Hawaiian tropic models 19719/11/2023 However, studies of the neural network (NN) approach to seasonal cyclone activity forecasting are limited. Since then, several methodologies have been adopted for the seasonal TC activity forecast in different TC-prone areas of the world, such as Poisson regression models (Elsner and Schmertmann, 1993 Lehmiller et al., 1997 Kim et al., 2010), Bayesian regression models (Elsner and Jagger, 2004, 2006 Chu and Zhao, 2007 Chu et al., 2010 Lu et al., 2010 Werner and Holbrook, 2011), projection pursuit regression (Chan et al., 1998, 2001). The seasonal TC activity forecasts for the Australian and North Atlantic regions were first made by Nicholls (1979) and Gray (1984 a, b), respectively. Tropical cyclone activity depends on both thermodynamical and dynamical factors, which is also reported by several researchers (Palmen, 1948 Gray, 1968 Elsberry and Jeffries, 1996 DeMaria et al. So, disaster managers and planners needed high quality forecasts to save human lives and prevent property losses. It caused 138 366 human causalities (Vos et al., 2009). In 2008, tropical cyclone Nargis was the second deadliest disaster of the decade in Myanmar. Tropical cyclones (TCs) are some of the most frequent and destructive natural hazards in TC-prone areas. Keywords: Tropical cyclone, seasonal prediction, neural network, artificial neural network, multiple linear regression, jackknife, north Indian Ocean.Ī natural hazard affects the environment and leads to huge economic losses and casualties. This tropical cyclone prediction technique may be useful for operational prediction purposes. However, the NN model is found to be superior to the 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. 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. 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. 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. The frequency of TCs and the large scale climate variables derived from the NCEP/NCAR reanalysis dataset of resolution 2.5° x 2.5 o have been analyzed for the period 1971-2013. Sin embargo, los resultados del modelo de redes neuronales fueron superiores a los del modelo linear de regresión múltiple, de modo que esta técnica de predicción de ciclones tropicales puede ser muy útil para propósitos operativos de predicción.Ī 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). Los resultados indican que el número de ciclones tropicales calculado por medio de ambos modelos es muy similar al número real de ciclones ocurridos en cada año. Con base en algunos parámetros estadísticos de desempeño, se evalúa la eficacia del modelo de redes neuronales y los resultados se comparan con el modelo lineal de regresión múltiple. Se eligieron cinco variables climáticas de gran escala (altura geopotencial a 500 hPa, humedad relativa a 500 hPa, presión superficial en el mar, y viento zonal a 700 y 200 hPa para el mes previo ) como predictores para aplicar un análisis de correlación. Se utilizaron datos del periodo 1971-2002 para desarrollar el modelo, y éste se probó con datos de muestreo independientes del periodo 2003-2013. Se analizan la frecuencia de los CT y las variables climáticas de gran escala derivadas de la base de datos de reanálisis del NCEP/NCAR con resolución de 2.5 x 2.5° para el periodo 1971-2013. Se desarrolla un modelo de red neuronal para predecir el número estacional de ciclones tropicales (CT) que se desarrollan en el Océano Índico septentrional después de la estación del monzón (octubre a diciembre). India Meteorological Department, Mausam Bhavan, Lodi Road, New Delhi 110003, India Department of Mathematics, Jadavpur University, Kolkata 700032, India India Meteorological Department, Mausam Bhavan, Lodi Road, New Delhi 110003, IndiaĬorresponding author: S. Seasonal prediction of tropical cyclone activity over the north Indian Ocean using the neural network model
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