著者

Daisuke Matsuoka Shiori Sugimoto Yujin Nakagawa Shintaro Kawahara Fumiaki Araki Yosuke Onoue Masaaki Iiyama Koji Koyamada

出版者

Meteorological Society of Japan

雑誌

SOLA (ISSN:13496476)

巻号頁・発行日

vol.15, pp.154-159, 2019 (Released:2019-07-23)

参考文献数

19

被引用文献数

7

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In this study, a stationary front is automatically detected from weather data using a U-Net deep convolutional neural network. The U-Net trained the transformation process from single/multiple physical quantities of weather data to detect stationary fronts using a 10-year data set. As a result of applying the trained U-Net to a 1-year untrained data set, the proposed approach succeeded in detecting the approximate shape of seasonal fronts with the exception of typhoons. In addition, the wind velocity (zonal and meridional components), wind direction, horizontal temperature gradient at 1000 hPa, relative humidity at 925 hPa, and water vapor at 850 hPa yielded high detection performance. Because the shape of the front extracted from each physical quantity is occasionally different, it is important to comprehensively analyze the results to make a final determination.

著者

Shiori Sugimoto

出版者

Meteorological Society of Japan

雑誌

SOLA (ISSN:13496476)

巻号頁・発行日

vol.16, pp.17-22, 2020 (Released:2020-02-13)

参考文献数

29

被引用文献数

12

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Heavy precipitation frequently occurs over Kyushu, southwestern Japan, during the Baiu season, and abundant moisture transport is a key driving factor. To statistically understand the intensification of moisture transport to Kyushu during the Baiu season, synoptic-scale atmospheric conditions are examined using a composite analysis of reanalysis data. A heavy precipitation day is defined as a day with area-averaged daily precipitation over Kyushu that is larger than 1.0 mm and ranked in top 10% during May 31 to July 19 from 1981 to 2015. During such heavy precipitation days, the precipitation observed over Kyushu exceeds 100 mm day−1. For several days before the occurrence of heavy precipitation over Kyushu, a plateau-scale disturbance develops over the Tibetan Plateau associated with daytime surface heating, and is characterized by cloud convection formation and eastward extension. During the eastward extension, latent heating from the cloud and upper-level high potential vorticity maintains the disturbance. The disturbance reaches northwest Kyushu on the heavy precipitation day, and a pair of positive and negative anomalies of relative vorticity over northwestern and southeastern Kyushu intensify the anomalous moisture transport.

著者

Shiori Sugimoto

出版者

Meteorological Society of Japan

雑誌

SOLA (ISSN:13496476)

巻号頁・発行日

pp.2020-004, (Released:2020-01-03)

被引用文献数

12

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Heavy precipitation frequently occurs over Kyushu, southwestern Japan, during the Baiu season, and abundant moisture transport is a key driving factor. To statistically understand the intensification of moisture transport to Kyushu during the Baiu season, synoptic-scale atmospheric conditions are examined using a composite analysis of reanalysis data. A heavy precipitation day is defined as a day with area-averaged daily precipitation over Kyushu that is larger than 1.0 mm and ranked in top 10% during May 31 to July 19 from 1981 to 2015. During such heavy precipitation days, the precipitation observed over Kyushu exceeds 100 mm day−1. For several days before the occurrence of heavy precipitation over Kyushu, a plateau-scale disturbance develops over the Tibetan Plateau associated with daytime surface heating, and is characterized by cloud convection formation and eastward extension. During the eastward extension, latent heating from the cloud and upper-level high potential vorticity maintains the disturbance. The disturbance reaches northwest Kyushu on the heavy precipitation day, and a pair of positive and negative anomalies of relative vorticity over northwestern and southeastern Kyushu intensify the anomalous moisture transport.

著者

Daisuke Matsuoka Shiori Sugimoto Yujin Nakagawa Shintaro Kawahara Fumiaki Araki Yosuke Onoue Masaaki Iiyama Koji Koyamada

出版者

Meteorological Society of Japan

雑誌

SOLA (ISSN:13496476)

巻号頁・発行日

pp.2019-028, (Released:2019-06-28)

被引用文献数

7

---

In this study, a stationary front is automatically detected from weather data using a U-Net deep convolutional neural network. The U-Net trained the transformation process from single/multiple physical quantities of weather data to detect stationary fronts using a 10-year data set. As a result of applying the trained U-Net to a 1-year untrained data set, the proposed approach succeeded in detecting the approximate shape of seasonal fronts with the exception of typhoons. In addition, the wind velocity (zonal and meridional components), wind direction, horizontal temperature gradient at 1000 hPa, relative humidity at 925 hPa, and water vapor at 850 hPa yielded high detection performance. Because the shape of the front extracted from each physical quantity is occasionally different, it is important to comprehensively analyze the results to make a final determination.

著者

Shiori Sugimoto Rui Ito Koji Dairaku Hiroaki Kawase Hidetaka Sasaki Shingo Watanabe Yasuko Okada Sho Kawazoe Takeshi Yamazaki Takahiro Sasai

出版者

Meteorological Society of Japan

雑誌

SOLA (ISSN:13496476)

巻号頁・発行日

vol.14, pp.46-51, 2018 (Released:2018-04-01)

参考文献数

32

被引用文献数

7

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To evaluate the influence of spatial resolution in numerical simulations on the duration of consecutive dry days (CDDs) and near-surface temperature over the central mountains in Japan, a regional climate model was used to conduct two experiments with horizontal resolutions of 5 and 20 km. Compared with observations, the spatial and temporal features of the CDDs were simulated well in the 5 km experiment, whereas in the 20 km simulation they were overestimated over the mountains and underestimated in the surrounding regions. The accuracy in the simulated CDDs affected the near-surface temperature in the model. In years with a difference of more than five days in the CDDs between the 5 and 20 km experiments, near-surface temperatures over the mountains were 0.2-0.3 K lower in the 5 km simulation compared with the 20 km simulation. This was due to the lower number of CDDs in 5 km simulation causing active cloud convection and reduced net radiation at the ground, resulting from a large decrease in the solar radiation at the ground. In addition, a land surface wetness controls a spatial heterogeneity of temperature difference between two experiments.