Mesoscale ensemble-based data assimilation and parameter estimation
The performance of the ensemble Kalman filter (EnKF) in forced, dissipative flow under imperfect model conditions is investigated through simultaneous state and parameter estimation where the source of model error is the uncertainty in the model parameters. Two numerical models with increasing compl...
Main Author: | Aksoy, Altug |
---|---|
Other Authors: | Nielsen-Gammon, John W. |
Format: | Others |
Language: | en_US |
Published: |
Texas A&M University
2005
|
Subjects: | |
Online Access: | http://hdl.handle.net/1969.1/2523 |
Similar Items
-
Mesoscale Resolution Radar Data Assimilation Experiments with the Harmonie Model
by: Serguei Ivanov, et al.
Published: (2018-09-01) -
Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation
by: Meng, Zhiyong
Published: (2007) -
Relevance of climatological background error statistics for mesoscale data assimilation
by: Jelena Bojarova, et al.
Published: (2019-01-01) -
Atmospheric Simulations Using OGCM-Assimilation SST: Influence of the Wintertime Japan Sea on Monthly Precipitation
by: Masaru Yamamoto Naoki Hirose
Published: (2010-01-01) -
Downscaling wind energy resource from mesoscale to local scale by nesting and data assimilation with a CFD model
by: Duraisamy Jothiprakasam, Venkatesh
Published: (2014)