Space weather and hurricanes Irma, Jose and Katia

Vyklyuk, Yaroslav; Radovanović, Milan; Milovanović, Boško; Milenković, Milan; Дољак, Дејан; Milićević, Slavica Malinović; Vuković, Natalia; Vujko, Aleksandra; Matsiuk, Nataliia; Mukherjee, Saumitra

doi:10.1007/s10509-019-3646-5

Cited by 4 publications

(5 citation statements)

References 34 publications

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“…Vyklyuk et al [20] underlined the fact that the use of LSTM neural networks allows us to obtain good consideration of the nonlinearity and time delay between input and output factors. Combining these models in the form of ensembles made it possible to significantly increase the accuracy and adequacy of the models in comparison with the approaches described in the research of Hyndman [27] and Radovanović et al [28].…”

Section: Resultsmentioning

confidence: 99%

“…The data included proton density (p/cc), bulk speed (km/s), and ion temperature (K). The source of data on solar flux F10.7 cm (2800 MHz) was Space Weather Canada [20]. The F10.7 cm solar radio flux (or just F10.7) is, along with the sunspot number, one of the most widely used indices of solar activity [21].…”

Section: Methodsmentioning

confidence: 99%

“…The assumption about the time delay between the solar activity and atmospheric processes suggests that recurrent neural networks should be used in the calculations. The best results for such data are obtained through LSTM ensembles [20]. The analysis was conducted through the following steps:…”

Section: Methodsmentioning

confidence: 99%

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Connection of Solar Activities and Forest Fires in 2018: Events in the USA (California), Portugal and Greece

et al. 2020

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The impact of solar activity on environmental processes is difficult to understand and complex for empirical modeling. This study aimed to establish forecast models of the meteorological conditions in the forest fire areas based on the solar activity parameters applying the neural networks approach. During July and August 2018, severe forest fires simultaneously occurred in the State of California (USA), Portugal, and Greece. Air temperature and humidity data together with solar parameters (integral flux of solar protons, differential electron flux and proton flux, solar wind plasma parameters, and solar radio flux at 10.7 cm data) were used in long short-term memory (LSTM) recurrent neural network ensembles. It is found that solar activity mostly affects the humidity for two stations in California and Portugal (an increase in the integral flux of solar protons of > 30 MeV by 10% increases the humidity by 3.25%, 1.65%, and 1.57%, respectively). Furthermore, an increase in air temperature of 10% increases the humidity by 2.55%, 2.01%, and 0.26%, respectively. It is shown that temperature is less sensitive to changes in solar parameters but depends on previous conditions (previous increase of 10% increases the current temperature by 0.75%, 0.34%, and 0.33%, respectively). Humidity in Greece is mostly impacted by solar flux F10.7 cm and previous values of humidity. An increase in these factors by 10% will lead to a decrease in the humidity of 3.89% or an increase of 1.31%, while air temperature mostly depends on ion temperature. If this factor increases by 10%, it will lead to air temperature rising by 0.42%.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Connection of Solar Activities and Forest Fires in 2018: Events in the USA (California), Portugal and Greece

et al. 2020

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“…These parameters represent solar wind and activity variability and are used in previous research. All the parameters used in this study are used in the investigation of dependencies between forest fires and solar activity [59,60], while some of them (IPF > 10 MeV, solar wind, and 10.7 cm radio flux) were used in the research of the relationship between solar activity and hurricanes [10,61]. Since previous research showed that a causal link exists, in this research we attempted to examine if there is a connection between these parameters of solar activity and precipitation-induced floods.…”

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confidence: 99%

Application of Solar Activity Time Series in Machine Learning Predictive Modeling of Precipitation-Induced Floods

et al. 2023

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This research is devoted to the determination of hidden dependencies between the flow of particles that come from the Sun and precipitation-induced floods in the United Kingdom (UK). The analysis covers 20 flood events during the period from October 2001 to December 2019. The parameters of solar activity were used as model input data, while precipitations data in the period 10 days before and during each flood event were used as model output. The time lag of 0–9 days was taken into account in the research. Correlation analysis was conducted to determine the degree of randomness for the time series of input and output parameters. For establishing a potential causative link, machine learning classification predictive modeling was applied. Two approaches, the decision tree, and the random forest were used. We analyzed the accuracy of classification models forecast from 0 to 9 days in advance. It was found that the most important factors for flood forecasting are proton density with a time lag of 9, differential proton flux in the range of 310–580 keV, and ion temperature. Research in this paper has shown that the decision tree model is more accurate and adequate in predicting the appearance of precipitation-induced floods up to 9 days ahead with an accuracy of 91%. The results of this study confirmed that by increasing technical capabilities, using improved machine learning techniques and large data sets, it is possible to improve the understanding of the physical link between the solar wind and tropospheric weather and help improve severe weather forecasting.

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“…Therefore, the monitoring and understanding of spatio-temporal atmospheric changes are important for research in a number of scientific disciplines as well as for geoinformation technologies. Here, first of all, the importance of research into atmospheric changes related to natural disasters should be emphasized (Molchanov et al, 2004;Price et al, 2007;Maurya et al, 2016;Kumar et al, 2017;Vyklyuk et al, 2017Vyklyuk et al, , 2019Manta et al, 2020;Malinović-Milićević et al, 2023). For example, the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) model based on the effects of ionisation provided by radon released from active tectonic faults before earthquakes is created in Pulinets et al (2022).…”

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confidence: 99%

Editorial: Atmospheric disturbances: responses to phenomena from lithosphere to outer space

Nina

Milovanović

Malinović-Milićević

et al. 2023

Front. Environ. Sci.

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Space weather and hurricanes Irma, Jose and Katia

Cited by 4 publications

References 34 publications

Connection of Solar Activities and Forest Fires in 2018: Events in the USA (California), Portugal and Greece

Connection of Solar Activities and Forest Fires in 2018: Events in the USA (California), Portugal and Greece

Application of Solar Activity Time Series in Machine Learning Predictive Modeling of Precipitation-Induced Floods

Editorial: Atmospheric disturbances: responses to phenomena from lithosphere to outer space

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