The evolution of the ECMWF hybrid data assimilation system

2015 ◽  
Vol 142 (694) ◽  
pp. 287-303 ◽  
Author(s):  
Massimo Bonavita ◽  
Elias Hólm ◽  
Lars Isaksen ◽  
Mike Fisher
Keyword(s):  
Data Assimilation ◽  
Hybrid Data Assimilation ◽  
Hybrid Data ◽  
Data Assimilation System ◽  
Assimilation System

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 Impact of Model Bias Correction on a Hybrid Data Assimilation System

2020 ◽  
Vol 34 (2) ◽  
pp. 400-412
Author(s):  
Yu Xia ◽  
Jing Chen ◽  
Xiefei Zhi ◽  
Lianglyu Chen ◽  
Yang Zhao ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Bias Correction ◽  
Model Bias ◽  
Hybrid Data Assimilation ◽  
Hybrid Data ◽  
Data Assimilation System ◽  
Assimilation System

Assimilating AMSU-A Radiances in the TC Core Area with NOAA Operational HWRF (2011) and a Hybrid Data Assimilation System: Danielle (2010)

2013 ◽  
Vol 141 (11) ◽  
pp. 3889-3907 ◽  
Author(s):  
Man Zhang ◽  
Milija Zupanski ◽  
Min-Jeong Kim ◽  
John A. Knaff
Keyword(s):  
Data Assimilation ◽  
Core Area ◽  
Transfer Model ◽  
Hybrid Data Assimilation ◽  
Radiance Assimilation ◽  
Hybrid Data ◽  
The Impact ◽  
Mean Square Errors ◽  
Data Assimilation System ◽  
Assimilation System

Abstract A regional hybrid variational–ensemble data assimilation system (HVEDAS), the maximum likelihood ensemble filter (MLEF), is applied to the 2011 version of the NOAA operational Hurricane Weather Research and Forecasting (HWRF) model to evaluate the impact of direct assimilation of cloud-affected Advanced Microwave Sounding Unit-A (AMSU-A) radiances in tropical cyclone (TC) core areas. The forward components of both the gridpoint statistical interpolation (GSI) analysis system and the Community Radiative Transfer Model (CRTM) are utilized to process and simulate satellite radiances. The central strategies to allow the use of cloud-affected radiances are (i) to augment the control variables to include clouds and (ii) to add the model cloud representations in the observation forward models to simulate the microwave radiances. The cloudy AMSU-A radiance assimilation in Hurricane Danielle's (2010) core area has produced encouraging results with respect to the operational cloud-cleared radiance preprocessing procedures used in this study. Through the use of the HVEDAS, ensemble covariance statistics for a pseudo-AMSU-A observation in Danielle's core area show physically meaningful error covariances and statistical couplings with hydrometeor variables (i.e., the total-column condensate in Ferrier microphysics). The cloudy radiance assimilation in the TC core region (i.e., ASR experiment) consistently reduced the root-mean-square errors of the background departures, and also generally improved the forecasts of Danielle's intensity as well as the quantitative cloud analysis and prediction. It is also indicated that an entropy-based information content quantification process provides a useful metric for evaluating the utility of satellite observations in hybrid data assimilation.

Development of an Operational Hybrid Data Assimilation System at KIAPS

2018 ◽  
Vol 54 (S1) ◽  
pp. 319-335 ◽  
Author(s):  
In-Hyuk Kwon ◽  
Hyo-Jong Song ◽  
Ji-Hyun Ha ◽  
Hyoung-Wook Chun ◽  
Jeon-Ho Kang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Hybrid Data Assimilation ◽  
Hybrid Data ◽  
Data Assimilation System ◽  
Assimilation System

scholarly journals Evaluation of the Four-Dimensional Ensemble-Variational Hybrid Data Assimilation with Self-Consistent Regional Background Error Covariance for Improved Hurricane Intensity Forecasts

Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 1007
Author(s):  
Shixuan Zhang ◽  
Zhaoxia Pu
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Forecast Errors ◽  
Hurricane Intensity ◽  
Background Error ◽  
Hybrid Data Assimilation ◽  
Self Consistent ◽  
Hybrid Data ◽  
Data Assimilation System ◽  
Assimilation System

The feasibility of a hurricane initialization framework based on the Gridpoint Statistical Interpolation (GSI)-based four-dimensional ensemble-variational (GSI-4DEnVar) hybrid data assimilation system for the Hurricane Weather Research and Forecasting model (HWRF) model is evaluated in this study. The system considers the temporal evolution of error covariances via the use of four-dimensional ensemble perturbations that are provided by high-resolution, self-consistent HWRF ensemble forecasts. It is different from the configuration of the GSI-based three-dimensional ensemble-variational (GSI-3DEnVar) hybrid data assimilation system, similar to that used in the operational HWRF, which employs background error covariances provided by coarser-resolution global ensembles from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) ensemble Kalman filtering data assimilation system. In addition, our proposed initialization framework discards the empirical intensity correction in the vortex initialization package that is employed by the GSI-3DEnVar initialization framework in operational HWRF. Data assimilation and numerical simulation experiments for Hurricanes Joaquin (2015), Patricia (2015), and Matthew (2016) are conducted during their intensity changes. The impacts of two initialization frameworks on the HWRF analyses and forecasts are compared. It is found that GSI-4DEnVar leads to a reduction in track, minimum sea level pressure (MSLP), and maximum surface wind (MSW) forecast errors in all of the HWRF simulations, compared with the GSI-3DEnVar initialization framework. With assimilating high-resolution observations within the hurricane inner-core region, GSI-4DEnVar can produce the initial hurricane intensity reasonably well without the empirical vortex intensity correction. Further diagnoses with Hurricane Joaquin indicate that GSI-4DEnVar can significantly alleviate the imbalances in the initial conditions and enhance the performance of the data assimilation and subsequent hurricane intensity and precipitation forecasts.

scholarly journals A Comparison of the Impacts of Radiosonde and AMSU Radiance Observations in GSI Based 3DEnsVar and 3DVar Data Assimilation Systems for NCEP GFS

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Govindan Kutty ◽  
Xuguang Wang
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Winter Season ◽  
Data Quality Control ◽  
Hybrid Data Assimilation ◽  
Environmental Prediction ◽  
Hemisphere Winter ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

The impact of observations can be dependent on many factors in a data assimilation (DA) system including data quality control, preprocessing, skill of the model, and the DA algorithm. The present study focuses on comparing the impacts of observations assimilated by two different DA algorithms. A three-dimensional ensemble-variational (3DEnsVar) hybrid data assimilation system was recently developed based on the Gridpoint Statistical Interpolation (GSI) data assimilation system and was implemented operationally for the National Center for Environmental Prediction (NCEP) Global Forecast System (GFS). One question to address is, how the impacts of observations on GFS forecasts differ when assimilated by the traditional GSI-three dimensional variational (3DVar) and the new 3DEnsVar. Experiments were conducted over a 6-week period during Northern Hemisphere winter season at a reduced resolution. For both the control and data denial experiments, the forecasts produced by 3DEnsVar were more accurate than GSI3DVar experiments. The results suggested that the observations were better and more effectively exploited to increment the background forecast in 3DEnsVar. On the other hand, in GSI3DVar, where the observation will be making mostly local, isotropic increments without proper flow dependent extrapolation is more sensitive to the number and types observations assimilated.

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 Evaluation of the Four-dimensional Ensemble-Variational Hybrid Data Assimilation with Self-consistent Regional Background Error Covariance for Improved Hurricane Intensity Forecasts

2020 ◽  
Author(s):  
Shixuan Zhang ◽  
Zhaoxia Pu
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Forecast Errors ◽  
Hurricane Intensity ◽  
Ensemble Forecasts ◽  
Background Error ◽  
Hybrid Data Assimilation ◽  
Self Consistent ◽  
Data Assimilation System ◽  
Assimilation System

The feasibility of a hurricane initialization framework based on the GSI-4DEnVar data assimilation system for the HWRF model is evaluated in this study. The system considers the temporal evolution of error covariances via the use of four-dimensional ensemble perturbations that are provided by high-resolution, self-consistent HWRF ensemble forecasts. It is different from the configuration of the GSI-3DEnVar data assimilation system, similar to that used in the operational HWRF, which employs background error covariances provided by coarser-resolution global ensembles from the NCEP GFS ensemble Kalman filtering data assimilation system. Data assimilation and numerical simulation experiments for Hurricanes Joaquin (2015), Patricia (2015), and Matthew (2016) are conducted during their intensity changes. The impacts of two initialization frameworks on the HWRF analyses and forecasts are compared. It is found that GSI-4DEnVar leads to a reduction in track, MSLP, and MSW forecast errors in all of the HWRF simulations, compared with the GSI-3DEnVar initialization framework. Further diagnoses with Hurricane Joaquin indicate that GSI-4DEnVar can significantly alleviate the imbalances in the initial conditions and enhance the performance of the data assimilation and subsequent hurricane intensity and precipitation forecasts.

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