Comparison of different sequential assimilation algorithms for satellite-derived leaf area index using the Data Assimilation Research Testbed (version Lanai)
<p>The leaf area index (LAI) is a crucial parameter for understanding the exchanges of mass and energy between terrestrial ecosystems and the atmosphere. In this study, the Data Assimilation Research Testbed (DART) has been successfully coupled to the Community Land Model with explicit carbon...
Main Authors: | , , , |
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Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2019-07-01
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Series: | Geoscientific Model Development |
Online Access: | https://www.geosci-model-dev.net/12/3119/2019/gmd-12-3119-2019.pdf |
Summary: | <p>The leaf area index (LAI) is a crucial parameter for
understanding the exchanges of mass and energy between terrestrial
ecosystems and the atmosphere. In this study, the Data Assimilation Research
Testbed (DART) has been successfully coupled to the Community Land Model
with explicit carbon and nitrogen components (CLM4CN) by assimilating Global
Land Surface Satellite (GLASS) LAI data. Within this framework, four
sequential assimilation algorithms, including the kernel filter (KF), the
ensemble Kalman filter (EnKF), the ensemble adjust Kalman filter (EAKF), and
the particle filter (PF), are thoroughly analyzed and compared. The results
show that assimilating GLASS LAI into the CLM4CN is an effective method for
improving model performance. In detail, the assimilation accuracies of the
EnKF and EAKF algorithms are better than those of the KF and PF algorithm.
From the perspective of the average and RMSD, the PF algorithm performs worse
than the EAKF and EnKF algorithms because of the gradually reduced
acceptance of observations with assimilation steps. In other words, the
contribution of the observations to the posterior probability during the
assimilation process is reduced. The EAKF algorithm is the best method
because the matrix is adjusted at each time step during the assimilation
procedure. If all the observations are accepted, the analyzed LAI seem to be
better than that when some observations are rejected, especially in
low-latitude regions.</p> |
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ISSN: | 1991-959X 1991-9603 |