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856.4.0.u: https://www.revistascca.unam.mx/atm/index.php/atm/article/view/ATM.2015.28.04.06/46521

100.1.#.a: Nath, Sankar

524.#.#.a: Nath, Sankar (2015). Seasonal prediction of tropical cyclone activity over the North Indian Ocean using the neural network model. Atmósfera; Vol. 28 No. 4, 2015; 271-281. Recuperado de https://repositorio.unam.mx/contenidos/11143

245.1.0.a: Seasonal prediction of tropical cyclone activity over the North Indian Ocean using the neural network model

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias de la Atmósfera y Cambio Climático, UNAM

264.#.0.c: 2015

264.#.1.c: 2015-10-06

653.#.#.a: Tropical cyclone; seasonal prediction; neural network; artificial neural network; multiple linear regression; jackknife; north Indian Ocean

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC 4.0 Internacional, https://creativecommons.org/licenses/by-nc/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico editora@atmosfera.unam.mx

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041.#.7.h: eng

520.3.#.a: A neural network (NN) model is developed to predict the seasonal number of tropical cyclones (TCs) formed over the north Indian Ocean during the post-monsoon season (October, November, December). The frequency of TCs and the large scale climate variables derived from the NCEP/NCAR reanalysis dataset of resolution 2.5º • 2.5o have been analyzed for the period 1971-2013. Data for the years 1971-2002 have been used for the development of the model, which is tested with independent sample data for the years 2003-2013. Applying correlation analysis, five large-scale climate variables, namely geopotential height at 500 hPa, relative humidity at 500 hPa, sea level pressure, and zonal wind at 700 hPa and 200 hPa for the antecedent month September are selected as predictors. Based on some performance parameter statistics, the performance of the NN model is evaluated and the results are compared with the multiple linear regression (MLR) model. From the results it is inferred that the predicted tropical cyclone count by both models is very close to the actual counts for both periods. However, the NN model is found to be superior to the MLR model. This tropical cyclone prediction technique may be useful for operational prediction purposes.

773.1.#.t: Atmósfera; Vol. 28 No. 4 (2015); 271-281

773.1.#.o: https://www.revistascca.unam.mx/atm/index.php/atm/index

046.#.#.j: 2021-10-20 00:00:00.000000

022.#.#.a: ISSN electrónico: 2395-8812; ISSN impreso: 0187-6236

310.#.#.a: Trimestral

300.#.#.a: Páginas: 271-281

264.#.1.b: Instituto de Ciencias de la Atmósfera y Cambio Climático, UNAM

doi: https://doi.org/10.20937/ATM.2015.28.04.06

handle: 12d39f739cdeaac6

harvesting_date: 2023-06-20 16:00:00.0

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last_modified: 2023-06-20 16:00:00

license_url: https://creativecommons.org/licenses/by-nc/4.0/legalcode.es

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No entro en nada

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Artículo

Seasonal prediction of tropical cyclone activity over the North Indian Ocean using the neural network model

Nath, Sankar

Instituto de Ciencias de la Atmósfera y Cambio Climático, UNAM, publicado en Atmósfera, y cosechado de Revistas UNAM

Licencia de uso

Procedencia del contenido

Entidad o dependencia
Instituto de Ciencias de la Atmósfera y Cambio Climático, UNAM
Revista
Atmósfera
Repositorio
Revistas UNAM
Contacto
Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

Cita

Nath, Sankar (2015). Seasonal prediction of tropical cyclone activity over the North Indian Ocean using the neural network model. Atmósfera; Vol. 28 No. 4, 2015; 271-281. Recuperado de https://repositorio.unam.mx/contenidos/11143

Descripción del recurso

Autor(es)
Nath, Sankar
Tipo
Artículo de Investigación
Área del conocimiento
Físico Matemáticas y Ciencias de la Tierra
Título
Seasonal prediction of tropical cyclone activity over the North Indian Ocean using the neural network model
Fecha
2015-10-06
Resumen
A neural network (NN) model is developed to predict the seasonal number of tropical cyclones (TCs) formed over the north Indian Ocean during the post-monsoon season (October, November, December). The frequency of TCs and the large scale climate variables derived from the NCEP/NCAR reanalysis dataset of resolution 2.5º • 2.5o have been analyzed for the period 1971-2013. Data for the years 1971-2002 have been used for the development of the model, which is tested with independent sample data for the years 2003-2013. Applying correlation analysis, five large-scale climate variables, namely geopotential height at 500 hPa, relative humidity at 500 hPa, sea level pressure, and zonal wind at 700 hPa and 200 hPa for the antecedent month September are selected as predictors. Based on some performance parameter statistics, the performance of the NN model is evaluated and the results are compared with the multiple linear regression (MLR) model. From the results it is inferred that the predicted tropical cyclone count by both models is very close to the actual counts for both periods. However, the NN model is found to be superior to the MLR model. This tropical cyclone prediction technique may be useful for operational prediction purposes.
Tema
Tropical cyclone; seasonal prediction; neural network; artificial neural network; multiple linear regression; jackknife; north Indian Ocean
Idioma
eng
ISSN
ISSN electrónico: 2395-8812; ISSN impreso: 0187-6236

Enlaces