Examining El Niño–Southern Oscillation Effects in the Subtropical Zone to Forecast Long-Distance Total Rainfall from Typhoons: A Case Study in Taiwan

Examining El Niño–Southern Oscillation Effects in the Subtropical Zone to Forecast... AbstractTyphoon rainfall predictions provide critical information that can be used for flood control and advanced disaster prevention preparations. However, total rainfall “nowcasts” (i.e., several days ahead) are not available in Taiwan when typhoons are distant. In this paper, we propose a long-distance total rainfall forecast (LTRF) model and present a real-time forecasting process that can use the LTRF model to determine the formation and possible approach of typhoons in the future. The LTRF model was formulated using two designed climate scenarios. Scenario 1 considered El Niño–Southern Oscillation (ENSO) effects, whereas Scenario 2 did not. Various raw sensor data, comprising climatological characteristics, sea surface temperature, satellite brightness temperatures, and total rainfall, were collected; moreover, attributes of the ENSO indices, including the Southern Oscillation Index and Niño 3.4 sea surface temperature anomaly, were reviewed. The scenario models were constructed using the C4.5 and Random Forests tree-based algorithms. Typhoon events occurring during 2001–2013 and 2014–2015 (specifically, Typhoons Matmo and Fung-Wong in 2014 and Soudelor and Dujuan in 2015) were examined for training and testing purposes, respectively. The Hualien Weather Station in Taiwan was selected as a study site, and the forecasting horizon was set at 6 hours. Finally, the model simulations, observations, and Central Weather Bureau (Taiwan) nowcasts were compared. The simulation results showed that the proposed LTRF model, when ENSO effects were accounted for, can efficiently forecast total typhoon rainfall when typhoons are distant from Taiwan. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Atmospheric and Oceanic Technology American Meteorological Society

Examining El Niño–Southern Oscillation Effects in the Subtropical Zone to Forecast Long-Distance Total Rainfall from Typhoons: A Case Study in Taiwan

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0426
D.O.I.
10.1175/JTECH-D-16-0216.1
Publisher site
See Article on Publisher Site

Abstract

AbstractTyphoon rainfall predictions provide critical information that can be used for flood control and advanced disaster prevention preparations. However, total rainfall “nowcasts” (i.e., several days ahead) are not available in Taiwan when typhoons are distant. In this paper, we propose a long-distance total rainfall forecast (LTRF) model and present a real-time forecasting process that can use the LTRF model to determine the formation and possible approach of typhoons in the future. The LTRF model was formulated using two designed climate scenarios. Scenario 1 considered El Niño–Southern Oscillation (ENSO) effects, whereas Scenario 2 did not. Various raw sensor data, comprising climatological characteristics, sea surface temperature, satellite brightness temperatures, and total rainfall, were collected; moreover, attributes of the ENSO indices, including the Southern Oscillation Index and Niño 3.4 sea surface temperature anomaly, were reviewed. The scenario models were constructed using the C4.5 and Random Forests tree-based algorithms. Typhoon events occurring during 2001–2013 and 2014–2015 (specifically, Typhoons Matmo and Fung-Wong in 2014 and Soudelor and Dujuan in 2015) were examined for training and testing purposes, respectively. The Hualien Weather Station in Taiwan was selected as a study site, and the forecasting horizon was set at 6 hours. Finally, the model simulations, observations, and Central Weather Bureau (Taiwan) nowcasts were compared. The simulation results showed that the proposed LTRF model, when ENSO effects were accounted for, can efficiently forecast total typhoon rainfall when typhoons are distant from Taiwan.

Journal

Journal of Atmospheric and Oceanic TechnologyAmerican Meteorological Society

Published: Aug 18, 2017

References

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