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We describe a new approach allowing for systematic causal attribution of weather and climate-related events, in near-real time. The method is purposely designed to facilitate its implementation at meteorological centers by relying on data…
Understanding and monitoring solar active regions is essential for operational space-weather forecasting and improved solar dynamo modeling. This requires comprehensive 360-degree observations of the Sun. While space-weather forecasting has…
Solar oscillations frequencies show a distinct change with solar activity. The changes in frequencies can be used to study the time variation of solar structure. We discuss constraints on the changes in solar structure with time as obtained…
In this review, our present day understanding of the Sun's global photospheric and coronal magnetic fields is discussed from both observational and theoretical viewpoints. Firstly, the large-scale properties of photospheric magnetic fields…
The solar magnetic activity cycle is responsible for periodic episodes of severe space weather, which can perturb satellite orbits, interfere with communications systems, and bring down power grids. Much progress has recently been made in…
The polar magnetic field of the Sun is a manifestation of certain aspects of the dynamo process and is a good precursor for predicting a sunspot cycle before its onset. Although actual synoptic measurements of this field exist only from the…
Many dynamical systems are difficult or impossible to model using high fidelity physics based models. Consequently, researchers are relying more on data driven models to make predictions and forecasts. Based on limited training data,…
Time-distance helioseismology is a set of powerful tools to study features below the Sun's surface. Inverse methods are needed to interpret time-distance measurements, with many examples in the literature. However, techniques that utilize a…
In this work we use Bayesian inference to quantitatively reconstruct the solar properties most relevant to the solar composition problem using as inputs the information provided by helioseismic and solar neutrino data. In particular, we use…
We present a new method for helioseismic diagnostics of the three-dimensional structure of sound speed, magnetic fields and flow velocities in the convection zone by inversion of acoustic travel-time data. The data are measurements of the…
In recent years, the convergence of data-driven machine learning models with Data Assimilation (DA) offers a promising avenue for enhancing weather forecasting. This study delves into this emerging trend, presenting our methodologies and…
A review of solar cycle prediction methods and their performance is given, including forecasts for cycle 24 and focusing on aspects of the solar cycle prediction problem that have a bearing on dynamo theory. The scope of the review is…
Thanks to space-borne experiments such as the AMS-02 and PAMELA missions in low-Earth orbit, along with the Voyager spacecrafts in the interstellar space, a large collection of multi-channel and time-resolved Galactic cosmic ray (GCR) data…
We review solar studies using AIA, HMI, and EVE data from the SDO spacecraft that revolutionized our physical understanding of the Sun. The relevant SDO studies cover the entire 15-year lifetime of SDO, from 2010 May 1 to 2025 May 1. The…
Stellar seismology appears more and more as a powerful tool for a better determination of the fundamental properties of solar-type stars. However the particular case of Sun is still challenging. The helioseismic sound speed determination…
Solar and heliospheric cosmic rays provide a unique perspective in cosmic ray research: we can observe not only the particles, but also the properties of the plasmas in which the they are accelerated and propagate, using in situ and…
Despite the known general properties of the solar cycles, a reliable forecast of the 11-year sunspot number variations is still a problem. The difficulties are caused by the apparent chaotic behavior of the sunspot numbers from cycle to…
The accuracy of simulation-based forecasting in chaotic systems is heavily dependent on high-quality estimates of the system state at the time the forecast is initialized. Data assimilation methods are used to infer these initial conditions…
From the observing campaigns of a number of helioseismic telescope networks such as the Global Oscillation Network Group (GONG) and also from the Solar Heliospheric Observatory satellite (SoHO), helioseismologists now have data on in excess…
The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the…