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.

Statistical post-processing aims to increase the predictive skill of probabilistic ensemble weather forecasts by learning the statistical relation between historical pairs of observations and ensemble forecasts within a given training data set. This study compares four different training schemes and shows that including multiple years of data in the training set typically yields a more stable post-processing while it loses the ability to quickly adjust to temporal changes in the underlying data.

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 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.

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.

Seasonal prediction of the of the North Atlantic Oscillation (NAO) has been improved in recent years by improving dynamical models and ensemble predictions. One step therein was the so called sub-sampling, which combines statistical with dynamical prediction. This study generalises this approach and makes it much more accessible. Furthermore, it presents a new verification approach for such predictions.

We present a new way to adaptively improve weather forecasts by incorporating last-minute observation information. The recently measured error, together with a statistical model, gives us an indication about the expected future error of wind speed forecasts, which are then adjusted accordingly. This new technique can be especially beneficial for customers in the wind energy industry, where it is important to have reliable short-term forecasts, as well as providers of extreme weather warnings.

We deduce that after a global thermal perturbation, the atmosphere would need about a couple of months to come back to equilibrium.

The difference between the energy that the earth receives from the sun and the energy it emits as black body radiation is stored in a scaling hierarchies of structures in the ocean, soil and hydrosphere. The simplest scaling storage model leads to the Fractional Energy Balance Equation (FEBE). This paper examines the statistical properties of FEBE temperature responses when it is driven by random fluctuations.

Current space weather problems are discussed for young researchers. We have discussed some of the major problems that need to be solved for space weather forecasting to become a reality.

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.

We study a model containing the characteristic of self-similarity (invariance under scale) which allows for scaling between lab experiments and geographical scale seismicity. Particular to this model is the dependency of the earthquake rates on the magnitude difference between events that are causally connected. We present results of a statistical analysis of magnitude correlations for the model along with its implications for the ongoing efforts in earthquake forecasting.