scholarly journals Online Model Parameter Estimation With Ensemble Data Assimilation in the Real Global Atmosphere: A Case With the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) and the Global Satellite Mapping of Precipitation Data

2018 ◽  
Author(s):  
Shunji Kotsuki ◽  
Koji Terasaki ◽  
Hisashi Yashiro ◽  
Hirofumi Tomita ◽  
Masaki Satoh ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Data Assimilation ◽  
Atmospheric Model ◽  
Precipitation Data ◽  
Model Parameter ◽  
The Real ◽  
Model Parameter Estimation ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Satellite Mapping

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 Performance evaluation of a throughput-aware framework for ensemble data assimilation: the case of NICAM-LETKF

2016 ◽  
Vol 9 (7) ◽  
pp. 2293-2300 ◽  
Author(s):  
Hisashi Yashiro ◽  
Koji Terasaki ◽  
Takemasa Miyoshi ◽  
Hirofumi Tomita
Keyword(s):  
Data Assimilation ◽  
High Performance ◽  
Weather Prediction ◽  
File Systems ◽  
Atmospheric Model ◽  
Distributed File Systems ◽  
Ensemble Data Assimilation ◽  
Computational Performance ◽  
Ensemble Data ◽  
Data Throughput

Abstract. In this paper, we propose the design and implementation of an ensemble data assimilation (DA) framework for weather prediction at a high resolution and with a large ensemble size. We consider the deployment of this framework on the data throughput of file input/output (I/O) and multi-node communication. As an instance of the application of the proposed framework, a local ensemble transform Kalman filter (LETKF) was used with a Non-hydrostatic Icosahedral Atmospheric Model (NICAM) for the DA system. Benchmark tests were performed using the K computer, a massive parallel supercomputer with distributed file systems. The results showed an improvement in total time required for the workflow as well as satisfactory scalability of up to 10 K nodes (80 K cores). With regard to high-performance computing systems, where data throughput performance increases at a slower rate than computational performance, our new framework for ensemble DA systems promises drastic reduction of total execution time.

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.

scholarly journals Performance evaluation of throughput-aware framework for ensemble data assimilation: The case of NICAM-LETKF

2016 ◽  
Author(s):  
H. Yashiro ◽  
K. Terasaki ◽  
T. Miyoshi ◽  
H. Tomita
Keyword(s):  
Data Assimilation ◽  
High Performance ◽  
Weather Prediction ◽  
File Systems ◽  
Atmospheric Model ◽  
Distributed File Systems ◽  
Ensemble Data Assimilation ◽  
Computational Performance ◽  
Ensemble Data ◽  
Data Throughput

Abstract. In this paper, we propose the design and implementation of an ensemble data assimilation (DA) framework for weather prediction at a high resolution and with a large ensemble size. We consider the deployment of this framework on the data throughput of file input/output (I/O) and multi-node communication. As an instance of the application of the proposed framework, a Local Ensemble Transform Kalman Filter (LETKF) was used with a Non-hydrostatic Icosahedral Atmospheric Model (NICAM) for the DA system. Benchmark tests were performed using the K computer, a massive parallel supercomputer with distributed file systems. The results showed an improvement in total time required for the workflow as well as satisfactory scalability of up to 10 K nodes (80 K cores). With regard to high-performance computing systems, where data throughput performance increases at a slower rate than computational performance, our new framework for ensemble DA systems promises drastic reduction of total execution time.

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