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Estimation of Hourly Rainfall During Typhoons Using Radar Mosaic-Based Convolutional Neural Networks

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Estimation of Hourly Rainfall During Typhoons Using Radar Mosaic-Based Convolutional Neural Networks remote sensing Article Estimation of Hourly Rainfall during Typhoons Using Radar Mosaic-Based Convolutional Neural Networks Chih-Chiang Wei * and Po-Yu Hsieh Department of Marine Environmental Informatics & Center of Excellence for Ocean Engineering, National Taiwan Ocean University, Keelung 20224, Taiwan; [email protected] * Correspondence: [email protected] Received: 7 February 2020; Accepted: 9 March 2020; Published: 10 March 2020 Abstract: Taiwan is located at the junction of the tropical and subtropical climate zones adjacent to the Eurasian continent and Pacific Ocean. The island frequently experiences typhoons that engender severe natural disasters and damage. Therefore, efficiently estimating typhoon rainfall in Taiwan is essential. This study examined the efficacy of typhoon rainfall estimation. Radar images released by the Central Weather Bureau were used to estimate instantaneous rainfall. Additionally, two proposed neural network-based architectures, namely a radar mosaic-based convolutional neural network (RMCNN) and a radar mosaic-based multilayer perceptron (RMMLP), were used to estimate typhoon rainfall, and the commonly applied Marshall–Palmer Z-R relationship (Z-R_MP) and a reformulated Z-R relationship at each site (Z-R_station) were adopted to construct benchmark models. Monitoring stations in Hualien, Sun Moon Lake, and Taichung were selected as the experimental stations in Eastern, Central, and Western Taiwan, respectively. This study compared the performance of the models in predicting rainfall at the three stations, and the results are outlined as follows: at the Hualien station, the estimations of the RMCNN, RMMLP, Z-R_MP, and Z-R_station models were mostly identical to the observed rainfall, and all models estimated an increase during peak rainfall on the hyetographs, but the peak values were underestimated. At the Sun Moon Lake and Taichung stations, however, the estimations of the four models were considerably inconsistent in terms of overall rainfall rates, peak rainfall, and peak rainfall arrival times on the hyetographs. The relative root mean squared error for overall rainfall rates of all stations was smallest when computed using RMCNN (0.713), followed by those computed using RMMLP (0.848), Z-R_MP (1.030), and Z-R_station (1.392). Moreover, RMCNN yielded the smallest relative error for peak rainfall (0.316), followed by RMMLP (0.379), Z-R_MP (0.402), and Z-R_station (0.688). RMCNN computed the smallest relative error for the peak rainfall arrival time (1.507 h), followed by RMMLP (2.673 h), Z-R_MP (2.917 h), and Z-R_station (3.250 h). The results revealed that the RMCNN model in combination with radar images could efficiently estimate typhoon rainfall. Keywords: rainfall; modeling; radar image; typhoon; CNN; deep learning 1. Introduction Taiwan is situated at the junction of the tropical and subtropical climate zones, bordering the Eurasian continent and the Pacific Ocean. Natural disasters frequently occur each year because of typhoons—the strong winds and rainfall of which often lead to severe disasters. In addition, the northwest Pacific Ocean is commonly affected by typhoons; approximately four typhoons strike Taiwan every year and cause serious severe damage [1,2]. For example, typhoon Megi, a typical moderate-strength typhoon, made landfall on the east coast of Taiwan in 2016. The strong winds and heavy rain caused severe damage; buildings were buried by a mudslide down a river, and 650,000 households experienced power outages, which resulted in US$100 million in economic Remote Sens. 2020, 12, 896; doi:10.3390/rs12050896 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, x FOR PEER REVIEW 2 of 19 heavy rain caused severe damage; buildings were buried by a mudslide down a river, and 650,000 Remotehouseholds Sens. 2020 experienced, 12, 896 power outages, which resulted in US$100 million in economic losses2 of[3] 19. Improving quantitative precipitation estimation (QPE) for tropical storms is crucial for disaster lossesmitigation [3]. [4,5] Improving. quantitative precipitation estimation (QPE) for tropical storms is crucial for disasterTaiwan’s mitigation Central [4,5 Mountain]. Range (CMR) runs from the north to the south of the island, dividing it intoTaiwan’s eastern and Central western Mountain parts. Range The CMR (CMR) is situated runs from in the east north and to theis 340 south km oflong the and island, 80 km dividing wide, itwith into an eastern average and elevation western of parts.approximately The CMR 2500 is situatedm [6]. When in the a typhoon east and strikes is 340 Taiwan km long from and the 80 east, km wide,it exerts with its an most average severe elevation effects ofon approximately the eastern regions, 2500 m [which6]. When sustain a typhoon considerable strikes Taiwandamage. from By thecontrast, east, itthe exerts western its most regions severe sustain effects less on damage the eastern because regions, the typhoon which sustain structure considerable is often disrupted damage. 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Therefore, efficiently estimating typhoon rainfall is crucialA radar for Taiwan.is commonly used for both weather surveillance and research to help meteorologists understandA radar the is commonlydynamics usedand formicrophysical both weather processes surveillance of atmospheric and research phenomena to help meteorologists [16]. Radar reflectivityunderstand is thea measure dynamics of the andfraction microphysical of electromagnetic processes waves of reflected atmospheric by precipitation phenomena particles [16]. (e.g.,Radar rain, reflectivity snow, or is hail). a measure Different of thecolors fraction are used of electromagnetic to create radar images waves reflectedaccording by to precipitationthe intensity particlesof the signal (e.g., reflected rain, snow, by precipitation or hail). 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