Probabilistic seasonal forecast of extreme events based on a numerical climate model

2017 Fiscal Year Final Research Report

• Project/Area Number: 26800243
• Research Category: Grant-in-Aid for Young Scientists (B)
• Allocation Type: Multi-year Fund
• Research Field: Meteorology/Physical oceanography/Hydrology
• Research Institution: Japan, Meteorological Research Institute
• Principal Investigator: Imada (Kanamaru) Yukiko (金丸由紀子) 気象庁気象研究所, 気候研究部, 主任研究官 (50582855)
• Project Period (FY): 2014-04-01 – 2018-03-31
• Keywords: 季節予報 / 異常気象の確率予測 / ラージアンサンブル / 気候モデル / ダウンスケーリング / 九州豪雨

Outline of Final Research Achievements

It is challenging to apply the seasonal prediction system based on a numerical climate model to probabilistic forecast of local extreme events such as orographic local heavy rainfall due to lack of ensemble size and spatial resolution. In this work, I conducted large-ensemble seasonal hindcast in combination with dynamical downscaling based on a high-resolution regional climate model (RCM), and challenged probabilistic hindcast of local extreme heavy rainfall in Kyushu.
The RCM can capture regional differences in heavy rainfall processes, resulting in improved potential predictability of the event probability. Furthermore, probabilistic prediction skill has also been improved through the use of enough ensemble members, although it does not reach a level of practical use. Further advanced numerical models are required.

Free Research Field

気候力学・気候モデリング