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 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.

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.

scholarly journals Impact of Assimilating Aircraft Reconnaissance Observations on Tropical Cyclone Initialization and Prediction Using Operational HWRF and GSI Ensemble–Variational Hybrid Data Assimilation

2018 ◽  
Vol 146 (12) ◽  
pp. 4155-4177 ◽  
Author(s):  
Mingjing Tong ◽  
Jason A. Sippel ◽  
Vijay Tallapragada ◽  
Emily Liu ◽  
Chanh Kieu ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
Inner Core ◽  
Positive Impact ◽  
Background Error ◽  
Hybrid Data Assimilation ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System ◽  
Tropical Cyclone Initialization

Abstract This study evaluates the impact of assimilating high-resolution, inner-core reconnaissance observations on tropical cyclone initialization and prediction in the 2013 version of the operational Hurricane Weather Research and Forecasting (HWRF) Model. The 2013 HWRF data assimilation system is a GSI-based hybrid ensemble–variational system that, in this study, uses the Global Data Assimilation System ensemble to estimate flow-dependent background error covariance. Assimilation of inner-core observations improves track forecasts and reduces intensity error after 18–24 h. The positive impact on the intensity forecast is mainly found in weak storms, where inner-core assimilation produces more accurate tropical cyclone structures and reduces positive intensity bias. Despite such positive benefits, there is degradation in short-term intensity forecasts that is attributable to spindown of strong storms, which has also been seen in other studies. There are several reasons for the degradation of intense storms. First, a newly discovered interaction between model biases and the HWRF vortex initialization procedure causes the first-guess wind speed aloft to be too strong in the inner core. The problem worsens for the strongest storms, leading to a poor first-guess fit to observations. Though assimilation of reconnaissance observations results in analyses that better fit the observations, it also causes a negative intensity bias at the surface. In addition, the covariance provided by the NCEP global model is inaccurate for assimilating inner-core observations, and model physics biases result in a mismatch between simulated and observed structure. The model ultimately cannot maintain the analysis structure during the forecast, leading to spindown.

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

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
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