A Further Study on an Extended Nonlinear Ocean-Atmosphere Coupled Hydrodynamic Characteristic System and the Abrupt Feature of ENSO Events

An extended ocean-atmosphere coupled characteristic system including thermodynamic physical processes in ocean mixed layer is formulated in order to describe SST explicitly and remove possible limitation of ocean-atmosphere coupling assumption in hydrodynamic ENSO models. It is revealed that there is a kind of abrupt nonlinear characteristic behaviour, which relates to rapid onset and intermittency of El Nino events, on the second order slow time scale due to the nonlinear interaction between a linear unstable low-frequency primary eigen component of ocean-atmosphere coupled Kelvin wave and its higher order harmonic components under a strong ocean-atmosphere coupling background. And, on the other hand, there is a kind of finite amplitude nonlinear characteristic behaviour on the second order slow time scale due to the nonlinear interaction between the linear unstable primary eigen component and its higher order harmonic components under a weak ocean-atmosphere coupling background in this model system.

STCANet:Spatiotemporal Coupled Attention Network for Ocean Surface Current Prediction

Currently,numerical models based on idealized assumptions,complex algorithms and high computational costs are unsatisfactory for ocean surface current prediction.Moreover,the complex temporal and spatial variability of ocean currents also makes the prediction methods based on time series data challenging.The deep network model can automatically learn and extract complex features hidden in large amount of complex data,so it is a promising method for high quality prediction of ocean currents.In this paper,we propose a spatiotemporal coupled attention deep network model STCANet that can extract abundant temporal and spatial coupling information on the behavior characteristics of ocean currents for improving the prediction accuracy.Firstly,Spatial Module is designed and implemented to extract the spatiotemporal coupling characteristics of ocean currents,and meanwhile the spatial correlations and dependencies among adjacent sea areas are obtained through Spatial Channel Attention Module(SCAM).Secondly,we use the GatedRecurrent-Unit(GRU)to extract temporal relationships of ocean currents,and design and implement the nearest neighbor time attention module to extract the interdependences of ocean currents between adjacent times,which can further improve the accuracy of ocean current prediction.Finally,a series of comparative experiments on the MediSea_Dataset and EastSea_Dataset showed that the prediction quality of our model greatly outperforms those of other benchmark models such as History Average(HA),Autoregressive Integrated Moving Average Model(ARIMA),Long Short-term Memory(LSTM),Gate Recurrent Unit(GRU)and CNN_GRU.

An explainable framework for load forecasting of a regional integrated energy system based on coupled features and multi-task learning

To extract strong correlations between different energy loads and improve the interpretability and accuracy for load forecasting of a regional integrated energy system(RIES),an explainable framework for load forecasting of an RIES is proposed.This includes the load forecasting model of RIES and its interpretation.A coupled feature extracting strat-egy is adopted to construct coupled features between loads as the input variables of the model.It is designed based on multi-task learning(MTL)with a long short-term memory(LSTM)model as the sharing layer.Based on SHapley Additive exPlanations(SHAP),this explainable framework combines global and local interpretations to improve the interpretability of load forecasting of the RIES.In addition,an input variable selection strategy based on the global SHAP value is proposed to select input feature variables of the model.A case study is given to verify the effectiveness of the proposed model,constructed coupled features,and input variable selection strategy.The results show that the explainable framework intuitively improves the interpretability of the prediction model.

Asymmetric Features for Two Types of ENSO

There are two types of ENSO,namely,the eastern Pacific（EP） ENSO that is characterized by the warmest（coldest） SST anomalies in the eastern equatorial Pacific,and the central Pacific（CP） ENSO whose maximum（minimum） SST anomalies are over the central equatorial Pacific.Asymmetric features of SST anomalies for the EP and CP types of ENSO events and their possible mechanisms were analyzed by using a variety of data during the period 1961-2010.The responses of atmospheric circulation to the two types of ENSO were also discussed.The results showed asymmetric features of SST anomalies in terms of spatial and temporal distributions and intensity.Although the dominant mechanisms differed at both development and decay stages,the oceanic vertical advection played a key role in the asymmetric intensity of the two ENSO events.In addition,both local and remote atmospheric responses showed strong asymmetric signals,which were consistent with the asymmetric distribution of SST anomalies.The asymmetric atmospheric responses in EP-ENSO（CP-ENSO） were similar to those associated with EP-El Nino（CP-La Nina）.The intensity of asymmetric responses related to the EP-ENSO was much stronger than that related to the CP-ENSO.

Multiple fault separation and detection by joint subspace learning for the health assessment of wind turbine gearboxes

The gearbox of a wind turbine （WT） has dominant failure rates and highest downtime loss among all WT subsystems. Thus, gearbox health assessment for maintenance cost reduction is of paramount importance. The concurrence of multiple faults in gearbox components is a common phenomenon due to fault induction mechanism. This problem should be considered before planning to replace the components of the WT gearbox. Therefore, the key fault patterns should be reliably identified from noisy observation data for the development of an effective maintenance strategy. However, most of the existing studies focusing on multiple fault diagnosis always suffer from inappropriate division of fault information in order to satisfy various rigorous decomposition principles or statistical assumptions, such as the smooth envelope principle of ensemble empirical mode decomposition and the mutual independence assumption of independent component analysis. Thus, this paper presents a joint subspace learning-based multiple fault detection （JSLMFD） technique to construct different subspaces adaptively for different fault pattems. Its main advantage is its capability to learn multiple fault subspaces directly from the observation signal itself. It can also sparsely concentrate the feature information into a few dominant subspace coefficients. Furthermore, it can eliminate noise by simply performing coefficient shrinkage operations. Consequently, multiple fault patterns are reliably identified by utilizing the maximum fault information criterion. The superiority of JSL-MFD in multiple fault separation and detection is comprehensively investigated and verified by the analysis of a data set of a 750 kW WT gearbox. Results show that JSL-MFD is superior to a state-of-the-art technique in detecting hidden fault patterns and enhancing detection accuracy.