Title
Heavy Precipitation Forecasting using the Combination ofLocal and Global Modeswith Application to Malaysian Rainfall

Authors

Junaida Binti Sulaiman


Source
九州工業大学大学院情報工学研究院情報科学博士論文(情報工学、情工博甲第2??号), pp. 1-100 (2015.1.13)

Abstract
Successive days of precipitation are known to cause flooding in monsoon-susceptible countries. The analysis of extreme precipitation trends is important for the prediction of high precipitation events. Forecasting of daily precipitation facilitates the prediction of the occurrences of rainfall and number of wet days. Using the maximum five-day accumulated precipitation (MX5d) index, we can predict the magnitude of precipitation in a specific period as it may indicate the extreme precipitation. Traditionally, a data-driven model is built on a whole data set describing the phenomenon within the data. This type of model does not considered the seasonal processes embedded in the data. Therefore, there is a need to built a model that encompassing different time scale and localized. One of the ways of doing this is to discover the different physically embedded relationships in precipitation process at different seasonal period and to built separate localized models for each of these seasonal periods. The use of committee models such as modular and ensemble models in weather and hydrological forecasting are increasing day by day. The study uses the modular concept by separating the heavy precipitation events based on sub-processes which are the seasonal monsoon and trained the subset of seasonal data using data driven models. Besides that, the study is carried out to evaluate the influence of global climate indices on local precipitation. It is interesting to see the influence of combining global and local predictors on local precipitation events. The method used past MX5d data and global climate indices such as Southern Oscillation Index (SOI), Madden Julian Oscillation (MJO), and Dipole Mode Index (DMI) in Kuantan and Kota Bharu, Malaysia using modular model trained on subset of data that represent the seasonal monsoon. The analysis started with evaluating the local and global inputs (MX5d with SOI, MJO, and DMI) in order to investigate the concurrent effect of lagged values of local precipitation data and global climate indices on seasonal extreme precipitation. Four subset of data are sampled representing two major seasonal variations in Malaysia. The experimental data are focused on the east coast area of Malaysia such that the effect of northeast monsoon season causes heavy precipitation events. The results show that the combination of local and global modes in a modular model is favorable than a single local mode. The proposed modular model is promising an encouraging result when different subset of data are trained on separate methods with different parameter values.

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