||Investigating the effect of typhoon track on rainfall spatial distribution in a watershed using artificial neural networks
||Department of Water Resources and Environmental Engineering
Artificial neural network (ANN)
Self-organizing map (SOM)
Rainfall Spatial Distribution
了解颱風的降雨情況，有助於水資源與防災系統的管理與規劃。目前已經發展的颱風降雨預報模式有幾種，包括了數值模式 (Numerical Weather Prediction, NWP)，劇烈天氣監測系統 (Quantitative Precipitation Estimation and Segregation Using Multiple Sensors, QPESUMS)，系集模式颱風定量降水預報(Ensemble Typhoon Quantitative Precipitation Forecast, ETQPF)。過去的學者多在研究颱風內部的降雨分布，或者颱風於大面積的降雨情形，像是颱風為整個台灣所帶來的降雨量，卻鮮少有研究在討論集水區的颱風降雨情形。另外，雖然已經有研究利用類神經網路 (Artificial Neural Networks, ANNs) 分析與預測一般的降雨，但是颱風降雨的分析與預報依舊多使用數值方法或統計方法。
本論文利用前饋式、非監督式、競爭式的類神經網路——自組特徵映射類神經網路 (Self-organizing map, SOM) 來分析颱風期間石門水庫集水區的降雨時空分布。研究發現路徑相似的颱風，其降雨的空間變化也會相似。因此，颱風降雨的空間分布與颱風路徑有高度相關性；倘若有兩場路徑不同之颱風中心位於同一個經緯度，其降雨空間分布不一定相同。
由於颱風路徑會影響集水區降雨之時空分布，相似路徑之颱風對同一集水區降雨之時空分布影響相似，所形成雨型也較為相似。因此，本研究使用特徵雨量組體圖 (Feature hydrographs of rainfall, FHR) 來描述路徑類似之颱風所帶來的雨型。每場颱風歷經時間不同、強度不同，造成降雨強度與總雨量亦不相同，故須進行正規化，即將歷經時間換成 0~1 之間的數值，將時雨量轉換成百分比，再將該神經元內所有正規化雨量歷程進行平均，即為特徵雨量組體圖。研究結果顯示，特徵雨量組體圖若呈中央集中型，且峰值較高與較寬，其颱風降雨的破壞性較高。反之，分布相對平均的特徵雨量組體圖，一般表示颱風遠離石門水庫集水區，颱風對水庫的影響較低。一旦中央氣象局發布颱風預報，本研究可根據預報的颱風路徑，利用特徵雨量組體圖來估算該場颱風的雨量組體圖，提供防災機構與水庫操作單位非常有利的資訊。
||Typhoons hit Taiwan several times every year, which could cause serious flood disasters. Because mountainous terrains and steep landforms can rapidly accelerate the speed of flood during typhoon events, rivers cannot be a stable source of water supply. Reservoirs become the most effective floodwater storage facilities for alleviating flood damages in Taiwan. Forecasting typhoon rainfall is a long-standing and challenging issue due to the complexity of typhoon.
This study focused on typhoon rainfall including the spatial distribution of rainfall, the temporal distribution of rainfall (hyetographs) and the total rainfall. For the spatial distribution of rainfall, self-organizing map (SOM) is implemented to explore the spatial distribution of typhoon rainfall. The spatial distribution of rainfall has similar change processes for the similar tracks of the typhoons. Rainfall spatial distribution is highly related to the classification of the typhoon track; if two typhoons have different track, their rainfall spatial distributions would be probably different, even they are in the same location. The feature hyetographs of rainfall (FHR) in the same classification of typhoon tracks are constructed. For the classification of typhoon tracks with great impact on the Shihmen watershed, the rainfall hyetographs are central distributed with a higher and wider peak that reveals the destructivity of the typhoon rainfall. In contrast, the relatively flat distribution of rainfall hyetograph means the typhoon is far away from the watershed and less harmful. FHR can be used to estimate the rainfall hyetographs based on the forecast typhoon tracks, this is highly valuable to the emergency response agencies and reservoir operation units. The forecast error of ETQPF in the Shihmen watershed is investigated for improving the total rainfall forecast. If the locations of the typhoon are near the watershed, the ETQPF has higher accuracy. On the other hand, typhoons distancing from the watershed, usually have higher uncertainty in the factors of typhoon rainfall because of the effect of the typhoon outer circulation flow, instead of the typhoon main structure, resulting in lower accurate ETQPF forecast. This study proposes the weighted average method to recalculate the typhoon rainfall by selecting QPFs from well-performed ensemble members. The more reliable the ensemble members are, the higher the values their weights are. The weights of the ensemble members, based on the determination coefficient between the forecast typhoon tracks of ensemble members and the actual tracks, are given to recalculate the total rainfall. When highly reliable ensemble members are chosen, the improvement rate of rainfall prediction could be more than 90% compared to the ETQPF.
Table of Contents VII
List of Figures IX
List of Tables XI
Chapter 1 Introduction 1
1.1 Background 1
1.2 Study objectives 3
Chapter 2 Literature review 4
2.1 Application of artificial neural network 4
2.2 Estimating and forecasting rainfall using statistical methods 6
2.3 Estimating and forecasting rainfall using satellite images 8
2.4 SOM Clustering of typhoon track 10
Chapter 3 Methodology 12
3.1 Self-organizing map 12
3.1.1 Architecture of SOM 13
3.1.2 Algorithm of SOM 14
Chapter 4 Case study 18
4.1 Study Area 18
4.2 Data Collection 19
4.2.1 Rainfall data from rain gauges 19
4.2.2 QPE data of QPESUMS 20
4.2.3 QPF data of TTFRI’s EPS 21
4.2.4 ETQPF data 22
4.3 Investigating the spatial distribution of typhoon rainfall 27
4.3.1 SOM configuration for the rainfall ratio 27
4.3.2 Exploring the spatial distribution of rainfall along the typhoon path 32
4.3.3 Comparing the rainfall ratios at the intersection of different typhoon paths 32
4.4 Estimating the rainfall patterns during typhoon events 33
4.5 Evaluating the forecast error of ETQPF 34
4.6 Predicting rainfall by selecting well-performed ensemble members 34
Chapter 5 Results and discussions 40
5.1 SOM clustering of typhoon paths 40
5.2 The spatial distribution of typhoon rainfall 42
5.2.1 The changes in rainfall ratio sequence 42
5.2.2 The comparison of the rainfall ratio between typhoons with same location of centers 46
5.3 The rainfall patterns during typhoon events 49
5.4 The forecast error of ETQPF 51
5.5 Predicting rainfall by selecting well performed ensemble members 54
Chapter 6 Conclusions and Suggestions 59
6.1 Conclusions 59
6.2 Suggestions 61
Appendix A The results of the pattern changes of rainfall ratios 67
Appendix B The results of the rainfall forecast by selecting well-performed ensemble members 76
Figure 2.1 CWB typhoon tracks categorisation between 1911 and 2015. (Central Weather Bureau, 2016) 10
Figure 2.2 SOM topological map of typhoon track. (Tamkang University Information Center for Water Environment, 2018) 11
Figure 3.1 Architecture of Self-Organizing Map (SOM). 13
Figure 3.2 Schematic diagram of a SOM topological neighbourhood, showing a monotonic decrease in the neighbourhood. 15
Figure 3.3 Flow chart of SOM algorithm. 17
Figure 4.1 Location of the Shihmen watershed and 10 rainfall gauging stations. 18
Figure 4.2 The grid for vectorising typhoon paths for Shihmen watershed. (Tamkang University Information Center for Water Environment, 2018) 19
Figure 4.3 The grid points of QPESUMS and ETQPF of Shihmen watershed. 20
Figure 4.4 Three nested domains used in TTFRI-EPS. (Wu et al., 2018) 23
Figure 4.5 ETQPF Generation by selecting the ensemble cases, which are the numerical weather predictions (black dots) along a chosen typhoon path (dashed red line) that fall within a distance (pink circle). (Central Weather Bureau, 2018) 24
Figure 4.6 SOM topological map of the hourly rainfall ratio (10 × 10). Black arrows indicate the changes in rainfall ratio along the track of Typhoon Soudelor. 29
Figure 4.7 SOM topological map of the hourly rainfall ratio (10 × 10). Black arrows indicate the changes in rainfall ratio along the track of Typhoon Dujuan. 30
Figure 4.8 The selected typhoon paths with filtered ensemble cases in ETQPF (A) and the 48-hour accumulated rainfall forecasts (B) of the Typhoon Matmo from 00 UTC on July 22nd through 00 UTC on July 24th in 2014. (1), (2) and (3) are the forecasts of the left track, official track, and right track, respectively. (Central Weather Bureau, 2018) 35
Figure 4.9 The best track of Typhoon Matmo (Central Weather Bureau) 36
Figure 4.10 The predicted and observed rainfall amounts for Typhoon Matmo. (Central Weather Bureau) 36
Figure 5.1 SOM topological map of the hourly rainfall ratio of Shihmen watershed. 42
Figure 5.2 Changes in rainfall ratio along typhoon tracks of 3rd neuron’s typhoon track. 43
Figure 5.3 The path of Typhoon Soudelor (ID: 201513) with labelled rainfall ratio. 44
Figure 5.4 The path of Typhoon Dujuan (ID: 201521) with labelled rainfall ratio. 45
Figure 5.5 The path of Typhoon Megi (ID: 201617) with labelled rainfall ratio. 45
Figure 5.6 The rainfall ratio at the intersection of Typhoons Fung-Wong and Trami 47
Figure 5.7 The rainfall ratios at the intersection of Typhoons Morakot and Megi 47
Figure 5.8 The rainfall ratios at the intersection of Typhoons Sepat and Soudelor 48
Figure 5.9 The feature hyetograph of rainfall of every neuron in SOM clustering of typhoon path. 49
Figure 5.10 Scatter diagram of observed rainfall and ETQPF forecast rainfall. 53
Figure 5.11 Plot of the absolute forecast error against the ETQPF forecast rainfall. 53
Table 4.1 Selected typhoon events and collected data. 25
Table 4.2 QPE data of QPESUMS separation for SOM model’s training and testing. 31
Table 4.3 Similar typhoons whose pattern changes of the rainfall ratio are compared. 32
Table 5.1 SOM Clustering of typhoon paths. (Tamkang University Information Center for Water Environment, 2018) 40
Table 5.2 Comparison of observed rainfalls and ETQPF forecast rainfalls. 52
Table 5.3 Comparison of the performance of ensemble members’ typhoon track forecast for training events. (Neuron 3 of SOM clustering of typhoon path) 56
Table 5.4 QPFs forecasted by the ensemble members and corresponding weights calculated from R2 in Table 5.3. (Neuron 3 of SOM clustering of typhoon path) 57
Table 5.5 Comparison of three groups of total QPFs forecasted by the ensemble members, total observed rainfall and ETQPF’s total rainfall prediction. (Neuron 3 of SOM clustering of typhoon path) 58
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