Recent  

We reviewed our progress on strongly-coupled ensemble data assimilation with a hierarchy of coupled models, ranging from the simple coupled Lorenz model to the state-of-the-art coupled general circulation model CFSv2. We showed that strongly-coupled ensemble data assimilation provides the most accurate coupled analyses if using a sufficient ensemble. When the ensemble size is insufficient, applying the correlation-cutoff method can significantly improve the strongly-coupled analyses.

This research found that control the weather would change the chaotic behavior of atmospheric model. We proposed to introduce chaos theory in the weather control. Experimental results demonstrated that the proposed approach reduced the manipulations, including the control times and magnitudes, which throw light on the weather control in a real atmospheric model.

In ensemble Kalman filter, the ensemble space may not fully capture the forecast errors due to the limited ensemble size and systematic model errors, which affect the accuracy of analysis and prediction. This study proposes a new algorithm to use cost-free pseudomembers to properly expand the ensemble space and improve analysis accuracy during the analysis step, without increasing the ensemble size during forecasting.

In this study, we propose a new, computationally feasible approach for identifying the existence and CO₂ value of a possible "tipping point" in winter Arctic sea ice area – the point after which sea ice could undergo abrupt and irreversible loss. Our results will help us better predict and understand abrupt elements of climate change, which threaten the ability of human and ecological systems to adapt to a warming world.

Our paper introduces very powerful methods of finding the effective behavior of complex systems, which can be applied well beyond the initial setting of sea ice. Applications include transport properties of porous and polycrystalline media such as rocks and glacial ice, and advection diffusion processes that arise throughout geophysics. Connections to random matrix theory establish unexpected parallels of these geophysical problems with semiconductor physics and Anderson localization phenomena.

Computing the probability of occurrence of rare events is relevant because of their high impact, but also difficult due to the lack of data. Rare-event algorithms are designed for that task but their efficiency relies on a score function that is hard to compute. We compare four methods that compute this function from data and measure their performance to assess which one would be best suited to be applied to a climate model. We find neural networks to be most robust and flexible for this task.

The goal of this paper is to obtain probabilistic predictions of a partially observed dynamical system, without knowing the model equations. It is illustrated using the 3-dimensional Lorenz system where only two components are observed. In the era of deep learning and large datasets, the proposed data-driven procedure is low-cost, easy to implement, using linear and Gaussian assumptions, and requires only a small amount of data.

We have evaluated three metaheuristic algorithms PSO, GWO and vPSOGWO using resistivity data. An enormous solution is assessed and the best-fitted solutions are chosen. The posterior PDF with 68.27 % CI, a mean and posterior solution, and correlation and covariance matrix were calculated for the assessment of the uncertainty, stability and mean model and found that vPSOGWO provides reliable and consistently better results which is correlated well with borehole information.

In this paper, we introduced a sample-based algorithm to obtain the CNOPs. Compared with the classical adjoint-based method, this approach is easier to implement and reduces the required storage for the basic state. When we reduce the number of samples to some extent, it reduces the computation markedly more when using the sample-based method, which can guarantee that the CNOP obtained is nearly consistent with some minor fluctuating errors oscillating in spatial distribution.

We've trained a machine learning system, based on data assimilation and data driven forecasting fundamentals, to predict 'shallow water' fluid dynamics, such as atmosphere, using limited amounts of data from regional measurements within a twin experiment. The model performs well and is robust to noise, leveraging intuition of physical dynamics at play in the shallow water system to help the machine learning system contextualize the data.

Non-equilibrium is dominant in geophysical and climate phenomena. Most of the processes that characterize energy flow occur far from equilibrium. These range from very large systems, such as weather patterns or ocean currents that remain far from equilibrium, owing to an influx of energy, to biological structures. During the last decades progress in non—equilibrium physics came from the study of very rare fluctuations and this paper provides an introduction to these theoretical developments.