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JDR Vol.17 No.7 pp. 1140-1149
(2022)
doi: 10.20965/jdr.2022.p1140

Paper:

Hybrid Scheme of Kinematic Analysis and Lagrangian Koopman Operator Analysis for Short-Term Precipitation Forecasting

Shitao Zheng*, Takashi Miyamoto*,**,†, Koyuru Iwanami***, Shingo Shimizu***, and Ryohei Kato***

*University of Yamanashi
4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan

Corresponding author

**German Research Center for Artificial Intelligence, Kaiserslautern, Germany

***National Research Institute for Earth Science and Disaster Resilience (NIED), Tsukuba, Japan

Received:
November 4, 2021
Accepted:
June 27, 2022
Published:
December 1, 2022
Keywords:
Koopman operator analysis, nonlinear dynamics, data-driven analysis, precipitation predict
Abstract

With the accumulation of meteorological big data, data-driven models for short-term precipitation forecasting have shown increasing promise. We focus on Koopman operator analysis, which is a data-driven scheme to discover governing laws in observed data. We propose a method to apply this scheme to phenomena accompanying advection currents such as precipitation. The proposed method decomposes time evolutions of the phenomena between advection currents under a velocity field and changes in physical quantities under Lagrangian coordinates. The advection currents are estimated by kinematic analysis, and the changes in physical quantities are estimated by Koopman operator analysis. The proposed method is applied to actual precipitation distribution data, and the results show that the development and decay of precipitation are properly captured relative to conventional methods and that stable predictions over long periods are possible.

Cite this article as:
S. Zheng, T. Miyamoto, K. Iwanami, S. Shimizu, and R. Kato, “Hybrid Scheme of Kinematic Analysis and Lagrangian Koopman Operator Analysis for Short-Term Precipitation Forecasting,” J. Disaster Res., Vol.17, No.7, pp. 1140-1149, 2022.
Data files:
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