Editorial Type:
Article
Article Type:
Research Article

Ensemble Transform with 3D Rescaling Initialization Method

Juhui Ma Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, and State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China

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Yuejian Zhu NOAA/NCEP/Environmental Modeling Center, College Park, Maryland

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Dingchen Hou NOAA/NCEP/Environmental Modeling Center, College Park, Maryland

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Xiaqiong Zhou I.M. Systems Group, Inc., and NOAA/NCEP/Environmental Modeling Center, College Park, Maryland

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Malaquias Peña I.M. Systems Group, Inc., and NOAA/NCEP/Environmental Modeling Center, College Park, Maryland

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Print Publication:
01 Nov 2014
DOI:
https://doi.org/10.1175/MWR-D-13-00367.1
Page(s):
4053–4073
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Abstract

The ensemble transform with rescaling (ETR) method has been used to produce fast-growing components of analysis error in the NCEP Global Ensemble Forecast System (GEFS). The rescaling mask contained in the ETR method constrains the amplitude of perturbations to reflect regional variations of analysis error. However, because of a lack of suitable three-dimensional (3D) analysis error estimation, in the operational GEFS the mask is based on the estimated analysis error at 500 hPa and is not flow dependent but changes monthly. With the availability of an ensemble-based data assimilation system at NCEP, a 3D mask can be computed. This study generates initial perturbations by the ensemble transform with 3D rescaling (ET_3DR) and compares the performance with the ETR. Meanwhile, the ET_3DR is also applied within the ensemble Kalman filter (EnKF) method (hereafter EnKF_3DR).

Results from a set of experiments indicate that the 3D mask suppresses perturbations less in unstable regions. Relative to the ETR, the large amplitudes of the ET_3DR initial perturbations at 500 hPa better reflect areas of baroclinic instability over the extratropics and deep convection over the tropics. Furthermore, the maxima of the vertical distribution for the ET_3DR initial perturbations correspond to the heights of the subtropical westerly and tropical easterly jet regions. Such perturbations produce faster spread growths. Results with EnKF_3DR also show benefits from an orthonormalization by the ensemble transform algorithm and amplitude constraint by the 3D mask rescaling. Thus, the EnKF_3DR forecasts outperform the EnKF.

Corresponding author address: Juhui Ma, Nanjing University of Information Science and Technology, 219 Ningliu Road, Nanjing, Jiangsu 210044, China. E-mail: juhui.ma@gmail.com

Abstract

The ensemble transform with rescaling (ETR) method has been used to produce fast-growing components of analysis error in the NCEP Global Ensemble Forecast System (GEFS). The rescaling mask contained in the ETR method constrains the amplitude of perturbations to reflect regional variations of analysis error. However, because of a lack of suitable three-dimensional (3D) analysis error estimation, in the operational GEFS the mask is based on the estimated analysis error at 500 hPa and is not flow dependent but changes monthly. With the availability of an ensemble-based data assimilation system at NCEP, a 3D mask can be computed. This study generates initial perturbations by the ensemble transform with 3D rescaling (ET_3DR) and compares the performance with the ETR. Meanwhile, the ET_3DR is also applied within the ensemble Kalman filter (EnKF) method (hereafter EnKF_3DR).

Results from a set of experiments indicate that the 3D mask suppresses perturbations less in unstable regions. Relative to the ETR, the large amplitudes of the ET_3DR initial perturbations at 500 hPa better reflect areas of baroclinic instability over the extratropics and deep convection over the tropics. Furthermore, the maxima of the vertical distribution for the ET_3DR initial perturbations correspond to the heights of the subtropical westerly and tropical easterly jet regions. Such perturbations produce faster spread growths. Results with EnKF_3DR also show benefits from an orthonormalization by the ensemble transform algorithm and amplitude constraint by the 3D mask rescaling. Thus, the EnKF_3DR forecasts outperform the EnKF.

Corresponding author address: Juhui Ma, Nanjing University of Information Science and Technology, 219 Ningliu Road, Nanjing, Jiangsu 210044, China. E-mail: juhui.ma@gmail.com
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