Improve weather forecasts by blending observations with models to create slowly evolving initial data.
This work applies estimation theory to numerical weather prediction, focusing on how to initialize forecasts so they evolve gradually. It compares standard data assimilation methods with a modified Kalman-Bucy approach that restricts updates to a slow-wave subspace, reducing fast, spurious disturbances.
Readers will see how projection techniques and careful weighting affect the assimilation process, and how different choices influence forecast quality over land and sea. The material blends theory with practical experiments on simple linear models to illustrate initialization and data assimilation in a clear, accessible way.
Ideal for readers of introductory data assimilation and numerical weather prediction, or anyone seeking a practical look at initialization methods in atmospheric science.
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HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # LX-9780267854783
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