In this work, it is shown that dust storms originating from Sahara and spreading over the Mediterranean Basin are not random events, but rather they are characterized by the presence of time correlations which extend over a timescale range up to about 80 days. The combination of statistical and atmospheric circulation analyses provides a novel and interesting picture of the atmospheric teleconnection between North Africa and the Mediterranean Basin associated with Saharan dust storms.

Extreme weather poses serious risks, making prevention crucial. Using the Lorenz 96 model as a testbed, we propose a bottom-up approach to mitigate extreme events via local interventions guided by multi-scenario ensemble forecasts. Unlike control-theoretic methods, our approach selects the best control scenario from available options. Achieving up to 99.4 % success, it outperforms previous methods while keeping costs reasonable, offering a practical way to reduce disasters with limited control.

Data-driven models (DDMs) learn from large datasets to make predictions, but data limitations affect reliability. Data assimilation (DA) improves accuracy by combining real-world observations with computational models. This research explores how DA enhances DDMs despite limited data. We propose an algorithm using DA to refine DDM training iteratively. This work has broad implications for fields like meteorology, engineering, and environmental science, where accurate prediction is critical.

We propose Ensemble Model Predictive Control (EnMPC), a novel method that improves control of complex systems like weather by integrating control theory with data assimilation. Unlike traditional methods, which are computationally expensive, EnMPC uses ensemble simulations to efficiently handle uncertainties and optimize solutions. This approach reduces computational cost while maintaining accuracy, making it a promising step toward real-world applications in dynamic system control.

Climate prediction is challenging due to systematic errors in traditional climate models. We addressed this by training a machine learning model to correct these errors and then integrating it with the traditional climate model to form an AI-physics hybrid model. Our study demonstrates that the hybrid model outperforms the original climate model on both short-term and long-term predictions of the atmosphere and ocean.

We study time series of solar wind large-scale structures (magnetic clouds, sheaths, slow and fast streams). These have profound importance for causing disturbances in the heliospheric conditions and driving space weather on Earth. The used techniques include methods deriving from information theory to determine entropy and complexity. We find that all of these techniques show stochastic fluctuations, but magnetic clouds stand out due to their coherent magnetic field

The Madden-Julian Oscillation (MJO) is a large-scale tropical wave of enhanced and suppressed rainfalls, slowly moving eastward at the equator, influencing the weather and climate globally. We study the MJO using a simplified model designed to capture its large-scale features. We introduce new, more realistic, inputs into the model, show that this enhanced model successfully replicates key characteristics of the MJO, and identify some of its limitations.