Related papers: Forecasting the solar cycle using variational data…
This paper addresses the impact of assimilating data from the Earth Networks Total Lightning Network (ENTLN) during two cases of severe weather. Data from the ENTLN serve as a substitute for those from the upcoming launch of the GOES…
Data assimilation, consisting in the combination of a dynamical model with a set of noisy and incomplete observations in order to infer the state of a system over time, involves uncertainty in most settings. Building upon an existing…
The integration of observational data into numerical models, known as data assimilation (DA), is fundamental for making Numerical Weather Prediction (NWP) possible, with breathtaking success over the past 60 years (Bauer et al. 2015).…
The flare activity and the ultraviolet emission of the sun during its 24-th cycle are analysed. As compared to cycles 21-23, where the most powerful flares were observed during the decay phase, in cycle 24 the greatest number of powerful…
Prediction of the Sun's magnetic activity is important because of its effect on space environment and climate. However, recent efforts to predict the amplitude of the solar cycle have resulted in diverging forecasts with no consensus.…
The precise shape of the Sun is sensitive to the influence of gravity, differential rotation, local turbulence and magnetic fields. It has been previously shown that the solar shape exhibits asphericity that evolves with the 11-year cycle.…
The results of determinations of the azimuthal and meridional velocities by time-distance helioseismology from Helioseismic and Magnetic Imager (HMI) onboard Solar Dynamics Observatory (SDO) from May 2010 to September 2020 at latitudes from…
The inherent stochastic and nonlinear nature of the solar dynamo makes the strength of the solar cycles vary in a wide range, making it difficult to predict the strength of an upcoming solar cycle. Recently, our work has shown that by using…
We have used solar oscillation frequencies and frequency splittings obtained over solar cycles 23, 24 and the rising phase of solar cycle 25 to investigate whether the tachocline properties (jump i.e., the change in the rotation rate across…
Data assimilation is an iterative approach to the problem of estimating the state of a dynamical system using both current and past observations of the system together with a model for the system's time evolution. Rather than solving the…
The weather and climate domains are undergoing a significant transformation thanks to advances in AI-based foundation models such as FourCastNet, GraphCast, ClimaX and Pangu-Weather. While these models show considerable potential, they are…
We study the origin of the predictive skill of some methods to forecast the strength of solar activity cycles. A simple flux transport model for the azimuthally averaged radial magnetic field at the solar surface is used, which contains a…
Here we analyze solar activity by focusing on time variations of the number of sunspot groups (SGs) as a function of their modified Zurich class. We analyzed data for solar cycles 2023 by using Rome (cycles 2021) and Learmonth Solar…
The apparently unusual behaviour of the TSI during the most recent minimum of solar activity has been interpreted as evidence against solar surface magnetism as the main driver of the secular change in the TSI. We test claims that the…
Four-dimensional variational data assimilation (4DVar) has become an increasingly important tool in data science with wide applications in many engineering and scientific fields such as geoscience1-12, biology13 and the financial…
Aims: To investigate solar polar fields during cycle 24, using measurements of solar magnetic fields in the latitude range 55 - 90 degree and 78 - 90 degree, to report a prolonged and unusual hemispheric asymmetry in the polar field…
The prediction of solar flares, eruptions, and high energy particle storms is of great societal importance. The data mining approach to forecasting has been shown to be very promising. Benchmark datasets are a key element in the further…
Weather forecasting is a crucial yet highly challenging task. With the maturity of Artificial Intelligence (AI), the emergence of data-driven weather forecasting models has opened up a new paradigm for the development of weather forecasting…
The need for reliable predictions of the solar activity cycle motivates the development of dynamo models incorporating a representation of surface processes sufficiently detailed to allow assimilation of magnetographic data. In this series…
We have used available intermediate degree p-mode frequencies for the solar cycle 23 to check the validity of previously derived empirical relations for frequency shifts (Jain et al.: 2000, Solar Phys., 192, 487). We find that the…