Prediction of Maximum Wind Speeds of Tropical Cyclones Over the South China Sea

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Date
2015-02
Authors
Haghroosta, Tahereh
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Abstract
Tropical cyclones (typhoons) are large-scale phenomena over warm waters in tropical areas. Strong winds, heavy precipitation, and storm surges are the most important natural disasters. In typhoon-prone areas, they are the cause of many financial and life losses each year. Thus, studying the behavior of typhoons and conducting accurate storm prediction are necessary for society and science. First, this study investigated the temporal and spatial variations of the sea surface temperature (SST), precipitation rate, latent heat flux (LHF), and sensible heat flux (SHF) with typhoons activity in the South China Sea during 1991-2011. These variations were examined based on the archived data from the National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP/NCAR). The effects of some typhoons on the mentioned parameters, sea level pressure (SLP), and the parameters’ anomalies were also studied. Second, the Weather Research and Forecasting (WRF) model was used to predict typhoon intensity and the mentioned parameters over the study area. The Climate Forecast System Reanalysis (CFSR) and the International Best Track Archive for Climate Stewardship (IBTrACS) datasets were used to analyze the model’s outputs and to investigate the model’s performance. Various configuration options available within the model can affect the performance of the model. Thus, different experiments were conducted to determine the best combination of physics parameterization schemes for simulation of the desired parameters, maximum wind, and typhoons’ track during the typhoon Noul. Then, the selected schemes were confirmed by seven other typhoons. Third, two different artificial intelligence methods, namely, an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) based on genetic algorithm (GA), were used to predict typhoon maximum wind speed. The results confirmed the superiority of the ANFIS-GA because its root mean square error was significantly lower than in ANN. Finally, in terms of wind speed prediction, the WRF and ANFIS were compared, and the results showed that the ANFIS model made better prediction with correlation coefficient of 0.98 versus 0.75 in WRF model. However, WRF model can be reliable when not much historical data are available. In fact, the comparison represents that the WRF model as a dynamical model considers the physical characteristic of the atmosphere and can be improved by researchers, and thus can perform reasonably.
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Keywords
Wind Speeds , South China Sea
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