Context. Forecasting the solar cycle amplitude is important for a better understanding of the solar dynamo as well as for many space weather applications. Different empirical relations of solar cycle parameters with the peak amplitude of the upcoming solar cycle have been established and used for solar cycle forecasts, as e.g. the Waldmeier rule relating the cycle rise time with its amplitude, the polar fields at previous minimum, etc. Recently, a separate consideration of the evolution of the two hemispheres revealed even tighter relations. Aims. We introduce the maximal growth rate of sunspot activity in the ascending phase of a cycle as a new and reliable precursor of a subsequent solar cycle amplitude. We investigate whether the suggested precursor provides benefits for the prediction of the solar cycle amplitude when using the sunspot indices (sunspot numbers, sunspot areas) derived separately for the two hemispheres compared to the total sunspot indices describing the entire solar disk. Methods. We investigate the relationship between the maximal growth rate of sunspot activity in the ascending phase of a cycle and the subsequent cycle amplitude on the basis of four data sets of solar activity indices: total sunspot numbers, hemispheric sunspot numbers from the new catalogue from 1874 onwards (Veronig et al. 2021), total and hemispheric sunspot areas.Results. For all the data sets, a linear regression based on the maximal growth rate precursor shows a significant correlation. Validation of predictions for cycles 1-24 shows high correlations between the true and predicted cycle amplitudes reaching r = 0.93 for the total sunspot numbers. The lead time of the predictions varies from 2 to 49 months, with a mean value of 21 months. Furthermore, we demonstrated that the sum of maximal growth rate indicators determined separately for the North and the South hemispheric sunspot numbers provides more accurate predictions than that using total sunspot numbers. The advantages reach 27% and 11% on average in terms of rms and correlation coefficient, respectively. The superior performance is also confirmed with hemispheric sunspot areas with respect to total sunspot areas. Conclusions. The maximal growth rate of sunspot activity in the ascending phase of a solar cycle serves as a reliable precursor of the subsequent cycle amplitude. Furthermore our finding provide a strong foundation for supporting regular monitoring, recording, and predictions of solar activity with hemispheric sunspot data, which capture the asymmetric behaviour of the solar activity and solar magnetic field and enhance solar cycle prediction methods.
This paper focuses on the role of information and communication technologies (ICT) in the sustainable independent tourism and hospitality. Moreover, it attempts to identify emerging trends in tourism of the 21st century. Nowadays, tourism has become more independent on large travel agencies and package tours and this transition has been caused by the development of Internet and information technologies. Most recently, the rise of the sharing economy had an array of important implications for the tourism sector with such digital platforms as Uber, Airbnb, Gett, Lyft, TripAdvisor, Expedia or Booking.com replacing the traditional ways to travel. Surely, independent tourism is not for everyone and might be restricted to small groups of people. However, it is crucial for sustainable development in tourism and hospitality sector due to the fact that it can replace massive tourism and limit the extent of overtourism in many popular destinations.
The electrical submersible pump (ESP), an efficient artificial lift method, was developed to increase production rates from wellbores (Bates et al. 2004). As the number of ESP installations increases annually, there is a greater awareness of their environmental impact and a growing responsibility to reduce the associated carbon footprint because frequent workovers to replace failed ESPs are primary sources of carbon dioxide emissions from the oil and gas industry. Because of this, companies are beginning to pursue cost reductions and look for methods to mitigate the consequences of production. Systems for monitoring ESP performance in real time are currently being developed on the basis of data analysis to detect potential problems in advance. This paper presents a digital solution for tracking ESP performance that includes an automated data processing pipeline and the use of statistical metrics to analyze the dynamics of failure and run life at the system node level. This comprehensive analysis helps diagnose problematic equipment nodes using developed web applications. The recommendation system determines the most-reliable ESP configurations under the necessary operating conditions.
For many countries like Russia, Canada, or the USA, a robust and detailed tree species inventory is essential to manage their forests sustainably. Since one can not apply unmanned aerial vehicle (UAV) imagery-based approaches to large scale forest inventory applications, the utilization of machine learning algorithms on satellite imagery is a rising topic of research. Although satellite imagery quality is relatively low, additional spectral channels provide a sufficient amount of information for tree crown classification tasks. Assuming that tree crowns are detected already, we use embeddings of tree crowns generated by Autoencoders as a data set to train classical Machine Learning algorithms. We compare our Autoencoder (AE) based approach to traditional convolutional neural networks (CNN) end-to-end classifiers.
<p>The sun&#8217;s magnetic field drives the 11-year solar cycle, and predicting its strength has practical importance for many space weather applications. Previous studies have shown that analysing the solar activity of the two hemispheres separately instead of the full sun can provide more detailed information on the activity evolution. However, the existing Hemispheric Sunspot Number data series (1945 onwards) was too short for meaningful solar cycle predictions. Based on a newly created hemispheric sunspot number catalogue for the time range 1874-2020 (Veronig et al. 2021, http://cdsarc.u-strasbg.fr/viz-bin/cat/J/A+A/652/A56) that is compatible with the International Sunspot Number from World Data Centre SILSO, we investigate the evolution of the solar cycle for the two hemispheres and develop a novel method for predicting the solar cycle amplitude. We demonstrate a steady relationship between the maximal growth rate of activity in the ascending phase of a cycle and its subsequent amplitude and form a 3rd order regression for the predictions. Testing this method for cycles 12-24, we show that the forecast made by the sum of the maximal growth rate from the North and South Hemispheric Sunspot number is more accurate than the same forecast from the Total Sunspot Number: The rms error of predictions is smaller by 27%, the correlation coefficient r is higher by 11% on average reaching values in the range r = 0.8-0.9 depending of the smoothing window of the monthly mean data. These findings demonstrate that empirical solar cycle prediction methods can be enhanced by investigating the solar cycle dynamics in terms of the hemispheric sunspot numbers, which is a strong argument supporting regular monitoring, recording, and analysing solar activity separately for the two hemispheres.</p>
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