scholarly journals Soil model parameter estimation with ensemble data assimilation

2009 ◽  
Vol 10 (2) ◽  
pp. 127-131 ◽  
Author(s):  
Biljana Orescanin ◽  
Borivoj Rajkovic ◽  
Milija Zupanski ◽  
Dusanka Zupanski
Keyword(s):  
Parameter Estimation ◽  
Data Assimilation ◽  
Model Parameter ◽  
Model Parameter Estimation ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Soil Model

scholarly journals Enhanced flood forecasting through ensemble data assimilation and joint state-parameter estimation

2019 ◽  
Vol 577 ◽  
pp. 123924 ◽  
Author(s):  
Matteo G. Ziliani ◽  
Rabih Ghostine ◽  
Boujemaa Ait-El-Fquih ◽  
Matthew F. McCabe ◽  
Ibrahim Hoteit
Keyword(s):  
Parameter Estimation ◽  
Data Assimilation ◽  
State Parameter ◽  
Flood Forecasting ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Joint State

Ensemble data assimilation for ocean biogeochemical state and parameter estimation at different sites

2017 ◽  
Vol 112 ◽  
pp. 65-89 ◽  
Author(s):  
M.E. Gharamti ◽  
J. Tjiputra ◽  
I. Bethke ◽  
A. Samuelsen ◽  
I. Skjelvan ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Data Assimilation ◽  
Ensemble Data Assimilation ◽  
Ensemble Data

scholarly journals An Investigation of Adaptive Radius for the Covariance Localization in Ensemble Data Assimilation

2021 ◽  
Vol 9 (11) ◽  
pp. 1156
Author(s):  
Xiang Xing ◽  
Bainian Liu ◽  
Weimin Zhang ◽  
Jianping Wu ◽  
Xiaoqun Cao ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Computational Cost ◽  
Adaptive Scheme ◽  
Model Parameter ◽  
Schur Product ◽  
Ensemble Data Assimilation ◽  
Localization Radius ◽  
Ensemble Data ◽  
Localization Scheme ◽  
Taper Function

The covariance matrix estimated from the ensemble data assimilation always suffers from filter collapse because of the spurious correlations induced by the finite ensemble size. The localization technique is applied to ameliorate this issue, which has been suggested to be effective. In this paper, an adaptive scheme for Schur product covariance localization is proposed, which is easy and efficient to implement in the ensemble data assimilation frameworks. A Gaussian-shaped taper function is selected as the localization taper function for the Schur product in the adaptive localization scheme, and the localization radius is obtained adaptively through a certain criterion of correlations with the background ensembles. An idealized Lorenz96 model with an ensemble Kalman filter is firstly examined, showing that the adaptive localization scheme helps to significantly reduce the spurious correlations in the small ensemble with low computational cost and provides accurate covariances that are similar to those derived from a much larger ensemble. The investigations of adaptive localization radius reveal that the optimal radius is model-parameter-dependent, vertical-level-dependent and nearly flow-dependent with weather scenarios in a realistic model; for example, the radius of model parameter zonal wind is generally larger than that of temperature. The adaptivity of the localization scheme is also illustrated in the ensemble framework and shows that the adaptive scheme has a positive effect on the assimilated analysis as the well-tuned localization.

Parameter estimation for ocean biogeochemical component in a global model using Ensemble Kalman Filter: a twin experiment

2021 ◽  
Author(s):  
Tarkeshwar Singh ◽  
Francois Counillon ◽  
Jerry F. Tjiputra ◽  
Mohamad El Gharamti
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Parameter Values

<p>Ocean biogeochemical (BGC) models utilize a large number of poorly-constrained global parameters to mimic unresolved processes and reproduce the observed complex spatio-temporal patterns. Large model errors stem primarily from inaccuracies in these parameters whose optimal values can vary both in space and time. This study aims to demonstrate the ability of ensemble data assimilation (DA) methods to provide high-quality and improved BGC parameters within an Earth system model in idealized twin experiment framework.  We use the Norwegian Climate Prediction Model (NorCPM), which combines the Norwegian Earth System Model with the Dual-One-Step ahead smoothing-based Ensemble Kalman Filter (DOSA-EnKF). The work follows on Gharamti et al. (2017) that successfully demonstrates the approach for one-dimensional idealized ocean BGC models. We aim to estimate five spatially varying BGC parameters by assimilating Salinity and Temperature hydrographic profiles and surface BGC (Phytoplankton, Nitrate, Phosphorous, Silicate, and Oxygen) observations in a strongly coupled DA framework – i.e., jointly updating ocean and BGC state-parameters during the assimilation. The method converges quickly (less than a year), largely reducing the errors in the BGC parameters and eventually it is shown to perform nearly as well as that of the system with true parameter values. Optimal parameter values can also be recovered by assimilating climatological BGC observations and challenging sparse observational networks. The findings of this study demonstrate the applicability of the approach for tuning the system in a real framework.</p><p> </p><p><strong>References</strong>:</p><p>Gharamti, M. E., Tjiputra, J., Bethke, I., Samuelsen, A., Skjelvan, I., Bentsen, M., & Bertino, L. (2017). Ensemble data assimilation for ocean biogeochemical state and parameter estimation at different sites. Ocean Modelling, 112, 65-89.</p>

Initiation of ensemble data assimilation

2006 ◽  
Author(s):  
M. Zupanski ◽  
S. J. Fletcher ◽  
I. M. Navon ◽  
B. Uzunoglu ◽  
R. P. Heikes ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Ensemble Data Assimilation ◽  
Ensemble Data
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