A neural network atmospheric model for hybrid coupled modelling

 The possibility of using a nonlinear empirical atmospheric model for hybrid coupled atmosphere-ocean modelling has been examined by using a neural network (NN) model for predicting the contemporaneous wind stress field from the upper ocean state. Upper ocean heat content (HC) from a 6-layer ocean model was a better predictor of the wind stress than the (observed or modelled) sea surface temperature (SST). Our results showed that the NN model generally had slightly better skills in predicting the contemporaneous wind stress than the linear regression (LR) model in the off-equatorial tropical Pacific and in the eastern equatorial Pacific. When the wind stresses from the NN and LR models were used to drive the ocean model, slightly better SST skills were found in the off-equatorial tropical Pacific and in the eastern equatorial Pacific when the NN winds were used instead of the LR winds. Better skills for the model HC were found in the western and central equatorial Pacific when the NN winds were used instead of the LR winds. Why NN failed to show more significant improvement over LR in the equatorial Pacific for the wind stress and SST is probably because the relationship between the surface ocean and the atmosphere in the equatorial Pacific over the seasonal time scale is almost linear. Received: 2 March 1999 / Accepted: 13 July 2000     

Nonlinear principal component analysis by neural networks

Nonlinear principal component analysis (NLPCA) can be performed by a neural network model which nonlinearly generalizes the classical principal component analysis (PCA) method. The presence of local minima in the cost function renders the NLPCA somewhat unstable, as optimizations started from different initial parameters often converge to different minima. Regularization by adding weight penalty terms to the cost function is shown to improve the stability of the NLPCA. With the linear approach, there is a dichotomy between PCA and rotated PCA methods, as it is generally impossible to have a solution simultaneously (a) explaining maximum global variance of the data, and (b) approaching local data clusters. With the NLPCA, both objectives (a) and (b) can be attained together, thus the nonlinearity in NLPCA unifies the PCA and rotated PCA approaches. With a circular node at the network bottleneck, the NLPCA is able to extract periodic or wave modes. The Lorenz (1963) 3‐component chaotic system and the monthly tropical Pacific sea surface temperatures (1950–1999) are used to illustrated the NLPCA approach.     

Disaster risk assessment of ports based on the perspective of vulnerability

Global environmental changes have led to frequent occurrences of climatic extremes. The increasingly frequent and high-magnitude natural disasters in Taiwan have caused significant mortality, injury, and property damage. In response, there have been requests to improve the capacity to cope with extreme climatic conditions through increased awareness and identification of vulnerability. Disruptions to transportation systems affect the resilience for sustaining daily operations. Among the various types of transportation systems, ports provide substantial employment and industrial activity, contributing to national and regional development. In addition, ports integrate the functions of supply chains such as services in logistics, information, and business, becoming the location of industrial clusters. Therefore, this study examines the risk of port failures from the perspective of vulnerability. Specifically, seven vulnerable factors derived from the extant literature and lessons learned from the previous disaster cases are evaluated using geographic information systems. The results reveal that port capacity and efficiency have a significant effect on port vulnerability in which the efficiency of gantry cranes, labor productivity, free trade zone business volume, and ground access networks play crucial roles in port failure. Moreover, the risks associated with port operation are evaluated by overlapping a hazard map of areas prone to debris flows and tsunami inundation. The risk maps can assist decision makers in understanding the vulnerability and adopting appropriate strategies to minimize disaster risks.     

A Note on the Flux-Variance Similarity Relationships for Heat and Water Vapour in the Unstable Atmospheric Surface Layer

Atmospheric surface layer (ASL) experiments over the past 10 years demonstrate that the flux-variance similarity functions for water vapour are consistently larger in magnitude than their temperature counterpart. In addition, latent heat flux calculations using the flux-variance method do not compare as favorably to eddy- correlation measurements when compared to their sensible heat counterpart. These two findings, in concert with measured heat to water vapour transport efficiencies in excess of unity, are commonly used as evidence of dissimilarity between heat and water vapour transport in the unstable atmospheric surface layer. In this note, it is demonstrated that even if near equality in flux-profile similarity functions for heat and water vapour is satisfied, the flux-variance similarity functions for water vapour are larger in magnitude than temperature for a planar, homogeneous, unstably-stratified turbulent boundary-layer flow.