Evaluation of the observation operator Jacobian for leaf area index data assimilation with an extended Kalman filter

2010 ◽  
Vol 115 (D9) ◽  
Author(s):  
Christoph Rüdiger ◽  
Clément Albergel ◽  
Jean-François Mahfouf ◽  
Jean-Christophe Calvet ◽  
Jeffrey P. Walker
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Extended Kalman Filter ◽  
Area Index ◽  
Observation Operator ◽  
Index Data

scholarly journals Comparison of different sequential assimilation algorithms for satellite-derived leaf area index using the Data Assimilation Research Testbed (version Lanai)

2019 ◽  
Vol 12 (7) ◽  
pp. 3119-3133 ◽  
Author(s):  
Xiao-Lu Ling ◽  
Cong-Bin Fu ◽  
Zong-Liang Yang ◽  
Wei-Dong Guo
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Terrestrial Ecosystems ◽  
Time Step ◽  
Area Index ◽  
Community Land Model ◽  
Better Than

Abstract. 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.

scholarly journals Comparison of Different Sequential Assimilation Algorithms for Satellite-derived Leaf Area Index Using the Data Assimilation Research Testbed (lanai)

2019 ◽  
Author(s):  
Xiao-Lu Ling ◽  
Cong-Bin Fu ◽  
Zong-Liang Yang ◽  
Wei-Dong Guo
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Terrestrial Ecosystems ◽  
Remotely Sensed ◽  
Time Step ◽  
Area Index ◽  
Community Land Model

Abstract. The leaf area index (LAI) is a crucial parameter for understanding the exchanges of momentum, carbon, energy, and water between terrestrial ecosystems and the atmosphere. To improve the ability to simulate land surface water and energy balances, the Data Assimilation Research Testbed (DART) has been successfully coupled to the Community Land Model (CLM) by assimilating global remotely sensed LAI data with explicit carbon and nitrogen components (CLM4CN). The purpose of this paper is to determine the best algorithm for LAI assimilation. Within this framework, four sequential assimilation algorithms, i.e., the Kalman Filter (KF), the Ensemble Kalman Filter (EnKF), the Ensemble Adjust Kalman Filter (EAKF), and the Particle Filter (PF), are applied, thoroughly analyzed and compared. The results show that assimilating remotely sensed LAI data into the CLM4CN is an effective method for improving model performance. In detail, the assimilation accuracies of the ensemble filter algorithms (EnKF and EAKF) are better than that of the KF algorithm because the KF is based on the linear model error assumption. 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.

scholarly journals The Influence of Assimilating Leaf Area Index in a Land Surface Model on Global Water Fluxes and Storages

2019 ◽  
Author(s):  
Xinxuan Zhang ◽  
Viviana Maggioni ◽  
Azbina Rahman ◽  
Paul Houser ◽  
Yuan Xue ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Water Storage ◽  
Land Surface Model ◽  
Surface Model ◽  
Area Index ◽  
Global Water

Abstract. Vegetation plays a fundamental role not only in the energy and carbon cycle, but also the global water balance by controlling surface evapotranspiration. Thus, accurately estimating vegetation-related variables has the potential to improve our understanding and estimation of the dynamic interactions between the water and carbon cycles. This study aims to assess to what extent a land surface model can be optimized through the assimilation of leaf area index (LAI) observations at the global scale. Two observing system simulation experiments (OSSEs) are performed to evaluate the efficiency of assimilating LAI through an Ensemble Kalman Filter (EnKF) to estimate LAI, evapotranspiration (ET), interception evaporation (CIE), canopy water storage (CWS), surface soil moisture (SSM), and terrestrial water storage (TWS). Results show that the LAI data assimilation framework effectively reduces errors in LAI simulations. LAI assimilation also improves the model estimates of all the water flux and storage variables considered in this study (ET, CIE, CWS, SSM, and TWS), even when the forcing precipitation is strongly positively biased (extremely wet condition). However, it tends to worsen some of the model estimated water-related variables (SSM and TWS) when the forcing precipitation is affected by a dry bias. This is attributed to the fact that the amount of water in the land surface model is conservative and the LAI assimilation introduces more vegetation, which requires more water than what available within the soil. Future work should investigate a multi-variate data assimilation system that concurrently merges both LAI and soil moisture (or TWS) observations.

scholarly journals Observed Vegetation Greening and Its Relationships with Cropland Changes and Climate in China

Land ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 274
Author(s):  
Yuzhen Zhang ◽  
Shunlin Liang ◽  
Zhiqiang Xiao
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Boosted Regression Trees ◽  
Trend Detection ◽  
Area Index ◽  
Vegetation Greenness ◽  
Detection Algorithms ◽  
Index Data

Chinese croplands have changed considerably over the past decades, but their impacts on the environment remain underexplored. Meanwhile, understanding the contributions of human activities to vegetation greenness has been attracting more attention but still needs to be improved. To address both issues, this study explored vegetation greening and its relationships with Chinese cropland changes and climate. Greenness trends were first identified from the normalized difference vegetation index and leaf area index from 1982–2015 using three trend detection algorithms. Boosted regression trees were then performed to explore underlying relationships between vegetation greening and cropland and climate predictors. The results showed the widespread greening in Chinese croplands but large discrepancies in greenness trends characterized by different metrics. Annual greenness trends in most Chinese croplands were more likely nonlinearly associated with climate compared with cropland changes, while cropland percentage only predominantly contributed to vegetation greening in the Sichuan Basin and its surrounding regions with leaf area index data and, in the Northeast China Plain, with vegetation index data. Results highlight both the differences in vegetation greenness using different indicators and further impacts on the nonlinear relationships with cropland and climate, which have been largely ignored in previous studies.

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