Abstract
Data assimilation is an effective way to integrate observations into models. We will demonstrate how parameters in a model may be estimated by data assimilation in such a way that model simulations best fit observations. Data assimilation based on Bayesian inversion is used to retrieve posterior distributions of model parameters from observations. The Markov Chain Monte Carlo (MCMC) method is applied as a numerical method to home in on the parameter set that maximizes goodness of fit between model outputs and measurements.
| Original language | English (US) |
|---|---|
| Title of host publication | Land Carbon Cycle Modeling |
| Subtitle of host publication | Matrix Approach, Data Assimilation, Ecological Forecasting, and Machine Learning, Second Edition |
| Publisher | CRC Press |
| Pages | 140-145 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781040026298 |
| ISBN (Print) | 9781032698496 |
| DOIs | |
| State | Published - Jan 1 2024 |
All Science Journal Classification (ASJC) codes
- General Business, Management and Accounting
- General Agricultural and Biological Sciences
- General Earth and Planetary Sciences
- General Environmental Science
- General Engineering
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