Dr. Tomoaki Mega Profile

Dr. Tomoaki Mega

at Osaka Univ

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Publications (1)

Proceedings Article | 14 November 2021 Poster + Paper

Comparative study of deep neural networks for very short-term prediction of torrential rains using polarimetric Phased-Array Weather Radar (MP-PAWR)

Philippe Baron, Dong-Kyun Kim, Kohei Kawashima, Hiroshi Hanado, Takeshi Maesaka, Tomoaki Mega, Shinsuke Satoh, Eiichi Yoshikawa, Seiji Kawamura, Katsuhiro Nakagawa, Tomoo Ushio

Proceedings Volume 11914, 1191416 (2021) https://doi.org/10.1117/12.2601830

KEYWORDS: 3D modeling, Meteorology, Radar, Convolution, Neural networks, Polarimetry, Motion models, Atmospheric modeling

Read Abstract +

Under the Japanese Cross Strategic Innovation Promotion Program (SIP), studies are conducted to perform very short-term predictions of local torrential rains based on a new multi-parameter phased-array weather radar (MP-PAWR) and deep neural networks (DNNs). The association of the two methods is expected to overcome the limitations of the conventional rains observation systems and numerical models that are not well suited to handle the rapid non-linear processes inherent in heavy convective rains. The unique spatio-temporal resolution of the observations allows us to train supervised DNNs to extrapolate the fast evolution of 3D convective cells. We compared two DNNs (CLM3D and CGRU3D) designed to fully exploit the information in the vertical dimension. Both methods use new techniques involving spatial convolutions in temporal recurrent iterations such as Long Short-Term Memory (LSTM) or Gated Recurrent Units (GRU) ones. The core of CLM3D is a stack of convLTSM2D layers, each of which is applied to a single altitude. CGRU3D uses a multilayer encoderdecoder with convGRU3D layers, each layer is associated with a size of 3D spatial features. Forecasts with a lead-time of 10 min at an altitude of 600 m with a horizontal resolution of about 500 m are compared. The models are tested with different types of heavy precipitation: localized short-lived rains on July 24, 2018 and wide-spread ones on the 29 of the same month. The models are evaluated with respect to a 3D linear advection nowcast model (OF3D) and a persistent one. We found that the DNN and OF3D models perform better on July 24 with similar scores that are significantly higher than those of the persistent model. Considering all rain events, critical success indexes (CSI) of 0.62, 0.53, 0.55 are found for CGRU3D, CLM3D and OF3D, respectively, and 0.43 for the persistent model. Regarding only heavy precipitation, the CSIs show a great variability between 0 and 0.4 on the predictions made that day. These results clearly illustrate the great challenge of nowcasting heavy precipitation. On July 29, none of the models have significantly higher scores than those obtained with the persistent nowcast. The interesting result of this study is that the two DNNs show similar nowcasting skills whatever the intensity and the type of rain, and this despite their architectures and training strategies being different. This may indicate that optimizing the tunning of the hyperpameters and the training dataset could not bring significant improvements and, the key, could be by feeding the models with more comprehensive information on the atmospheric state.

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