Simulation, Precursor Analysis and Targeted Observation Sensitive Area Identification for Two Types of ENSO using ENSO-MC v1.0

The global impact of an El Niño-Southern Oscillation (ENSO) event can differ greatly depending on whether it is an Eastern-Pacific-type (EP-type) event or a Central-Pacific-type (CP-type) event. Reliable predictions of the two types of ENSO are therefore of critical importance. Here we construct a deep neural network with multichannel structure for ENSO (named ENSO-MC) to simulate the spatial evolution of sea surface temperature (SST) anomalies for the two types of events. We select SST, heat content, and wind stress (i.e., three key ingredients of Bjerknes feedback) to represent coupled ocean-atmosphere dynamics that underpins ENSO, achieving skillful forecasts for the spatial patterns of SST anomalies out to one year ahead. Furthermore, it is of great significance to analyze the precursors of EP-type or CP-type events and identify targeted observation sensitive area for the understanding and prediction of ENSO. Precursors analysis is to determine what type of initial perturbations will develop into EP-type or CP-type events. Sensitive area identification is to determine the regions where initial states tend to have greatest impacts on evolution of ENSO. We use saliency map method to investigate the subsurface precursors and identify the sensitive areas of ENSO. The results show that there are pronounced signals in the equatorial subsurface before EP events, while the precursory signals of CP events are located in the North Pacific. It indicates that the subtropical precursors seem to favor the generation of the CP-type El Niño and the EP-type El Niño is more related to the tropical thermocline dynamics. And the saliency maps show that the sensitive areas of the surface and the subsurface are located in the equatorial central Pacific and the equatorial western Pacific, respectively. The sensitivity experiments imply that additional observations in the identified sensitive areas can improve forecasting skills. Our results of precursors and sensitive areas are consistent with the previous theories of ENSO, demonstrating the potential usage and advantages of the ENSO-MC model in improving the simulation, understanding and observations of two ENSO types.

Improving the thermal structure predictions in the Yellow Sea by conducting targeted observations in the CNOP-identified sensitive areas

Targeted observation is an appealing procedure for improving model predictions. However, studies on oceanic targeted observations have been largely based on modeling efforts, and there is a need for field validating operations. Here, we report the results of a field targeted observation that is designed based on the sensitive areas identified by the Conditional Nonlinear Optimal Perturbation approach to improve the 7th day thermal structure prediction in the Yellow Sea. By introducing the technique of cycle data assimilation and the new concept of time-varying sensitive areas, an observing strategy is designed and validated by a set of Observing System Simulation Experiments. Then, the impact of targeted observations was investigated by a choreographed field campaign in the summer of 2019. The results of the in-field Observing System Experiments show that, compared to conventional local data assimilation, conducting targeted observations in the sensitive areas can yield more benefit at the verification time. Furthermore, dynamic analysis demonstrates that the refinement of vertical thermal structures is mainly caused by the changes in the upstream horizontal temperature advection driven by the Yellow Sea Cold Water Mass circulation. This study highlights the effectiveness of targeted observations on reducing the forecast uncertainty in the ocean.

Fretting Wear Observation of Flat Contacts

With the instrumentality of a newly developed fretting test bench for planar contacts, a state-of-the-art method of inter-mediate imaging helps to understand fretting wear mechanisms of different materials and lubricants. The test bench uses application like planar surfaces unlike the usual point or elliptical contact in model testing (with the tribological test chain). Applications considered prone to fretting wear have large planar contacts – like bearing seats and shaft hub connections – and contact pressures normally perceiv ed as low or uncritical. This article examines a method to evaluate a targeted observation of the surfaces. The method uses a movable upper sample to open the contact and to document an interim status of the test by image recording. Among other things, this is to obtain time-lapse recordings of the progressive wear and tear. Just opening the contact can already influence the tribological system and the result of a test. It is shown whether and how this opening process has an impact on tests with continuous contact.

Factors Influencing Ensemble Sensitivity–Based Targeted Observing Predictions at Convection-Allowing Resolutions

Ensemble sensitivity analysis (ESA) is applied to select types of observations, in various locations and in advance of forecast convection, to systematically evaluate the effectiveness of ESA-based observation targeting for 10 convection forecasts. To facilitate the analysis, observing system simulation experiments and perfect models are utilized to generate synthetic targeted observations of temperature and pressure for future assimilation with an ensemble prediction system. Various observation assimilation experiments are carried out to assess the impacts of nonlinearity, covariance localization, and numerical noise on ESA-based observation-impact predictions. It is discovered that localization applied during data assimilation restricts targeted-observation increments onto the forecast responses of composite reflectivity and 3-hourly accumulated precipitation, making impact predictions poor. In addition, numerical noise introduced by nonlinear perturbation evolution tends to reduce the correlations between observed and predicted impacts; small, random-perturbation experiments often yielded similar impacts on forecasts as targeted observations. Nonlinearity also manifests in the observation impacts when comparing targeted observations with nontargeted, randomly chosen observations; random observations have seemingly the same impact on forecasts as targeted observations. The results, under idealized conditions and simplified ensemble configurations, demonstrate that ESA-based targeting for nonlinear convection forecasts may be most applicable at short time scales. Important implications for ESA-based targeting methods employed with real-time ensemble systems are also discussed.

STUDENTS’ COGNITIVE AUTONOMY FORMATION IN THE PROCESS OF ENGLISH FOR SPECIFIC PURPOSES LEARNING: A MODEL OF IT IMPLEMENTATION INTO TRADITIONAL TEACHING

Due to the rapid development of new information technologies, educational techniques require rethinking of approaches to teaching methods and tools to fulfill the needs of learners regarding their training for the future job. The educators should consider not only the interests of students but the demands of employers who are looking for quick-thinking specialists ready to acquire new knowledge and skills in short terms. The article presents a model for teaching ESP (English for Specific Purposes) which facilitates formation of students’ cognitive autonomy. On the basis of scientific and theoretical analysis of this issue, the author considers the methodological approaches to its elucidating. To clarify the structure of students’ cognitive autonomy, the author provides the components and their subjective links on the subcomponents level on the hierarchical basis. The author determines the criteria and levels of students’ cognitive autonomy as well as subjective and objective factors influencing the efficiency of students’ cognitive autonomy in ESP study which were obtained through methods of mass survey (interviews, conversations, questioning), targeted observation of the formation of students’ cognitive autonomy; methods of testing and ranking. As a result, the author designed and implemented into practice a model for teaching ESP, which represents a flexible educational environment with a suitable choice of teaching techniques, modes, strategies, tools, and resources on the basis of the integral unity of information technologies and traditional teaching technologies. The stages of the experimental teaching included the following: preparing the experiment, implementing, processing the results, and interpreting them. For the analysis and correlation of the income and the outcome sample data, the author applied statistical methods. Thus, the synthesis stage of the experiment allowed confirming the effectiveness of the implemented model for teaching ESP, which facilitates students’ cognitive autonomy formation.

Targeted observations based on identified sensitive areas by CNOP to improve the thermal structures prediction in the summer Yellow Sea: preparatory work for the campaign in the field

<p>Targeted observation is an appealing procedure to improve oceanic model predictions by taking additional assimilation of collected measurements. However, studies on targeted observation in the oceanic field have been largely based on modeling efforts, and there is a need for field validating observations. Here, we report the preparatory work of a field campaign, which is designed based on the identified sensitive area by the Conditional Nonlinear Optimal Perturbation (CNOP) approach, to improve the short-range summer thermal structures prediction in the Yellow Sea (YS). We firstly simulated the hindcasting (2016-2018) temperature structures in the summertime, and found that the locations of the sensitive areas are generally consistent in space for each hindcast year. Then, we introduced the technique of multiple-assimilation and the definition of time-varying sensitive area, and designed observing strategies for the YS summer campaign. Observing System Simulation Experiments (OSSEs) were conducted prior to address the plan on field campaign in the Yellow Sea in August 2019. Results show that, reducing the initial errors in the sensitive area can lead to more improvement on thermal structures prediction than that in other area.</p>

A Flow-dependent Targeted Observation Method

<p>In this study, we developed a flow-dependent ensemble-based targeted observation method, by minimizing the analysis error variance under the framework of Ensemble Kalman filter (EnKF) data assimilation system. This method estimates the background error statistics as a flow dependent function. The covariance localization is also introduced for computing efficiency and alleviating the spurious observations.  As a test bed, an  optimal observation array of sea level anomalies (SLA) is designed for its seasonal prediction over the tropical Indian Ocean (TIO) region.  Furthermore, the observing system simulation experiments (OSSEs) is used to verify the resultant optimal observational array using our recently developed coupled data assimilation system. A comparison between this flow-dependent method and the traditional method is also given. ​</p>