Initial Error-induced Optimal Perturbations in ENSO Predictions, as Derived from an Intermediate Coupled Model

The initial errors constitute one of the main limiting factors in the ability to predict the E1 Nino-Southem Oscillation （ENSO） in ocean-atmosphere coupled models. The conditional nonlinear optimal perturbation （CNOP） approach was em- ployed to study the largest initial error growth in the E1 Nino predictions of an intermediate coupled model （ICM）. The optimal initial errors （as represented by CNOPs） in sea surface temperature anomalies （SSTAs） and sea level anomalies （SLAs） were obtained with seasonal variation. The CNOP-induced perturbations, which tend to evolve into the La Nifia mode, were found to have the same dynamics as ENSO itself. This indicates that, if CNOP-type errors are present in the initial conditions used to make a prediction of E1 Nino, the E1 Nino event tends to be under-predicted. In particular, compared with other seasonal CNOPs, the CNOPs in winter can induce the largest error growth, which gives rise to an ENSO amplitude that is hardly ever predicted accurately. Additionally, it was found that the CNOP-induced perturbations exhibit a strong spring predictability barrier （SPB） phenomenon for ENSO prediction. These results offer a way to enhance ICM prediction skill and, particularly, weaken the SPB phenomenon by filtering the CNOP-type errors in the initial state. The characteristic distributions of the CNOPs derived from the ICM also provide useful information for targeted observations through data assimilation. Given the fact that the derived CNOPs are season-dependent, it is suggested that seasonally varying targeted observations should be implemented to accurately predict ENSO events.

The Relationship between the El Nio/La Nia Cycle and the Transition Chains of Four Atmospheric Oscillations. Part Ⅱ:The Relationship and a New Approach to the Prediction of El Nio

ABSTRACT The authors explored the connection and transition chains of the Northern Oscillation （NO） and the North Pacific Oscilla tion （NPO）, the Southern Oscillation （SO）, and the Antarctic Oscillation （AAO） on the interannual timescale in a companion paper. In this study, the connection between the transition chains of the four oscillations （the NO and NPO, the SO and AAO） and the El Nifio/La Nifia cycle were examined. It was found that during the transitions of the four oscillations, alternate anticyclonic/cyclonic correlation centers propagated from the Western Pacific to the Eastern Pacific along both sides of the equator. Between the anticyclonic/cyclonic correlation centers, the zonal wind anomalies also moved eastwardly, favoring the advection of sea surface temperature anomalies from the tropical Western Pacific to the Eastern Pacific. When the anti cyclonic anomalies arrived in the Eastern Pacific, the positive phase of NO/SO and La Nifia were established and vice versa. Thus, in 4-6 years, with an entire transition chain of the four oscillations, an E1 Nifio/La Nifia cycle completed. The eastward propagation of the covarying anomalies of the sea level pressure, zonal wind, and sea surface temperature was critical to the transition chains of the four oscillations and the cycle of E1 Nifio/La Nifia. Based on their close link, a new empirical prediction method of the timing of E1 Nifio by the transition chains of the four oscillations was proposed. The assessment provided confidence in the ability of the new method to supply information regarding the long-term variations of the ocean and atmosphere in the tropical Pacific.

Predicting corn digestible and metabolizable energy content from its chemical composition in growing pigs

Background： The nutrient composition of corn is variable. To prevent unforeseen reductions in growth performance, grading and analytical methods are used to minimize nutrient variability between calculated and analyzed values. This experiment was carried out to define the sources of variation in the energy content of corn and to develop a practical method to accurately estimate the digestible energy （DE） and metabolisable energy （ME） content of individual corn samples for growing pigs. Twenty samples were taken from each of five provinces in China （Jilin, Hebei, Shandong, Liaoning, and Henan） to obtain a range of quality. Results： The DE and ME contents of the 100 corn samples were measured in 3.5.3 ±1.92 kg growing pigs （six pigs per corn sample）. Sixty corn samples were used to build the prediction model; the remaining forty samples were used to test the suitability of these models. The chemical composition of each corn sample was determined, and the results were used to establish prediction equations for DE or ME content from chemical characteristics. The mean DE and ME content of the 100 samples were 4,053 and 3,923 kcal/kg （dry matter basis）, respectively. The physical characteristics were determined, as well, and the results indicated that the bulk weight and 1,000-kernel weight were not associated with energy content. The DE and ME values could be accurately predicted from chemical characteristics. The best fit equations were as follows： DE, kcal/kg of DM = 1062.68 ＋ （49.72 ×EE） ＋ （0.54 × GE） ＋ （9.1 ] x starch）, with R^2 = 0.62, residual standard deviation （RSD） = 48 kcal/kg, and P 〈 0.01; ME, kcal/kg of dry matter basis （DM） = 671.54 ＋ （0.89 ×DE） - （5.57 × NDF） - （191.39 ×ash）, with R^2 = 0.87, RSD = 18 kcal/kg, and P〈 0.01. Conclusion： This experiment confirms the large variation in the energy content of corn, describes the factors that influence this variation, and presents equations based on chemical measurements that may be used to predict the DE and ME content of individual corn samples.

Predicted epitopes of H5N1 bird flu virus by bioinformatics method: a clue for further vaccine development

To the Editor： Bird flu or avian flu, caused by H5N1 virus, is a new emerging infectious disease. It is noted that this H5N1 virus jumped the species barrier and caused severe disease with high mortality in humans in many countries. The continued westward dissemination of H5N1 influenza A viruses in avian populations and the nearly 50% mortality of humans infected with H5N1 are a source of great international concern.1 Providing sufficient antiviral drugs and development and approval of new vaccines are the keys for control of the possible emerging pandemic of this atypical influenza.1＇2 Based on the advance in bioinformatics, the immunomics becomes a new alternative in vaccine development.3 Advanced technologies for vaccine development, such as genome sequence analysis, microarray, proteomics approach, high-throughput cloning, bioinformatics database tools and computational vaccinology can be applied for vaccine development of several diseases including new emerging diseases.

Nonlinear Distributed Model Predictive Control for Multiple Missiles Against Maneuvering Target with a Trajectory Predictor

This study aims to solve the problem of multi-missile simultaneous attacks on maneuvering target.The challenges include multimissile cooperative control and target's trajectory prediction.A controller based on nonlinear distributed model predictive control(NDMPC)is designed for multiple missiles against a maneuvering target,and a trajectory prediction inethod based on particle swarm optimization(PSO)algorithm is proposed.This study has mainly completed the following three aspects of work.Firstly,the cost function of the cont roller is constructed to optimize the accuracy and synchronization of the multi-missile system with consideration of collision avoidance.Secondly,the velocity control of the leading missile is designed by using the range-to-go in-formation in real time to ensure the attack fficiency and the control of the terminal velocity difference.Finally,a kinematic model of the target is cstimated by using short-term real-time data with the PSO algorithm.The established model is employed to predict the target trajectory in the interval between radar scans.Numerical simulation results of two different s enarios demonstrate the effectiveness of the proposed cooperative guidance approach.