We design a new nonlinear model to construct the accurate relationship between auroral oval boundaries and 18 space physical parameters, and explore the influence of every single space physical parameter on auroral oval boundary in this paper. As a result, we found the combination of some space physical parameters can strengthen each other’s influence on aurora oval boundary prediction, and this model can achieve the best performance when only partial space physical parameters are used as input.

The borehole strain data of the Guza station are used to study the negentropy anomalies before the Wenchuan earthquake. We observed the distribution of anomalies in the skewness-kurtosis domain and accumulated the anomaly frequency over time. Our results show: 1) The earthquake moment is proved to be a critical time during the whole earthquake process. 2) Two cumulative acceleration phases are corresponding to the two crustal stress releases, which may be the precursors to the earthquake.

The soil radon anomalies apart from seismic activity are also governed and controlled by meteorological parameters. This is the major complication which arise for isolating the seismic induced precursory signals. Digital filter assists in eliminating the high frequency quasi periodic components from the time series and SSA method helps eliminating the diurnal and semi- diurnal fluctuations from soil radon time series for improved correlation of seismic induced soil radon emanation.

The COPSO-BP algorithm is proposed for transient electromagnetic inversion. The BP's initial weight and threshold parameters were trained by COPSO algorithm, which overcomes the shortcoming of single BP falls easily into local optimum. The layered geoelectric model inversion showed that COPSO-BP method has better accuracy, stability and relative less training times. It is expected to be used in 1-D direct current sounding, 1-D magnetotelluric sounding, seismic wave impedance, source detection.

The North Atlantic Oscillation (NAO) phenomenon has a significant impact on the global climate. In this paper, we propose a hybrid algorithm to identify the perturbations that trigger NAO events. The result indicates that the perturbations solved by our method can trigger the NAO mode successfully. Moreover, using the parallel framework, the speedup ratio of the parallel algorithm achieves 40 compared to the serial version.

A generic methodology is developed to estimate the model error and simulate the model uncertainty related to a specific physical process. The method estimates the model error by comparing two different representations of the physical process in otherwise identical models. The found model error can then be used to perturb the model and simulate the model uncertainty. When applying this methodology to deep convection an improvement in the probabilistic skill of the ensemble forecast is found.

In this paper, we rewrite the adaptive cooperation co-evolution of parallel particle swarm optimization and wolf search algorithm based on principal component analysis (ACPW) and applied it to solve conditional nonlinear optimal perturbation (CNOP) in the WRF-ARW for identifying sensitive areas of typhoon target observations. The experimental results show that the ACPW is meaningful, feasible and effective.

Neural networks are a technique that is widely used to predict the time-evolution of physical systems. For this the neural network is shown past evolution of the system – it is "trained" – and then can be used to predict the evolution in the future. We show some limitations in this approach for certain systems that are important to consider when using neural networks for climate and weather-related applications.

The conspicuous feature of the atmosphere ocean system is the strong difference in mass of the two media. In this respect there is a strong analogy to Brownian motion, with light and fast molecules colliding with heavy and slow Brownian particles. I apply the tools of non-equilibrium statistical mechanics to study Brownian motion to air-sea interaction.

A methodology is developed to simplify the mathematical description of activity in a time-dependent driven system. The method describes the response in the system that is most-closely related to the driver. This reduced description has advantages: low noise, high prediction efficiency, linearity in the described system response to the driver, and compactness. The analysis of the Earth’s magnetospheric system is demonstrated.