Data assimilation in a multi-scale model

DOI10.1515/MCWF-2017-0006zbMath1504.86028OpenAlexW2789558175MaRDI QIDQ2681720

Guannan Hu, Christian L. E. Franzke

Publication date: 3 February 2023

Published in: Mathematics of Climate and Weather Forecasting (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1515/mcwf-2017-0006

zbMATH Keywords

error analysismodel errormulti-scale modelLorenz-96 model

Mathematics Subject Classification ID

Geostatistics (86A32) Meteorology and atmospheric physics (86A10) Climate science and climate modeling (86A08)

Uses Software

• EnKF

Cites Work

• A computational strategy for multiscale systems with applications to Lorenz 96 model
• Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter
• Normal forms for reduced stochastic climate models
• Models for stochastic climate prediction
• Comparison of extended and ensemble Kalman filters for data assimilation in coastal area modelling
• Nonlinear and Stochastic Climate Dynamics
• Linear theory for filtering nonlinear multiscale systems with model error
• An applied mathematics perspective on stochastic modelling for climate

This page was built for publication: Data assimilation in a multi-scale model