Direct Assimilation of Radar Data With Ensemble Kalman Filter and Hybrid Ensemble‐Variational Method in the National Weather Service Operational Data Assimilation System GSI for the Stand‐Alone Regional FV3 Model at a Convection‐Allowing Resolution

2020 ◽  
Vol 47 (19) ◽  
Author(s):  
Chong‐Chi Tong ◽  
Youngsun Jung ◽  
Ming Xue ◽  
Chengsi Liu
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Variational Method ◽  
Ensemble Kalman Filter ◽  
Radar Data ◽  
National Weather Service ◽  
Operational Data ◽  
Data Assimilation System ◽  
Assimilation System ◽  
Weather Service

An ensemble Kalman filter data assimilation system for the martian atmosphere: Implementation and simulation experiments

Icarus ◽  
2010 ◽  
Vol 209 (2) ◽  
pp. 470-481 ◽  
Author(s):  
Matthew J. Hoffman ◽  
Steven J. Greybush ◽  
R. John Wilson ◽  
Gyorgyi Gyarmati ◽  
Ross N. Hoffman ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Martian Atmosphere ◽  
Simulation Experiments ◽  
Data Assimilation System ◽  
Assimilation System

scholarly journals Preliminary Test of a Data Assimilation System with a Regional High-Resolution Atmosphere–Ocean Coupled Model Based on an Ensemble Kalman Filter

2017 ◽  
Vol 145 (2) ◽  
pp. 565-581 ◽  
Author(s):  
Masaru Kunii ◽  
Kosuke Ito ◽  
Akiyoshi Wada
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Coupled Model ◽  
Sea Surface ◽  
Intensity Change ◽  
Near Surface ◽  
Data Assimilation System ◽  
Atmospheric Variables ◽  
Assimilation System

An ensemble Kalman filter (EnKF) that uses a regional mesoscale atmosphere–ocean coupled model was preliminarily examined to provide realistic sea surface temperature (SST) estimates and to represent the uncertainties of SST in ensemble data assimilation strategies. The system was evaluated through data assimilation cycle experiments over a one-month period from July to August 2014, during which time a tropical cyclone (TC) as well as severe rainfall events occurred. The results showed that the data assimilation cycle with the coupled model reproduced SST distributions realistically even without assimilating SST and sea surface salinity observations, and atmospheric variables provided to ocean models can, therefore, control oceanic variables physically to some extent. The forecast error covariance calculated in the EnKF with the coupled model showed dependency on oceanic vertical mixing for near-surface atmospheric variables due to the difference of variability between the atmosphere and the ocean as well as the influence of SST variations on the atmospheric boundary layer. The EnKF with the coupled model reproduced the intensity change of Typhoon Halong (2014) during the mature phase more realistically than with an uncoupled atmosphere model, although there remained a degradation of the SST estimate, particularly around the Kuroshio region. This suggests that an atmosphere–ocean coupled data assimilation system should be developed that is able to physically control both atmospheric and oceanic variables.

scholarly journals Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part I: Design of the Assimilation System

2022 ◽  
Vol 14 (2) ◽  
pp. 371
Author(s):  
Sina Voshtani ◽  
Richard Ménard ◽  
Thomas W. Walker ◽  
Amir Hakami
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Error Variance ◽  
Companion Paper ◽  
Advective Flow ◽  
Vertical Error ◽  
Analysis Error ◽  
Data Assimilation System ◽  
Assimilation System

We present a parametric Kalman filter data assimilation system using GOSAT methane observations within the hemispheric CMAQ model. The assimilation system produces forecasts and analyses of concentrations and explicitly computes its evolving error variance while remaining computationally competitive with other data assimilation schemes such as 4-dimensional variational (4D-Var) and ensemble Kalman filter (EnKF). The error variance in this system is advected using the native advection scheme of the CMAQ model and updated at each analysis while the error correlations are kept fixed. We discuss extensions to the CMAQ model to include methane transport and emissions (both anthropogenic and natural) and perform a bias correction for the GOSAT observations. The results using synthetic observations show that the analysis error and analysis increments follow the advective flow while conserving the information content (i.e., total variance). We also demonstrate that the vertical error correlation contributes to the inference of variables down to the surface. In a companion paper, we use this assimilation system to obtain optimal assimilation of GOSAT observations.

scholarly journals Development of an ensemble Kalman filter data assimilation system for the Venusian atmosphere

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Norihiko Sugimoto ◽  
Akira Yamazaki ◽  
Toru Kouyama ◽  
Hiroki Kashimura ◽  
Takeshi Enomoto ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Venusian Atmosphere ◽  
Data Assimilation System ◽  
Assimilation System
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