Related papers: Solar Cycle Prediction Using TCN Deep Learning Mod…
Although systematic measurements of the solar polar magnetic field exist only from mid 1970s, other proxies can be used to infer the polar field at earlier times. The observational data indicate a strong correlation between the polar field…
The analysis of the observations of solar activity indexes SSN (NOAA Sunspot Numbers), the radio flux at a wavelength of 10.7 cm (F10.7) and the solar constant (TSI) during the cycles 22 - 24 is presented. We found a decrease of the…
We present a simple, free, fast and accurate C/C++ and Python routine called SolTrack, which can compute the position of the Sun at any instant and any location on Earth. The code allows tracking of the Sun using a low-specs embedded…
Landfall of a tropical cyclone is the event when it moves over the land after crossing the coast of the ocean. It is important to know the characteristics of the landfall in terms of location and time, well advance in time to take…
Further development of the work of Obridko et al. [1] based on recent data confirms the assumption that the 25th cycle of solar activity is a medium-low cycle. Its height is expected to be $125.2\pm5.6$, and the expected date of the maximum…
The non-stationarity characteristic of the solar power renders traditional point forecasting methods to be less useful due to large prediction errors. This results in increased uncertainties in the grid operation, thereby negatively…
A new prediction technique based on logarithmic values is proposed to predict the maximum amplitude (Rm) of a solar cycle from the preceding minimum aa geomagnetic index (aamin). The correlation between lnRm and lnaamin (r = 0.92) is…
A proper forecast of the menstrual cycle is meaningful for women's health, as it allows individuals to take preventive actions to minimize cycle-associated discomforts. In addition, precise prediction can be useful for planning important…
The subject of this paper is the existence and stability of solar cycles with durations in the range of 20-250 years. Five types of data series are used: 1) The Zurich series (1749-2009 AD), the mean annual International sunspot number Ri,…
The length of the solar activity cycle fluctuates considerably. The temporal evolution of the corresponding cycle phase, that is, the deviation of the epochs of activity minima or maxima from strict periodicity, provides relevant…
Solar energy is one of the most abundant and tapped sources of renewable energies with enormous future potential. Solar panel output can vary widely with factors like intensity, temperature, dirt, debris and so on affecting it. We have…
This paper presents a method for time series forecasting with deep learning and its assessment on two datasets. The method starts with data preparation, followed by model training and evaluation. The final step is a visual inspection.…
The prediction of a stock price has always been a challenging issue, as its volatility can be affected by many factors such as national policies, company financial reports, industry performance, and investor sentiment etc.. In this paper,…
Precipitation from tropical cyclones (TCs) can cause disasters such as flooding, mudslides, and landslides. Predicting such precipitation in advance is crucial, giving people time to prepare and defend against these precipitation-induced…
This article reviews some of the leading results obtained in solar dynamo physics by using temporal oscillator models as a tool to interpret observational data and dynamo model predictions. We discuss how solar observational data such as…
The main purpose of this study is the determination of solar minimum date of the new sunspot cycle No 24. It is provided by using of four types of mean daily data values for the period Jan 01. 2006 - Dec 31. 2009: (1) the solar radioindex…
Sunspots are the most important indicator of the magnetic activity on the solar surface during a cycle. Every sunspot group is formed and shaped by the magnetic field of the Sun. Hence, the magnetic field intensity shows itself as the size…
A new method for estimating tropical cyclone track uncertainty is presented and tested. This method uses a neural network to predict a bivariate normal distribution, which serves as an estimate for track uncertainty. We train the network…
Solar energy is one of the most economical and clean sustainable energy sources on the planet. However, the solar energy throughput is highly unpredictable due to its dependency on a plethora of conditions including weather, seasons, and…
The polar precursor method is widely considered to be the most robust physically motivated method to predict the amplitude of an upcoming solar cycle.It uses indicators of the magnetic field concentrated near the poles around sunspot…