Raindrop Size Distribution Characteristics for Tropical Cyclones and Meiyu-Baiu Fronts Impacting Tokyo, Japan

Chen, Yong; Duan, Jing; An, Junling; Liu, Huizhi

doi:10.3390/atmos10070391

Cited by 21 publications

(26 citation statements)

References 45 publications

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“…From Fig.5a & b, it can be seen that for both TY and NTY rainy days, with the increase of drop diameter classes, contribution to total number concentration decreased, while that of rainfall rate increased and then decreased. This characteristic also agrees with the findings of previous studies on tropical cyclones (Chen et al, 2019) and summer season rainfall (Wu et al, 2019). For both TY and NTY rainy days, small size drops (< 1 mm) predominantly contributed to a large number concentration (> 70%) and about 10% to rainfall rate.…”

Section: Contribution Of Raindrop Diameters To N T and Rsupporting

confidence: 92%

“…rates, the distributions of D m get narrowed. Similar behavior was reported in previous studies on tropical cyclone and summer season rainfall (Kumar and Reddy, 2013;Wen et al, 2018;Chang et al, 2009;Janapati et al, 2020;Chen et al, 2019;Wu et al, 2019). For both TY and NTY rainy days, fewer variations in D m values for R > 25 mm h −1 is due to the reaching of RSD to equilibrium condition through raindrop breakup and coalescence, (Hu and Srivastava, 1995), and an increase in number concentration can lead to a further increase in rainfall rates (Bringi and Chandrasekar, 2001).…”

Section: The Rainfall Rate Relationships With D M and N Wsupporting

confidence: 86%

“…There were studies in elucidating the tropical cyclones and non-tropical cyclones RSD characteristics of a given season over India, Australia, China, and Japan (Radhakrishna and Narayana Rao, 2010;Kumar and Reddy, 2013;Deo and Walsh, 2016;Chen et al, 2019;. At a south India station, Gadanki, more small and mid-size drops were observed in tropical cyclonic rainfall than non-cyclonic rainfall (Radhakrishna and Narayana Rao, 2010).…”

Section: Seasonal Characteristics Of Rsd Bymentioning

confidence: 99%

See 2 more Smart Citations

Microphysical features of typhoon and non-typhoon rainfall observed in Taiwan, an island in the northwest Pacific

Janapati¹,

Seela²,

Lin³

et al. 2020

Preprint

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Abstract. The microphysical features of the typhoon (TY) and non-typhoon (NTY) rainfall in summer seasons are analyzed using long-term (2004 to 2016) data from the impact disdrometer installed in north Taiwan. The RSD stratified based on rainfall rate showed distinct RSD characteristics between TY and NTY rainfall. More (less) number of small (big) size raindrops are noticed in TY rainfall than NTY rainfall. RSD features in terms of gamma parameters are studied for these two weather regimes. The mass-weighted mean diameter (Dm) values are higher in NTY than TY rainfall, and an inverse behavior is observed for the normalized intercept parameter (Nw). Even after separating the rainfall regimes into convective and stratiform type, a large Dm is found in NTY compared to TY precipitation. Distinct variations in Z–R, Dm–R, Nw–R, KE–R, and KE–Dm relations are noticed between TY and NTY rainfall. Possible mechanisms responsible for the RSD variations between TY and NTY are discussed using reanalysis, remote-sensing, and ground-based radar datasets.

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Section: Contribution Of Raindrop Diameters To N T and Rsupporting

confidence: 92%

Section: The Rainfall Rate Relationships With D M and N Wsupporting

confidence: 86%

Section: Seasonal Characteristics Of Rsd Bymentioning

confidence: 99%

See 1 more Smart Citation

Microphysical features of typhoon and non-typhoon rainfall observed in Taiwan, an island in the northwest Pacific

Janapati¹,

Seela²,

Lin³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Such newly developed models can be used to process radar images in atmospheric studies (e.g., rainfall inversion or typhoon intensity prediction). For example, Chen et al [49] proposed a satellite imagery-based CNN for estimating the tropical cyclone intensity. Tran and Song [50] predicted multichannel radar image sequences by using deep neural network-based image processing techniques.…”

Section: Introductionmentioning

confidence: 99%

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

Wei

Hsieh

2020

Remote Sensing

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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.

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“…Tang and Matyas [22] presented a methodology to forecast a tropical cyclone rainfall distribution up to 8 h into the future using a high-resolution Doppler radar reflectivity mosaic in a large analytical domain. Chen et al [23] reported the vertical structures of raindrop size distribution features and quantitative precipitation estimation parameters of two main synoptic systems, typhoons and meiyu/baiu fronts, based on summer observations with a ground-based impact disdrometer and a vertically pointing radar.…”

Section: Introductionmentioning

confidence: 99%

Extreme Gradient Boosting Model for Rain Retrieval using Radar Reflectivity from Various Elevation Angles

Wei

Hsu

2020

Remote Sensing

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The purpose of this study was to develop an optimal estimation model for rainfall rate retrievals using radar reflectivity, thereby gaining an effective grasp of rainfall information for disaster prevention uses. A process was designed for evaluating the optimal retrieval models using various dataset combinations with radar reflectivity and ground meteorological attributes. Various ground meteorological attributes (such as relative humidity, wind speed, precipitation, etc.) were obtained using the land-based weather stations affiliated with Taiwan’s Central Weather Bureau (CWB). This study used nine radar reflectivity provided by the Hualien weather surveillance radar station’s Volume Cover Pattern 21 system. The developed models are built using multiple machine learning algorithms, including linear regression (REG), support vector regression (SVR), and extreme gradient boosting (XGBoost), in addition to the Marshall–Palmer formula (MP). The study examined 14 typhoons that occurred from 2008 to 2017 at Chenggong station in southeast Taiwan, and Lanyu station in the outlying islands, and the top four major rainfall events were designated as test typhoons—Nanmadol (2011), Tembin (2012), Matmo (2014), and Nepartak (2016). The results indicated that for rainfall retrievals, radar reflectivity at a scanning (elevation) angle of 6.0° combined with ground meteorological attributes were the optimal input variables for the Chenggong station, whereas radar reflectivity at an elevation angle of 4.3° combined with ground meteorological attributes were optimal for the Lanyu station. In terms of model performance, XGBoost models had the lowest error index at Chenggong and Lanyu stations compared with MP, REG, and SVR models. XGBoost models at Lanyu station had the highest efficiency coefficient (0.903), and those at Chenggong station had the second highest (0.885). As a result, pairing the combination of optimal radar reflectivity and ground meteorological attributes, as verified by the evaluation process, with a high-efficiency algorithm (XGBoost) can effectively increase the accuracy of rainfall retrieval during typhoons.

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Raindrop Size Distribution Characteristics for Tropical Cyclones and Meiyu-Baiu Fronts Impacting Tokyo, Japan

Cited by 21 publications

References 45 publications

Microphysical features of typhoon and non-typhoon rainfall observed in Taiwan, an island in the northwest Pacific

Microphysical features of typhoon and non-typhoon rainfall observed in Taiwan, an island in the northwest Pacific

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

Extreme Gradient Boosting Model for Rain Retrieval using Radar Reflectivity from Various Elevation Angles

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