scholarly journals Research on PSO-BPNN Based Model for Forecasting Typhoon Rainfall

2020 ◽  
Vol 1650 ◽  
pp. 032026
Author(s):  
Pin Wang
Keyword(s):  
2014 ◽  
Vol 28 (1) ◽  
pp. 66-85 ◽  
Author(s):  
Chung-Chieh Wang ◽  
Bo-Xun Lin ◽  
Cheng-Ta Chen ◽  
Shih-How Lo

Abstract To quantify the effects of long-term climate change on typhoon rainfall near Taiwan, cloud-resolving simulations of Typhoon (TY) Sinlaku and TY Jangmi, both in September 2008, are performed and compared with sensitivity tests where these same typhoons are placed in the climate background of 1950–69, which is slightly cooler and drier compared to the modern climate of 1990–2009 computed using NCEP–NCAR reanalysis data. Using this strategy, largely consistent responses are found in the model although only two cases are studied. In control experiments, both modern-day typhoons yield more rainfall than their counterpart in the sensitivity test using past climate, by about 5%–6% at 200–500 km from the center for Sinlaku and roughly 4%–7% within 300 km of Jangmi, throughout much of the periods simulated. In both cases, the frequency of more-intense rainfall (20 to >50 mm h−1) also increases by about 5%–25% and the increase tends to be larger toward higher rain rates. Results from the water budget analysis, again quite consistent between the two cases, indicate that the increased rainfall from the typhoons in the modern climate is attributable to both a moister environment (by 2.5%–4%) as well as, on average, a more active secondary circulation of the storm. Thus, a changing climate may already have had a discernible impact on TC rainfall near Taiwan. While an overall increase in TC rainfall of roughly 5% may not seem large, it is certainly not insignificant considering that the long-term trend observed in the past 40–50 yr, whatever the causes might be, may continue for many decades in the foreseeable future.


2015 ◽  
Vol 30 (1) ◽  
pp. 217-237 ◽  
Author(s):  
Jing-Shan Hong ◽  
Chin-Tzu Fong ◽  
Ling-Feng Hsiao ◽  
Yi-Chiang Yu ◽  
Chian-You Tzeng

Abstract In this study, an ensemble typhoon quantitative precipitation forecast (ETQPF) model was developed to provide typhoon rainfall forecasts for Taiwan. The ETQPF rainfall forecast is obtained by averaging the pick-out cases, which are screened using certain criterion based on given typhoon tracks from an ensemble prediction system (EPS). Therefore, the ETQPF model resembles a climatology model. However, the ETQPF model uses the quantitative precipitation forecasts (QPFs) from an EPS instead of historical rainfall observations. Two typhoon cases, Fanapi (2010) and Megi (2010), are used to evaluate the ETQPF model performance. The results show that the rainfall forecast from the ETQPF model, which is qualitatively compared and quantitatively verified, provides reasonable typhoon rainfall forecasts and is valuable for real-time operational applications. By applying the forecast track to the ETQPF model, better track forecasts lead to better ETQPF rainfall forecasts. Moreover, the ETQPF model provides the “scenario” of the typhoon QPFs according to the uncertainty of the forecast tracks. Such a scenario analysis can provide valuable information for risk assessment and decision making in disaster prevention and reduction. Deficiencies of the ETQPF model are also presented, including that the average over the pick-out case usually offsets the extremes and reduces the maximum ETQPF rainfall, the underprediction is especially noticeable for weak phase-locked rainfall systems, and the ETQPF rainfall error is related to the model bias. Therefore, reducing model bias is an important issue in further improving the ETQPF model performance.


Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 870 ◽  
Author(s):  
Chih-Chiang Wei ◽  
Tzu-Hao Chou

Situated in the main tracks of typhoons in the Northwestern Pacific Ocean, Taiwan frequently encounters disasters from heavy rainfall during typhoons. Accurate and timely typhoon rainfall prediction is an imperative topic that must be addressed. The purpose of this study was to develop a Hadoop Spark distribute framework based on big-data technology, to accelerate the computation of typhoon rainfall prediction models. This study used deep neural networks (DNNs) and multiple linear regressions (MLRs) in machine learning, to establish rainfall prediction models and evaluate rainfall prediction accuracy. The Hadoop Spark distributed cluster-computing framework was the big-data technology used. The Hadoop Spark framework consisted of the Hadoop Distributed File System, MapReduce framework, and Spark, which was used as a new-generation technology to improve the efficiency of the distributed computing. The research area was Northern Taiwan, which contains four surface observation stations as the experimental sites. This study collected 271 typhoon events (from 1961 to 2017). The following results were obtained: (1) in machine-learning computation, prediction errors increased with prediction duration in the DNN and MLR models; and (2) the system of Hadoop Spark framework was faster than the standalone systems (single I7 central processing unit (CPU) and single E3 CPU). When complex computation is required in a model (e.g., DNN model parameter calibration), the big-data-based Hadoop Spark framework can be used to establish highly efficient computation environments. In summary, this study successfully used the big-data Hadoop Spark framework with machine learning, to develop rainfall prediction models with effectively improved computing efficiency. Therefore, the proposed system can solve problems regarding real-time typhoon rainfall prediction with high timeliness and accuracy.


2006 ◽  
Vol 37 (1-2) ◽  
pp. 87-105 ◽  
Author(s):  
Cheng-Shang Lee ◽  
Li-Rung Huang ◽  
Horng-Syi Shen ◽  
Shi-Ting Wang
Keyword(s):  

2013 ◽  
Vol 303-306 ◽  
pp. 1524-1527
Author(s):  
Xu Dong Liu ◽  
Wei Wang

According to the nature of 16 - the intersection model and the constraints of the broad boundary region, get 67 topological relations that can be realized between a concave region and a simple broad boundary region. We realize the reasoning of topological relations between a concave region and a simple broad boundary region, and establish topology model of typhoon rainfall warning, and provide reference for expand of the related topology model applications.


2012 ◽  
Vol 70 (3) ◽  
pp. 1763-1793 ◽  
Author(s):  
Tsung-Yi Pan ◽  
Lung-Yao Chang ◽  
Jihn-Sung Lai ◽  
Hsiang-Kuan Chang ◽  
Cheng-Shang Lee ◽  
...  

Author(s):  
Jenq-Dar Tsay ◽  
Kevin Kao ◽  
Chun-Chieh Chao ◽  
Yu-Cheng Chang

Rainfall retrieval using geostationary satellites provides critical means to the monitoring of extreme rainfall events. Using the relatively new Himawari 8 meteorological satellite with three times more channels than its predecessors, the deep learning framework of “convolutional autoencoder” (CAE) was applied to the extraction of cloud and precipitation features. The CAE method was incorporated into the Convolution Neural Network version of the PERSIANN precipitation retrieval that uses GOES satellites. By applying the CAE technique with the addition of Residual Blocks and other modifications of deep learning architecture, the presented derivation of PERSIANN operated at the Central Weather Bureau of Taiwan (referred to as PERSIANN-CWB) expands four extra convolution layers to fully use Himawari 8’s infrared and water vapor channels, while preventing degradation of accuracy caused by the deeper network. The development of PERSIANN-CWB was trained over Taiwan for its diverse weather systems and localized rainfall features, and the evaluation reveals an overall improvement from its CNN counterpart and superior performance over all other rainfall retrievals analyzed. Limitation of this model was found in the derivation of typhoon rainfall, an area requiring further research.


2017 ◽  
Vol 28 (1) ◽  
pp. 11-21
Author(s):  
Yi-Ling Chang ◽  
Hsi-Chyi Yeh ◽  
Gin-Rong Liu ◽  
Chian-Yi Liu ◽  
Chung-Chih Liu ◽  
...  

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