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hess-22-4921-2018.pdf

🔗 Source: hess.copernicus.org

📊 Size: 848 KB

📄 Pages: 14 pages

⬇️ Downloads: 102

Summary

The ensemble Kalman filter (EnKF) is a popular data assimilation method in soil hydrology, used to estimate states and parameters simultaneously. However, due to unrepresented model errors and limited ensemble size, state and parameter uncertainties can become too small during assimilation. To address this issue, a novel multiplicative inflation method is proposed, specifically designed for soil hydrology. The method employs a Kalman filter within the EnKF to estimate inflation factors based on the difference between measurements and mean forecast state. It can adjust inflation factors to spatiotemporally varying model errors and successfully transfer inflation to parameters in the augmented state, leading to improved estimation.

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Description

The ensemble Kalman filter (EnKF) is a popular data assimilation method in soil hydrology, used to estimate states and parameters simultaneously.

Technical Information

File Format: PDF
File Size: 848 KB
Pages: 14
Language: EN
Total Downloads: 102
Last Updated: 3 hours ago

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