Precision and convergence speed of the ensemble Kalman filter-based parameter estimation: setting parameter uncertainty for reliable and efficient estimation

Precision and convergence speed of the ensemble Kalman filter-based parameter estimation: setting parameter uncertainty for reliable and efficient estimation

Abstract Determining physical process parameters in atmospheric models is critical to obtaining accurate weather and climate simulations; estimating optimal parameters is essential for reducing model error. Recently, automatic parameter estimation using the ensemble Kalman filter (EnKF) has been tes...

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Bibliographic Details
Main Authors: Kenta Sueki, Seiya Nishizawa, Tsuyoshi Yamaura, Hirofumi Tomita
Format: Article
Language:English
Published: SpringerOpen 2022-09-01
Series:Progress in Earth and Planetary Science
Subjects:
Parameter estimation
Data assimilation
Ensemble Kalman filter
Atmospheric model
Cloud microphysics
Deep convective system
Online Access:https://doi.org/10.1186/s40645-022-00504-4

Internet

https://doi.org/10.1186/s40645-022-00504-4

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