EGU23-5831, updated on 22 Feb 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

 Nowcasting localized heavy precipitation using a multi-parameter phased array weather radar (MP-PAWR) and a 3D recurrent neural network.

Philippe Baron1,2, Kouhei Kawashima2, Dong-Kyun Kim2, Hiroshi Hanado1, Takeshi Maesaka3, Shinsuke Satoh1, Seiji Kawamura1, and Tomoo Ushio2
Philippe Baron et al.
  • 1Remote sensing Laboratory, National Institute of Information and Communications Technology (NICT), koganei, tokyo, Japan (
  • 2Electrical Engineering Dept., Osaka University, Osaka, Japan
  • 3National Research Institute for Earth Science and Disaster Resilience (NIED), Tsukuba, Japan

Temporal extrapolation of radar observations of precipitation is a means of nowcasting sudden localized heavy rains, i.e., restricted convective rains on a spatial scale of less than 10 km and a lifetime of a few tens of minutes. Such nowcasts are necessary to set up warning systems to anticipate damage to infrastructure and reduce the fatalities these storms cause. It is a difficult task due to the storm suddenness, their restricted area, and nonlinear behavior that are not well captured by current operational systems, even for a lead time of only 10 minutes. Often, conventional approaches use radar observations with 5 min resolution and a Lagrangian advection based extrapolation model with a poor description of the vertical dimension. In this study, we use a new Multi-Parameter Phased-Array Weather Radar (MP-PAWR) with a temporal resolution of 30 sec and a 3D recurrent neural network to improve 10-minute nowcasts of sudden localized rains. The MP-PAWR has been operational in Japan (Saitama prefecture) since 2018. The nowcast model is a supervised neural network trained with adversarial technique. It considers the 3D volume surrounding the instrument up the height of 10 km and the polarimetric information of the measurement.  Improvements with conventional nowcasting techniques will be discussed with some typical examples.

How to cite: Baron, P., Kawashima, K., Kim, D.-K., Hanado, H., Maesaka, T., Satoh, S., Kawamura, S., and Ushio, T.:  Nowcasting localized heavy precipitation using a multi-parameter phased array weather radar (MP-PAWR) and a 3D recurrent neural network., EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-5831,, 2023.