A high forest fire season of two to three years is regularly observed each decade in Mexican forests. This seems to be related to the presence of the El Niñ o phenomenon and to the amount of total solar irradiance. In this study, the results of a multi-cross wavelet analysis are reported based on the occurrence of Mexican forest fires, El Niñ o and the total solar irradiance for the period 1970-2014. The analysis shows that Mexican forest fires and the strongest El Niñ o phenomena occur mostly around the minima of the solar cycle. This suggests that the total solar irradiance minima provide the appropriate climatological conditions for the occurrence of these forest fires. The next high season for Mexican forest fires could start in the next solar minimum, which will take place between the years 2017 and 2019. A complementary space analysis based on MODIS active fire data for Mexican forest fires from 2005 to 2014 shows that most of these fires occur in cedar and pine forests, on savannas and pasturelands, and in the central jungles of the Atlantic and Pacific coasts.
The development of high frequencies theoiy and practice of radiometric sensing of environment has shown an opportunity to receive the information on temperature and salinity of superficial water layer, underwater wind speed, on which depends geometry of a surface and share of its covering foamed formntion. The further development of satellite microwave measurement needs to find a solution to the problem of great multiparametrical return tasks In this paper we consider regression and electrodynamic model of the sea and ocean and their limiting errors of electrophysical parameters measurements of sea surface in order to find the solution to the tasks. The measurements are investigated at passive remote sensing. Temperature and concentration dependence of complex dielectric penneability of water and water mono and polyeleclrolyte systems (analogues of the widely widespread liquid natural objects) are also investigated. The measurements are executed with the help of the developed experimental stand of a reflective type on frequency 35.5 GHz at the interval of temperatures 271-353 K.Radiobrilliance temperature for regression and electrodynamic models of the sea and ocean, are designed with the help ofreceived date on ' and ".
Every year, tropical hurricanes affect North and Central American wildlife and people. The ability to forecast hurricanes is essential in order to minimize the risks and vulnerabilities in North and Central America. Machine learning is a newly tool that has been applied to make predictions about different phenomena. We present an original framework utilizing Machine Learning with the purpose of developing models that give insights into the complex relationship between the land–atmosphere–ocean system and tropical hurricanes. We study the activity variations in each Atlantic hurricane category as tabulated and classified by NOAA from 1950 to 2021. By applying wavelet analysis, we find that category 2–4 hurricanes formed during the positive phase of the quasi-quinquennial oscillation. In addition, our wavelet analyses show that super Atlantic hurricanes of category 5 strength were formed only during the positive phase of the decadal oscillation. The patterns obtained for each Atlantic hurricane category, clustered historical hurricane records in high and null tropical hurricane activity seasons. Using the observational patterns obtained by wavelet analysis, we created a long-term probabilistic Bayesian Machine Learning forecast for each of the Atlantic hurricane categories. Our results imply that if all such natural activity patterns and the tendencies for Atlantic hurricanes continue and persist, the next groups of hurricanes over the Atlantic basin will begin between 2023 ± 1 and 2025 ± 1, 2023 ± 1 and 2025 ± 1, 2025 ± 1 and 2028 ± 1, 2026 ± 2 and 2031 ± 3, for hurricane strength categories 2 to 5, respectively. Our results further point out that in the case of the super hurricanes of the Atlantic of category 5, they develop in five geographic areas with hot deep waters that are rather very well defined: (I) the east coast of the United States, (II) the Northeast of Mexico, (III) the Caribbean Sea, (IV) the Central American coast, and (V) the north of the Greater Antilles.
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