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|Other Titles:||Time Series Analysis and Forecast Models for Rainfall in Muar, Johor|
Yuen Yi Ng
Department of Geography, NTNU
|Abstract:||本研究使用1951 年至2007 年APHRODITE( Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources) 0.25度X 0.25 度高解析度日降雨網格資料，並將之直到分為年、月和季三組降雨資料分析馬來西亞柔佛州地區的長期降雨趨勢和進行預測。研究結果顯示，麻坡地區的年降雨近年來有上升的趨勢，經傅立葉轉換(Fast Fourier Transform, FFT)結果顯示，此區的降雨週期訊號以4 年、2-3 特和每年的週期循環為主，此規律循環可推測為TBO( Tropospheric Biennial Oscillation) 和ENSO (EI-Nino Southern Oscillation) 事件的影響，近年來麻坡地區降雨對ENSO 事件的訊號越趨明顯。此外，冬季降雨在1998 年前後出現明顯的上升趨勢，推估t1l;近年冬季季風的增強，加速了熱帶地區的大氣擾動。根據此區的降雨變率分析，冬季在II 和12 月的氣象災害以水災為主，而春季在2 月則需留意高溫乾旱的天氣現象。本研究採用ARJMA (Autoregressive Integrated Moving Average) 時間序列模式進行降雨預測，預測結果顯示有忽略極端值的存在。|
This study was carried out using the APHRODITE (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources) 0.25° X 0.25° high resolution daily rainfall grid data from 1951-2007. All data set were divided into annual, monthly, and seasonal rainfall data to analyze the long-tenn rainfall trend and rainfall forecast in Muac. The results showed that there was an increasing trend in annual rainfall fe，己eotty. The signals of 4 year, 2-3year, and annual cycles were found in Muae rainfall cycles through Fast Fourier Transfonn (FFT). Based on the above mentioned data, this rainfall pattern was suggested to be contributed by the TBO (Tropospheric Biennial Oscillation) and more significantly, by the ENSO (EI-Nino Southern Oscillat凹的events. Furthermore, the rainfall in the winter has been increasing since 1998. It implicated an intensified East Asian winter monsoon that led to an accelerated perturbation in the tropical region. According to the analyzed relationships between the rainfall variability and meteorological disaster, floods were the major concern in November and December, while drought was the prime woe in February. ARIMA time series model was used to forecast the annual rainfall in Muar. Nevertheless, it has a shortcoming of overlooking the extreme rainfall data.
|Appears in Collections:||地理研究|
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