Using the Dipolar and Quadrupolar Moments to Improve Solar-Cycle Predictions Based on the Polar Magnetic Fields

The solar cycle and its associated magnetic activity are the main drivers behind changes in the interplanetary environment and Earth's upper atmosphere (commonly referred to as space weather and climate). In recent years there has been an effort to develop accurate solar cycle predictions, leading to nearly a hundred widely spread predictions for the amplitude of solar cycle 24. Here we show that cycle predictions can be made more accurate if performed separately for each hemisphere, taking advantage of information about both the dipolar and quadrupolar moments of the solar magnetic field during minimum

Two-component model for the chemical evolution of the Galactic disk

In the present paper, we introduce a two-component model of the Galactic disk to investigate its chemical evolution. The formation of the thick and thin disks occur in two main accretion episodes with both infall rates to be Gaussian. Both the pre-thin and post-thin scenarios for the formation of the Galactic disk are considered. The best-fitting is obtained through $\chi^2$-test between the models and the new observed metallicity distribution function of G dwarfs in the solar neighbourhood (Hou et al 1998). Our results show that post-thin disk scenario for the formation of the Galactic disk should be preferred. Still, other comparison between model predictions and observations are given.Comment: 23 pages, 7 figure

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie

Short-term prediction of photovoltaic power generation using Gaussian process regression

Photovoltaic (PV) power is affected by weather conditions, making the power generated from the PV systems uncertain. Solving this problem would help improve the reliability and cost effectiveness of the grid, and could help reduce reliance on fossil fuel plants. The present paper focuses on evaluating predictions of the energy generated by PV systems in the United Kingdom Gaussian process regression (GPR). Gaussian process regression is a Bayesian non-parametric model that can provide predictions along with the uncertainty in the predicted value, which can be very useful in applications with a high degree of uncertainty. The model is evaluated for short-term forecasts of 48 hours against three main factors -- training period, sky area coverage and kernel model selection -- and for very short-term forecasts of four hours against sky area. We also compare very short-term forecasts in terms of cloud coverage within the prediction period and only initial cloud coverage as a predictor

An upper limit on nickel overabundance in the supercritical accretion disk wind of SS 433 from X-ray spectroscopy

We take advantage of a long (with a total exposure time of 120 ks) X-ray observation of the unique Galactic microquasar SS 433, carried out with the XMM-Newton space observatory, to search for a fluorescent line of neutral (or weakly ionized) nickel at the energy 7.5 keV. We consider two models of the formation of fluorescent lines in the spectrum of SS 433: 1) due to reflection of hard X-ray radiation from a putative central source on the optically thick walls of the accretion disk "funnel"; and 2) due to scattering of the radiation coming from the hottest parts of the jets in the optically thin wind of the system. It is shown, that for these cases, the photon flux of Ni I K$_{\alpha}$ fluorescent line is expected to be 0.45 of the flux of Fe I K$_{\alpha}$ fluorescent line at 6.4 keV, for the relative nickel overabundance $Z_{Ni}/Z = 10$, as observed in the jets of SS 433. For the continuum model without the absorption edge of neutral iron, we set a 90 per cent upper limit on the flux of the narrow Ni I K$_{\alpha}$ line at the level of $0.9 \times 10^{-5}$ ph s$^{-1}$ cm$^{-2}$. For the continuum model with the absorption edge, the corresponding upper limit is $2.5 \times 10^{-5}$ ph s$^{-1}$ cm$^{-2}$. At the same time, for the Fe I K$_{\alpha}$ line, we measure the flux of $9.9_{8.4}^{11.2} \times 10^{-5}$ ph s$^{-1}$ cm$^{-2}$. Taken at the face value, the results imply that the relative overabundance of nickel in the wind of the accretion disc should be at least 1.5 times less than the corresponding excess of nickel observed in the jets of SS 433.Comment: 17 pages, 12 figures, 4 tables, Astronomy Letters, in press, 2018, Volume 44, Issue

Comparison of Solar Radiation Intensity Forecasting Using ANFIS and Multiple Linear Regression Methods

Solar radiation forecasting is important in solar energy power plants (SEPPs) development. The electrical energy generated from the sunlight depends on the weather and climate conditions in the area where the SEPPs are installed. The condition of solar irradiation will indirectly affect the electrical grid system into which the SEPPs are injected, i.e. the amount and direction of the power flow, voltage, frequency, and also the dynamic state of the system. Therefore, the prediction of solar radiation condition is very crucial to identify its impact into the system. There are many methods in determining the prediction of solar radiation, either by mathematical approach or by heuristic approach such as artificial intelligent method. This paper analyzes the comparison of two methods, Adaptive Neuro Fuzzy Inference (ANFIS) method, which belongs into the heuristic methods, and Multiple Linear Regression (MLP) method, which uses a mathematical approach. The performance of both methods is measured using the root mean square error (RMSE) and the mean absolute error (MAE) values. The data of the Swiss Basel city from Meteoblue are used to test the performance of the two methods being compared. The data are divided into four cases, being classified as the training data and the data used as predictions. The solar radiation prediction using the ANFIS method indicates the results which are closer to the real measurement results, being compared to the the use MLP method. The average values of RMSE and MAE achieved are 123.27 W/m2 and 90.91 W/m2 using the ANFIS method, being compared to 138.70 W/m2 and 101.56 W/m2 respectively using the MLP method. The ANFIS method gives better prediction performance of 12.51% for RMSE and 11.71% for MAE with respect to the use of the MLP method