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.

Baroclinic and barotropic instabilities are well known as the processes responsible for the production of the most important energy-containing eddies in the atmospheres and oceans of the Earth and other planets. Linear and nonlinear instability theories provide insights into when such instabilities may occur, grow to large amplitude and saturate, with examples from the laboratory, simplified numerical models and planetary atmospheres. We conclude with a number of open issues for future research.

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.

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 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.

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.

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.

Geophysical flows span a broader range of temporal and spatial scales than can be resolved numerically. One way to alleviate the ensuing numerical errors is to combine simulations with measurements, taking account of the accuracies of these two sources of information. Here we quantify the distribution of numerical simulation errors without relying on high-resolution numerical simulations. Specifically, small-scale random vortices are added to simulations while conserving energy or circulation.

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

This study presents a novel method for reduced-rank data assimilation of multiscale highly nonlinear systems. Time-varying dynamical properties are used to determine the rank and projection of the system onto a reduced subspace. The variable reduced-rank method is shown to succeed over other fixed-rank methods. This work provides implications for performing strongly coupled data assimilation with a limited number of ensemble members on high dimensional coupled climate models.

We study a model for the relationship between space plasma and geomagnetic activity, by using an MHD shell model, where its forcing has been replaced by solar wind fluctuations data. We study the fractality of the forcing, its output and the activity of the model, which may represent the existence of geomagnetic storms. We find correlations between some of these metrics and the solar cycle, suggesting that the complexity of the solar wind may have influence on the level of geomagnetic activity.

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.

This paper aims at analysing and simulating correlations between two fields in a scale invariant framework. It starts by theoretically assessing and numerically confirming the behaviour of renormalized multiplicative power law combinations of two fields with known scale invariant properties. Then a new indicator of correlation is suggested and tested on rainfall data to study correlation between the common rain rate and drop size distribution features.

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.