania State University/National Center for Atmospheric Research fifth generation Mesoscale Model (MM5) linked to the Oregon State University (OSU) land-surface scheme, is used to assess the strength of soil moisture–precipitation feedback in the region of influence of the North American monsoon (NAM). Two control simulations are made with external forcing taken from the National Centers for Environmental Prediction re-analysis, and with a nested horizontal resolution of 30 km, for the period 1 June to 30 September in wetter than average (1999) and drier than average (2000) monsoon seasons. These two model runs are then repeated with a prescribed precipitation rate anomaly in July over the entire NAM region, and comparisons made between atmospheric and land-surface states in the two control runs and the two runs with anomalous precipitation. The results show that size and importance of soil moisture–precipitation feedbacks in the NAM region have substantial interannual variability, and that the resulting behaviour has a strong dependency on the intensity of the prescribed precipitation anomaly. It is also shown that a marked precipitation anomaly in the NAM region results in modified soil moisture, rainfall, and surface temperature, which persist for about one month, and that a precipitation anomaly within the NAM region not only has an impact on soil moisture locally, but also causes a remote, downwind soil moisture anomaly one month later. Analysis of the modelled response to the soil moisture anomaly indicates that not only land–atmosphere interactions, but also the large-scale atmospheric circulation act together to determine the modified precipitation and soil moisture fields in the NAM system. Copyright © 2004 Royal Meteorological Society

In this study, the seasonal development of the North American monsoon system (NAMS), as simulated by a mesoscale model during a 22-yr simulation from 1980 through 2001, is assessed. Comparison between model simulations and observations shows that the model simulation reproduces the precipitation, skin temperature, and wind field patterns in the seasonal development (May–July) of the NAMS reasonably well and that the mesoscale features and spatial heterogeneity of the NAMS are described correctly. The onset of the monsoon in the central and southern Sierra Madre Occidental (SMO) in Mexico occurs on 20 June, about 2 weeks earlier than the onset in Sonora, Mexico (6 July), the Sonoran Desert, and central Arizona and New Mexico (8 July). The temperature in Mexico is highest after the onset of the monsoon and then decreases with the increasing monsoon rainfall. However, the temperature in the Sonoran Desert and central Arizona and New Mexico is highest just prior to the onset of the monsoon, and high ...

To the EditorPlatelets may affect tumor cell proliferation and angiogenesis by releasing several cytokines including platelet-derived growth factor and vascular endothelial growth factor, among oth...

Meteorological radar is a remote sensing system that provides rainfall estimations at high spatial and temporal resolutions. The radar-based rainfall intensities ( R) are calculated from the observed radar reflectivities ( Z). Often, rain gauge rainfall observations are used in combination with the radar data to find the optimal parameters in the Z‐R transformation equation. The scale dependency of the power-law Z‐R parameters when estimated from radar reflectivity and rain gauge intensity data is explored herein. The multiplicative ( a) and exponent (b) parameters are said to be ‘‘scale dependent’’ if applying the observed and calculated rainfall intensities to objective function at different scale results in different ‘‘optimal’’ parameters. Radar and gauge data were analyzed from convective storms over a midsize, semiarid, and well-equipped watershed. Using the root-mean-square difference (rmsd) objective function, a significant scale dependency was observed. Increased time- and space scales resulted in a considerable increase of the a parameter and decrease of the b parameter. Two sources of uncertainties related to scale dependency were examined: 1) observational uncertainties, which were studied both experimentally and with simplified models that allow representation of observation errors; and 2) model uncertainties. It was found that observational errors are mainly (but not only) associated with positive bias of the b parameter that is reduced with integration, at least for small scales. Model errors also result in scale dependency, but the trend is less systematic, as in the case of observational errors. It is concluded that identification of optimal scale for Z‐R relationship determination requires further knowledge of reflectivity and rainintensity error structure.

The importance of high-resolution rainfall data to understanding the intricacies of the dynamics of hydrological processes and describing them in a sophisticated and accurate way has been increasingly realized. The last decade has witnessed a number of studies and numerous approaches to the possibility of transformation of rainfall data from one scale to another, nearly unanimously pointing to such a possibility. However, an important limitation of such approaches is that they treat the rainfall process as a realization of a stochastic process, and therefore there seems to be a lack of connection between the structure of the models and the underlying physics of the rainfall process. The present study introduces a new framework based on the notion of deterministic chaos to investigate the behavior of the dynamics of rainfall transformation between different temporal scales aimed toward establishing this connection. Rainfall data of successively doubled resolutions (i.e., 6, 12, 24, 48, 96, and 192 hours) observed at Leaf River basin, in the state of Mississippi, United States of America, are studied. The correlation dimension method is employed to investigate the presence of chaos in the rainfall transformation. The finite and low correlation dimensions obtained for the distributions of weights between rainfall data of different scales indicate the existence of chaos in the rainfall transformation, suggesting the applicability of a chaotic model. The formulation of a simple chaotic disaggregation model and its application to the Leaf River rainfall data provides encouraging results with practical potential. The disaggregation model results themselves indicate the presence of chaos in the dynamics of rainfall transformation, providing support for the results obtained using the correlation dimension method.

PERSIANN, an automated system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks, has been developed for the estimation of rainfall from geosynchronous satellite longwave infared imagery (GOES–IR) at a resolution of 0.25° × 0.25° every half–hour. The accuracy of the rainfall product is improved by adaptively adjusting the network parameters using the instantaneous rain–rate estimates from the Tropical Rainfall Measurement Mission (TRMM) microwave imager (TMI product 2A12), and the random errors are further reduced by accumulation to a resolution of 1° × 1° daily. The authors' current GOES–IR–TRMM TMI based product, named PERSIANN–GT, was evaluated over the region 30°S–30°N, 90°E–30°W, which includes the tropical Pacific Ocean and parts of Asia, Australia, and the Americas. The resulting rain–rate estimates agree well with the National Climatic Data Center radar–gauge composite data over Florida and Texas (correlation coefficient r > 0.7). The product al...

We examined two techniques that adjust the parameters of the GOES Precipitation Index (GPI) by combining the polar microwave and the geosynchronous infrared observations at three frequencies: daily, pentad, and monthly. The first technique is the adjusted GPI (AGPI), and the second is the universally adjusted GPI (UAGPI). The study shows that rainfall estimates can be improved by frequent calibrations providing there is sufficient superior (microwave) rainfall sampling within the calibration time and space domain. For this work, daily and pentad calibrations produce monthly rainfall estimates almost as good as monthly calibration. The daily calibration produced better daily rainfall estimates than pentad and monthly calibration, but it generates similar pentad rainfall estimates to these of the pentad calibration. The monthly calibrated scheme is not suitable for the daily and pentad rainfall estimates. Under the current twice-per-day sampling rate of polar-orbiting microwave observations, the pentad calibration scheme is suggested for the monthly, pentad, and daily rainfall. The potentials of applying the UAGPI and the AGPI techniques for daily rainfall estimation are also investigated.

We developed and evaluated a three-layer snow model for application in general circulation models. This one-dimensional snow model has many features of the detailed physically based model SNTHERM, yet is computationally much simpler. We have also extended the point model to vegetated areas using the parameter-ization concepts of the Biosphere-Atmosphere Transfer Scheme (BATS). Results of model applications for two types of vegetated fields - a short grassland in the French Alps and an old aspen forest in the southern study area of BOREAS - were presented. The results, on one hand, indicate the suitability of the model structure and parameter setting; on the other hand, the results explore the limitation of using 'point' field observations to evaluate an area model.

An evaluation of the Biosphere Atmosphere Transfer Scheme (BATS) snow submodel was conducted, both in a stand-alone mode and within the National Center for Atmospheric Research (NCAR) Community Climate Model version 3 (CCM3). We evaluated, in the stand-alone mode, the performance of BATS parameterizations at local scales using ground-based observations from the former Soviet Union and from Mammoth Mountain, California. The BATS snow scheme reproduces well the seasonal evolution of snow water equivalent in both sites, and the results for the Mammoth Mountain site compare well with those from a more complex, physically based model (SNTHERM), In the coupled mode, we evaluated the modelled snow cover extent, snow mass, precipitation and temperature from BATS as linked to the NCAR CCM3 using available observations. The coupled models capture the broad pattern of seasonal and geographical distribution of snow cover, with better overall performance than the passive microwave snow data derived from the Nimbus-7 Scanning Multi-channel Microwave Radiometer (SMMR) which generally underestimates snow depth. In terms of continents, the snow mass is better simulated during the accumulation period than during the melt period, which is the case for both North America and Eurasia, The simulation of snow mass, precipitation and air temperature for North America is slightly better than that for Eurasia. A rigorous evaluation of snow simulations in coupled land-atmosphere models requires high quality global datasets of snow cover extent, snow depth and snow water equivalent. The available datasets and model outputs are not yet ready to fulfil this objective.

A new cloud-patch method for the identification and removal of no-rain cold clouds from infrared (IR) imagery is presented. A cloud patch is defined as a cluster of connected IR imagery pixels that are colder than a given IR brightness temperature threshold. The threshold is derived through a combination of the rainfall field estimated from microwave observations and the IR data closely coincident with microwave sensor satellite overpasses. Seven cloud-patch features are used to describe cloud-top properties, including six IR based and one VIS based. The ID3 algorithm is used to extract structural knowledge from a training dataset and to produce classification rules expressed explicitly on the values of various patch features; these rules can be used to explain the physical principles underlying the cloud classification. The method was evaluated for the Japanese islands and surrounding oceans using AIP/1 data for June (training period) and July–August (evaluation period) 1989. The results of iden...

The soil-moisture nudging technique suggests using model forecast errors in near-surface air temperature and relative humidity to re-initialize (update) soil moisture in atmospheric models. This study investigates the application of soil-moisture nudging using a single-column version of the European Centre for Medium-Range Weather Forecasts (ECMWF) model. the model was applied at 16 sites selected to sample a range of climates and land covers across the globe, with atmospheric forcing taken from the ECMWF operational analysis for 15 June 1994 and 15 December 1994. When observation errors are set to zero, the Optimal Interpolation technique for deriving estimates of nudging coefficients shows computational instability because strong correlation between near-surface temperature and near-surface relative humidity forecast errors makes the coefficient matrix of the linear equations ill-posed. Therefore, Principal Component Analysis (PCA) is used as a pre-processor to identify the independent and dependent components of temperature and relative humidity errors. With PCA, the soilmoisture nudging coefficients appropriate to the dominant principal component become stable and effective for correcting soil moisture within days. Such coefficients, when derived for the northern hemisphere summer over the 16 sites, are sufficiently consistent to propose using their all-site, daily-average values in a globally applicable soil-moisture analysis scheme. Tests of this method at the First ISLSCP (International Satellite Land Surface Climatology Program) Field Experiment (FIFE) site confirm that soil-moisture nudging can provide good estimates of near-surface weather variables and surface fluxes in a numerical weather prediction (NWP) model which gives poor simulation of precipitation (thereby poor soil moisture). However, the PCA method is unable to give an accurate determination of the location of the soil moisture within soil layers that are accessible to the atmosphere. Further, these tests show that, if the NWP model has good simulation of precipitation but poor simulation of surface radiation, soil-moisture nudging could wrongly vary the soil moisture so as to provide good estimates of the surface sensible-heat flux, but poor estimates of surface latent-heat flux. an approach to distinguish error sources from surface radiation and soil moisture is necessary for further improvement.

Satellite-based remotely sensed data have the potential to provide hydrologically relevant information about spatially and temporally varying physical variables. A methodology for estimating such variables from multichannel remotely sensed data is presented; the approach is based on a modified counterpropagation neural network (MCPN) and is both effective and efficient at building complex nonlinear input-output function mappings from large amounts of data. An application to high-resolution estimation of the spatial and temporal variation of surface rainfall using geostationary satellite infrared and visible imagery is presented. Test results also indicate that spatially and temporally sparse ground-based observations can be assimilated via an adaptive implementation of the MCPN method, thereby allowing on-line improvement of the estimates.

A system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) is under development at The University of Arizona. The current core of this system is an adaptive Artificial Neural Network (ANN) model that estimates rainfall rates using infrared satellite imagery and ground-surface information. The model was initially calibrated over the Japanese Islands using remotely sensed infrared data collected by the Geostationary Meteorological Satellite (GMS) and ground-based data collected by the Automated Meteorological Data Acquisition System (AMeDAS). The model was then validated for both the Japanese Islands (using GMS and AMeDAS data) and the Florida peninsula (using GOES-8 and NEXRAD data). An adaptive procedure is used to recursively update the network parameters when ground-based data are available. This feature dramatically improves the estimation performance in response to the diverse precipitation characteristics of different geographical regio...

Water resources decision making in semiarid regions is driven by the need to have reliable water supplies of adequate quality, in the face of stressed surface water, and dwindling groundwater supplies. In recent years, remote sensing data sets have emerged as major sources of information on hydrologic variables. In this chapter, after a brief discussion on remote sensing of precipitation, the potential applications of satellite-based data to drought monitoring, flood forecasting and water resources management are highlighted. It is hoped that this introduction motivates greater utilization of satellite data in water resources management and decision making.

[1] NASA's MODIS global snow-covered area (SCA) products are one of the mission's major objectives that frequently contain cloud hindrances, which degrade their practical usability. Many techniques have been developed to mitigate the problem but with no assurance of eliminating all of the clouds. An image-processing algorithm with its kernel based on the variational interpolation theorem is developed to automatically obtain cloud-free dynamic SCA maps from MODIS. Two cases consisting of “accumulation” and “melting” phases are processed and validated using observations at 121 ground-snow sensors over the Sierra Nevada Mountains in California. The results show that the algorithm cleared all the cloud hindrance over the period of study. In terms of accuracy, the retrieved cloud-free snow cover for the accumulation case had an average omission error of around 22.5% and average commission error of around 2.1%, as compared to all available ground sensors. These high percentages of errors basically came from the input data of Terra and Aqua, which had omission errors of 14.3% and 20.2% (and the commission errors of ∼0.5%), respectively. For the melting case, when there were fewer clouds and hence more sensors available, the errors of omission and commission between the algorithm and direct observations from Terra and Aqua were close to each other (5.7–5.0% for omission and 0% for commission).