|Title:||Improvement of Typhoon Precipitation Forecast Efficiency by Coupling SSM/I Microwave Data with Climatologic Characteristics and Precipitation||Authors:||Chih-Chiang Wei||Keywords:||Forecasting;Hydrologic models;Neural networks||Issue Date:||1-Jun-2013||Source:||Wei, Chih-Chiang. "Improvement of typhoon precipitation forecast efficiency by coupling SSM/I microwave data with climatologic characteristics and precipitation." Weather and forecasting 28.3 (2013): 614-630.||Journal Volume:||28||Journal Issue:||3||Start page/Pages:||614–630||Source:||Weather and Forecasting||Abstract:||
Prediction of flash floods in an accurate and timely fashion is one of the most important challenges in weather prediction. This study aims to address the rainfall prediction problem for quantitative precipitation forecasts over land during typhoons. To improve the efficiency of forecasting typhoon precipitation, this study develops Bayesian network (BN) and logistic regression (LR) models using three different datasets and examines their feasibility under different rain intensities. The study area is the watershed of the Tanshui River in Taiwan. The dataset includes a total of 70 typhoon events affecting the watershed from 1997 to 2008. For practicability, the three datasets used include climatologic characteristics of typhoons issued by the Central Weather Bureau (CWB), rainfall rates measured using automatic meteorological gauges in the watershed, and microwave data originated from Special Sensor Microwave Imager (SSM/I) radiometers. Five separate BN and LR models (cases), differentiated by a unique combination of input datasets, were tested, and their predicted rainfalls are compared in terms of skill scores including mean absolute error (MAE), RMSE, bias (BIA), equitable threat score (ETS), and precision (PRE). The results show that the case where all three input datasets are used is better than the other four cases. Moreover, LR can provide better predictions than BN, especially in flash rainfall situations. However, BN might be one of the most prominent approaches when considering the ease of knowledge interpretation. In contrast, LR describes associations, not causes, and does not explain the decision.
|Appears in Collections:||海洋環境資訊系|
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