scholarly journals Ionospheric dynamics and drivers obtained from a physics-based data assimilation model

Radio Science ◽  
2009 ◽  
Vol 44 (1) ◽  
pp. n/a-n/a ◽  
Author(s):  
Ludger Scherliess ◽  
Donald C. Thompson ◽  
Robert W. Schunk
Keyword(s):  
Data Assimilation ◽  
Assimilation Model ◽  
Ionospheric Dynamics

Terminator field-aligned currents: A new finding from the Ionospheric Dynamics and Electrodynamics Data Assimilation Model

2014 ◽  
Vol 119 (6) ◽  
pp. 4752-4757 ◽  
Author(s):  
L. Zhu ◽  
R. W. Schunk ◽  
V. Eccles ◽  
L. Scherliess ◽  
J. J. Sojka ◽  
...  
Keyword(s):  
Data Assimilation ◽  
New Finding ◽  
Assimilation Model ◽  
Ionospheric Dynamics

A data assimilation model for wall pressure-driven mean flow reconstruction

2022 ◽  
Vol 34 (1) ◽  
pp. 015101
Author(s):  
Sen Li ◽  
Chuangxin He ◽  
Yingzheng Liu
Keyword(s):  
Data Assimilation ◽  
Wall Pressure ◽  
Mean Flow ◽  
Flow Reconstruction ◽  
Assimilation Model ◽  
Pressure Driven

scholarly journals Numerical simulation of groundwater in an unconfined aquifer with a novel hybrid model (case study: Birjand Aquifer, Iran)

2021 ◽  
Author(s):  
Ali Mohtashami ◽  
Seyed Arman Hashemi Monfared ◽  
Gholamreza Azizyan ◽  
Abolfazl Akbarpour
Keyword(s):  
Numerical Modeling ◽  
Data Assimilation ◽  
Hybrid Model ◽  
Numerical Models ◽  
Unconfined Aquifer ◽  
Model Case ◽  
Hydrodynamic Parameters ◽  
East Of Iran ◽  
Assimilation Model

Abstract In recent decades, due to the population growth and low precipitation, the overexploitation of ground water resources has become an important issue. To ensure a sustainable scheme for these resources, understanding the behavior of the aquifers is a key step. This study takes a numerical modeling approach to investigate the behavior of an unconfined aquifer in an arid area located in the east of Iran. A novel hybrid model is proposed that couples the numerical modeling to a data assimilation model to remove the uncertainty in the hydrodynamic parameters of the aquifer including the hydraulic conductivity coefficients and specific yields. The uncertainty that exists in these parameters results in unreliability of the head values acquired from the models. Meshless local Petrov-Galerkin (MLPG) is used as the numerical model, and particle filter (PF) is our data assimilation model. These models are implemented in the MATLAB software. We have calibrated and validated our PF-MLPG model by the observation head data from the piezometers. The RMSE in head values for our model and other commonly used numerical models in the literature including the finite difference method and MPLG are calculated as 0.166, 1.197 and 0.757 m, respectively. This fact shows the necessity of using this method in each aquifer.

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