ENSO Predictions in an Intermediate Coupled Model Influenced by Removing Initial Condition Errors in Sensitive Areas： A Target Observation Perspective

Previous studies indicate that ENSO predictions are particularly sensitive to the initial conditions in some key areas（socalled ＂sensitive areas＂）. And yet, few studies have quantified improvements in prediction skill in the context of an optimal observing system. In this study, the impact on prediction skill is explored using an intermediate coupled model in which errors in initial conditions formed to make ENSO predictions are removed in certain areas. Based on ideal observing system simulation experiments, the importance of various observational networks on improvement of El Ni n？o prediction skill is examined. The results indicate that the initial states in the central and eastern equatorial Pacific are important to improve El Ni n？o prediction skill effectively. When removing the initial condition errors in the central equatorial Pacific, ENSO prediction errors can be reduced by 25%. Furthermore, combinations of various subregions are considered to demonstrate the efficiency on ENSO prediction skill. Particularly, seasonally varying observational networks are suggested to improve the prediction skill more effectively. For example, in addition to observing in the central equatorial Pacific and its north throughout the year,increasing observations in the eastern equatorial Pacific during April to October is crucially important, which can improve the prediction accuracy by 62%. These results also demonstrate the effectiveness of the conditional nonlinear optimal perturbation approach on detecting sensitive areas for target observations.

Interacting mechanism and initiation prediction of multiple cracks

The maximum Mode Ⅰ and Mode Ⅱ stress intensity factors(SIFs), KI,kmax(θ) and KII,kmax(θ)(0°<θ<360°), of inclined parallel multi-crack varying with relative positions(including horizontal and vertical spacings) are calculated by the complex function and integration method to analyze their interacting mechanism and determine the strengthening and weakening zone of SIFs. The multi-crack initiation criterion is established based on the ratio of maximum tension-shear SIF to predict crack initiation angle, load, and mechanism. The results show that multi-crack always initiates in Mode Ⅰ and the vertical spacing is better not to be times of half crack-length for crack-arrest, which is in good agreement with test results of the red-sandstone cube specimens with three parallel cracks under uniaxial compression. This can prove the validity of the multi-crack initiation criterion.

Prediction of low-cycle crack initiation life of powder superalloy FGH96 with inclusions based on damage mechanics

The effects of inclusions in powder superalloy FGH96 on low-cycle fatigue life were studied, and a low-cycle crack initiation life prediction model based on the theory of damage mechanics was proposed. The damage characterization parameter was proposed after the construction of damage evolution equations. Fatigue tests of the powder superalloy specimens with and without inclusion were conducted at 530 and 600 ℃, and the model verification was carried out for specimens with elliptical, semi-elliptical, polygon and strip-shaped surface/subsurface inclusion. The stress analysis was performed by finite element simulation and the predicted life was calculated. The results showed a satisfying agreement between predicted and experimental life.

Gaps,challenges and possible solution for prediction of wheel-rail rolling contact fatigue crack initiation

The prediction of wheel/rail rolling contact fatigue(RCF)crack initiation during railway operations is an important task.Since RCF crack evolution is influenced by many factors,its prediction process is complex.This paper reviews the existing approaches to predict RCF crack initiation.The crack initiation region is predicted by the shakedown map.By combining the shakedown map with various initiation criteria and the critical plane method,the crack initiation life is calculated.The classification,methodologies,theories and applications of these approaches are included in this paper.The advantages and limitations of these methods are analyzed to provide recommendation for RCF crack initiation prediction.This review highlights that wheel/rail dynamic characteristic,complex working conditions,surface defects and wear all affect the RCF crack initiation.The optimal selection of criteria is essential in the crack initiation prediction.Based on the research gap regarding the challenging process of crack initiation prediction detailed in this review,a proposed prediction process of RCF crack initiation is proposed to achieve a more accurate result.

Improved decadal climate prediction in the North Atlantic using EnOI-assimilated initial condition

Decadal prediction experiments of Beijing Climate Center climate system model version 1.1 （BCC- CSM1.1） participated in Coupled Model Intercomparison Project Phase 5 （CMIPS） had poor skill in extratropics of the North Atlantic, the initialization of which was done by relaxing modeled ocean temperature to the Simple Ocean Data Assimilation （SODA） reanalysis data. This study aims to improve the prediction skill of this model by using the assimilation technique in the initialization. New ocean data are firstly generated by assimilating the sea surface temperature （SST） of the Hadley Centre Sea Ice and Sea Surface Temperature （HadlSST） dataset to the ocean model of BCC-CSM1.1 via Ensemble Optimum Interpolation （EnOI）. Then a suite of decadal reforecasts launched annually over the period 1961- 2005 is carried out with simulated ocean temperature restored to the assimilated ocean data. Comparisons between the re-forecasts and previous CMIP5 forecasts show that the re-forecasts are more skillful in mid-to-high latitude SST of the North Atlantic. Improved prediction skill is also found for the Atlantic multi-decadal oscillation （AMO）, which is consistent with the better skill of Atlantic meridional overturning circulation （AMOC） predicted by the re-forecasts. We conclude that the EnOl assimilation generates better ocean data than the SODA reanalysis for initializing decadal climate prediction of BCC-CSM1.1 model.

Case studies of ERS-1 altimeter data applicating in China Sea

The impact of ERS-1 altimeter significant wave height on analysis of wave field and wave pre- dictions is tested through analysis of selected cases. Application of the altimeter data may modifg initial tield and thus 24-hour prediction of significant wave height. However the variations in initial wave field almost make no effect on 48-hour predictions.

Feature Selection for the Prediction of Translation Initiation Sites

Translation initiation sites (TISs) are important signals in cDNA sequences. In many previous attempts to predict TISs in cDNA sequences, three major factors affect the prediction performance: the nature of the cDNA sequence sets, the rel- evant features selected, and the classification methods used. In this paper, we examine different approaches to select and integrate relevant features for TIS pre- diction. The top selected significant features include the features from the position weight matrix and the propensity matrix, the number of nucleotide C in the se- quence downstream ATG, the number of downstream stop codons, the number of upstream ATGs, and the number of some amino acids, such as amino acids A and D. With the numerical data generated from these features, different classifi- cation methods, including decision tree, naive Bayes, and support vector machine, were applied to three independent sequence sets. The identified significant features were found to be biologically meaningful, while the experiments showed promising results.

On the “spring predictability barrier” for strong El Nio events as derived from an intermediate coupled model ensemble prediction system

Using predictions for the sea surface temperature anomaly(SSTA) generated by an intermediate coupled model(ICM)ensemble prediction system(EPS), we first explore the "spring predictability barrier"(SPB) problem for the 2015/16 strong El Nio event from the perspective of error growth. By analyzing the growth tendency of the prediction errors for ensemble forecast members, we conclude that the prediction errors for the 2015/16 El Nio event tended to show a distinct season-dependent evolution, with prominent growth in spring and/or the beginning of the summer. This finding indicates that the predictions for the 2015/16 El Nio occurred a significant SPB phenomenon. We show that the SPB occurred in the 2015/16 El Nio predictions did not arise because of the uncertainties in the initial conditions but because of model errors. As such, the mean of ensemble forecast members filtered the effect of model errors and weakened the effect of the SPB, ultimately reducing the prediction errors for the 2015/16 El Nio event. By investigating the model errors represented by the tendency errors for the SSTA component,we demonstrate the prominent features of the tendency errors that often cause an SPB for the 2015/16 El Nio event and explain why the 2015/16 El Nio was under-predicted by the ICM EPS. Moreover, we reveal the typical feature of the tendency errors that cause not only a significant SPB but also an aggressively large prediction error. The feature is that the tendency errors present a zonal dipolar pattern with the west poles of positive anomalies in the equatorial western Pacific and the east poles of negative anomalies in the equatorial eastern Pacific. This tendency error bears great similarities with that of the most sensitive nonlinear forcing singular vector(NFSV)-tendency errors reported by Duan et al. and demonstrates the existence of an NFSV tendency error in realistic predictions. For other strong El Nio events, such as those that occurred in 1982/83 and 1997/98, we obtain the tendency errors of the NFSV structure, which cause a significant SPB and yield a much larger prediction error. These results suggest that the forecast skill of the ICM EPS for strong El Nio events could be greatly enhanced by using the NFSV-like tendency error to correct the model.