Zobrazeno 1 - 10
of 653
pro vyhledávání: '"cyclone intensity"'
Autor:
Priyanka Nandal, Prerna Mann, Navdeep Bohra, Ghadah Aldehim, Asma Abbas Hassan Elnour, Randa Allafi
Publikováno v:
Alexandria Engineering Journal, Vol 113, Iss , Pp 227-241 (2025)
Satellite images serve a crucial role in weather prediction, particularly in assessing the strength of tropical storms such as cyclones. Tropical cyclones are commonly observed in regions of open oceans where conventional meteorological stations are
Externí odkaz:
https://doaj.org/article/db41a2910d064993ba647a33b9f7b05f
Autor:
K. Rajesh, Logesh Ravi, Nalluri Madhusudana Rao, V. Ramaswamy, J. SenthilKumar, K. Kannan, Mohammad Shorfuzzaman, Amr Yousef, Mohamed Elsaid Ragab Elkholy, A. Sasikumar
Publikováno v:
IEEE Access, Vol 12, Pp 102552-102565 (2024)
Early prediction of cyclones helps reduce deaths and damage to properties worldwide. With the advancement in satellite imaging technology, obtaining atmospheric images and remotely sensed objects such as cyclones is possible using different modalitie
Externí odkaz:
https://doaj.org/article/dadbb661504d4d91acf6ccff3a8cdc30
Autor:
S. Yu. Glebova
Publikováno v:
Известия ТИНРО, Vol 204, Iss 1, Pp 183-205 (2024)
Mean patterns of atmospheric circulation over the Bering Sea in 4 seasons: winter (January-March), spring (April-June), summer (July-September) and autumn (OctoberDecember) are described using the author’s typification of synoptic situations. Frequ
Externí odkaz:
https://doaj.org/article/808b068c1f4d421d8bccd5e949faf765
Autor:
Franciskus Antonius Alijoyo, Taviti Naidu Gongada, Chamandeep Kaur, N. Mageswari, J.C. Sekhar, Janjhyam Venkata Naga Ramesh, Yousef A.Baker El-Ebiary, Zoirov Ulmas
Publikováno v:
Alexandria Engineering Journal, Vol 92, Iss , Pp 346-357 (2024)
Predicting cyclone intensity is an important aspect of weather forecasting since it influences disaster preparation and response. This framework addresses the pressing need for precise cyclone intensity prediction by presenting a unique predictive mo
Externí odkaz:
https://doaj.org/article/ae0996fc2d4b4a83b4a260793792e465
Publikováno v:
Frontiers in Earth Science, Vol 12 (2024)
Coupling a three-dimensional ocean circulation model to an atmospheric model can significantly improve forecasting of tropical cyclones (TCs). This is particularly true of forecasts for TC intensity (maximum sustained surface wind and minimum central
Externí odkaz:
https://doaj.org/article/0282c33f01c847ee813bfcca1de718ce
Publikováno v:
Machine Learning with Applications, Vol 17, Iss , Pp 100569- (2024)
Intense tropical cyclones (TCs) cause significant damage to human societies. Forecasting the multiple stages of TC intensity changes is considerably crucial yet challenging. This difficulty arises due to imbalanced data distribution and the need for
Externí odkaz:
https://doaj.org/article/96ef02108c6e4b88a5aa66405eadb287
Publikováno v:
Frontiers in Earth Science, Vol 11 (2024)
Accurate prediction and monitoring of tropical cyclone (TC) intensity are crucial for saving lives, mitigating damages, and improving disaster response measures. In this study, we used a convolutional neural network (CNN) model to estimate TC intensi
Externí odkaz:
https://doaj.org/article/86de0f4d0a6f4bdc92dab7519ebdfce9
Publikováno v:
Environmental Research Letters, Vol 19, Iss 9, p 094002 (2024)
Storm surges caused by tropical cyclones (TCs) have long ranked first among all types of marine disasters in casualties and economic losses, and can lead to further regional exacerbation of consequences stemming from these losses along different coas
Externí odkaz:
https://doaj.org/article/8dc73dd694134fc5b930f1aa9112ef04
Publikováno v:
Environmental Research Letters, Vol 19, Iss 4, p 044067 (2024)
It is well known that tropical cyclones (TCs) making landfall in Southern China (SC) account for more than half of all TCs making landfall in China. Therefore, it is important to have an in-depth understanding of the activities of TCs in SC under cli
Externí odkaz:
https://doaj.org/article/594e3ed913124884a72d2e3e67f5ee99
Publikováno v:
Environmental Research Letters, Vol 19, Iss 2, p 024006 (2024)
This paper developed a deep learning (DL) model for forecasting tropical cyclone (TC) intensity in the Northwest Pacific. A dataset containing 20 533 synchronized and collocated samples was assembled, which included ERA5 reanalysis data as well as sa
Externí odkaz:
https://doaj.org/article/5eac6deda38f4cd1933c7f449cb3bf88