Estimating of the Global Ionosphere Maps Using Hybrid Data Assimilation Method and Their Background Influence Analysis

2020 ◽  
Vol 125 (8) ◽  
Author(s):  
Sicheng Wang ◽  
Sixun Huang ◽  
Hanxian Fang
Keyword(s):  
Data Assimilation ◽  
Influence Analysis ◽  
Hybrid Data Assimilation ◽  
Hybrid Data

Multi-resolution Hybrid Data Assimilation Core on a Cubed-sphere Grid (HybDA)

2018 ◽  
Vol 54 (S1) ◽  
pp. 337-350 ◽  
Author(s):  
Hyo-Jong Song ◽  
Ji-Hyun Ha ◽  
In-Hyuk Kwon ◽  
Junghan Kim ◽  
Jihye Kwun
Keyword(s):  
Data Assimilation ◽  
Hybrid Data Assimilation ◽  
Hybrid Data ◽  
Cubed Sphere

scholarly journals Impact of 3D Var GSI-ENKF hybrid data assimilation system

2016 ◽  
Vol 125 (8) ◽  
pp. 1509-1521 ◽  
Author(s):  
V S Prasad ◽  
C J Johny ◽  
Jagdeep Singh Sodhi
Keyword(s):  
Data Assimilation ◽  
Hybrid Data Assimilation ◽  
Hybrid Data ◽  
Data Assimilation System ◽  
Assimilation System

scholarly journals EnKF and Hybrid Gain Ensemble Data Assimilation. Part II: EnKF and Hybrid Gain Results

2015 ◽  
Vol 143 (12) ◽  
pp. 4865-4882 ◽  
Author(s):  
Massimo Bonavita ◽  
Mats Hamrud ◽  
Lars Isaksen
Keyword(s):  
Data Assimilation ◽  
Hybrid Data Assimilation ◽  
Ensemble Data Assimilation ◽  
System A ◽  
Ensemble Data ◽  
Study Results ◽  
Numerical Experimentation ◽  
Component Systems ◽  
Hybrid Data ◽  
Data Assimilation System

Abstract The desire to do detailed comparisons between variational and more scalable ensemble-based data assimilation systems in a semioperational environment has led to the development of a state-of-the-art EnKF system at ECMWF, which has been described in Part I of this two-part study. In this part the performance of the EnKF system is evaluated compared to a 4DVar of similar resolution. It is found that there is not a major difference between the forecast skill of the two systems. However, similarly to the operational hybrid 4DVar–EDA, a hybrid EnKF–variational system [which we refer to as the hybrid gain ensemble data assimilation (HG-EnDA)] is capable of significantly outperforming both component systems. The HG-EnDA has been implemented with relatively little effort following Penny’s recent study. Results of numerical experimentation comparing the HG-EnDA with the hybrid 4DVar–EDA used operationally at ECMWF are presented, together with diagnostic results, which help characterize the behavior of the proposed ensemble data assimilation system. A discussion of these results in the context of hybrid data assimilation in global NWP is also provided.

scholarly journals Application of the WRF Hybrid ETKF–3DVAR Data Assimilation System for Hurricane Track Forecasts

2011 ◽  
Vol 26 (6) ◽  
pp. 868-884 ◽  
Author(s):  
Xuguang Wang
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Weather Research And Forecasting ◽  
Hybrid Data Assimilation ◽  
Error Covariance ◽  
Hybrid Data ◽  
Hurricane Track ◽  
Ensemble Transform Kalman Filter ◽  
Mean Square Errors ◽  
Specific Error

Abstract A hybrid ensemble transform Kalman filter (ETKF)–three-dimensional variational data assimilation (3DVAR) system developed for the Weather Research and Forecasting Model (WRF) was studied for the forecasts of the tracks of two major hurricanes, Ike and Gustav, in 2008 over the Gulf of Mexico. The impacts of the flow-dependent ensemble covariance generated by the ETKF were revealed by comparing the forecasts, analyses, and analysis increments generated by the hybrid data assimilation method with those generated by the 3DVAR that used the static background covariance. The root-mean-square errors of the track forecasts by the hybrid data assimilation (DA) method were smaller than those by the 3DVAR for both Ike and Gustav. Experiments showed that such improvements were due to the use of the flow-dependent covariance provided by the ETKF ensemble in the hybrid DA system. Detailed diagnostics further revealed that the increments produced by the hybrid and the 3DVAR were different for both the analyses of the hurricane itself and its environment. In particular, it was found that the hybrid, using the flow-dependent covariance that gave the hurricane-specific error covariance estimates, was able to systematically adjust the position of the hurricane during the assimilation whereas the 3DVAR was not. The study served as a pilot study to explore and understand the potential of the hybrid method for hurricane data assimilation and forecasts. Caution needs to be taken to extrapolate the results to operational forecast settings.

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