A robust ensemble Kalman filter to innovate short-range severe weather prediction

• Project/Area Number: 24K07131
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 17020:Atmospheric and hydrospheric sciences-related
• Research Institution: The University of Tokyo
• Principal Investigator: Le Duc 東京大学, 大学院工学系研究科(工学部), 助教 (50773157)
• Project Period (FY): 2024-04-01 – 2029-03-31
• Project Status: Granted (Fiscal Year 2024)
• Budget Amount: ¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000); Fiscal Year 2028: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000); Fiscal Year 2027: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000); Fiscal Year 2026: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000); Fiscal Year 2025: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000); Fiscal Year 2024: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
• Keywords: robust data assimilation / outliers / ensemble Kalman filter

Outline of Research at the Start

This study proposes a way to maximize the use of observations in weather forecasts. The proposed method effectively utilizes all observations without rejecting them. The method will be implemented with real-time observations over Japan with the aim of improving weather forecasts in extreme cases.