J16.1
Cycling experiment results for a GSI regional hybrid ETKF data assimilation scheme for the WRF model

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Thursday, 27 January 2011: 11:00 AM
Cycling experiment results for a GSI regional hybrid ETKF data assimilation scheme for the WRF model
2B (Washington State Convention Center)
Arthur P. Mizzi, NCAR, Boulder, CO; and Z. Liu and X. Y. Huang
Manuscript (2.2 MB)

I. INTRODUCTION

The National Center for Atmospheric Research (NCAR) Mesoscale and Microscale Meteorology Division Data Assimilation Group (MMM/DA) has been collaborating with the United States Air Force Weather Agency (AFWA) and the National Oceanic and Atmospheric Administration (NOAA) National Center for Environment Prediction (NCEP) to develop a regional hybrid version of NCEP's Gridpoint Statistical Interpolation (GSI) data assimilation system. NCEP in collaboration with the University of Oklahoma developed a GSI global hybrid assimilation system. MMM/DA modified that global system for regional application using the Advanced Research Weather Research and Forecast modeling system (WRF ARW). This paper presents results from that work.

II. OVERVIEW OF THE GSI REGIONAL HYBRID SYSTEM

The GSI Regional Hybrid follows the scheme proposed by Hamill and Snyder (2000) as implemented by Wang et al. (2008). In summary, given an initial analysis, a perturbation generation strategy such as perturbed observations, singular vectors, or an ETKF is used to generate an ensemble of initial analyses. That ensemble is used to generate an ensemble of initial forecasts, which are used to begin the assimilation cycling experiment. Cycling proceeds as follows: (i) compute the ensemble mean and variance for the ensemble of forecasts, (ii) apply the GSI Regional Hybrid to update the ensemble mean analyses, (iii) use a modified version of the ETKF described by Wang et al., (2007) to update the ensemble perturbations, (iv) combine the updated mean and perturbations to obtain an ensemble of updated analyses/initial conditions, (v) update the boundary conditions, (vi) generate an ensemble of forecasts to begin the next cycle, and (vii) repeat steps (i) through (vii) for the duration of the cycling experiment.

III. SINGLE OBSERVATIONS EXPERIMENTS

In this section, we present results from single observation experiments with the GSI Regional Hybrid using WRF ARW. The domain of interest is the western Pacific during the passage of Typhoon Morakot (August 2, 2009 to August 11, 2009). We examine the sensitivity of single observations results to variations in: (i) the weighting between the variational and ensemble components of the background error, (ii) the magnitude of the hybrid horizontal localization length scale, (iii) the magnitude of the hybrid vertical localization length scale, and (iv) the number of ensemble members. We present results for a single temperature observation (Case 1) and a single wind observation (Case 2) placed near Typhoon Morakot on August 6, 2009. Our results show that the single observation increments respond properly to variations in the aforementioned parameters.

IV. COMPARISON OF THE GSI ENSEMBLE HYBRID/ETKF, GSI ENSEMBLE NON-HYBRID/ETKF AND GSI SYSTEMS

In this section, we present results from cycling experiments comparing results from GSI Ensemble Hybrid/ETKF, GSI Ensemble Non-Hybrid/ETKF, and GSI Non-Hybrid Non-Ensemble. The domain of interest is the western Atlantic during the passage of Hurricane Dean (August 15, 2007 to August 19, 2007). We present graphics comparing: (i) the horizontal and vertical characteristics of the analysis increments from the different systems, (ii) temporal characteristics of diagnostic parameters such as the ensemble spread, the ETKF inflation factor, and the convergence statistics, and (iii) verification scores. Our preliminary results suggest that hybrid increments display greater flow dependence than the non-hybrid and non-ensemble systems.

V. REFERENCES

Hamill, T. M., and C. Snyder, 2000: A hybrid ensemble Kalman filter-3D variational analysis scheme. Mon. Wea. Rev., 128, 2905-2919.

Wang, X. and C. H. Bishop, 2003: A comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes. J. Atmos. Sci., 60, 1140-1158.

Wang, X., T. M. Hamill J. S. Whitaker, and C. H. Bishop, 2007: A comparison of hybrid ensemble transform Kalman filter-OI and ensemble square-root filter analysis schemes. Mon. Wea. Rev., 135, 1055-1076.

Wang, X., D. M. Barker, C. Snyder, and T. M. Hamill, 2008: A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part I: Observing system simulation experiment. Mon. Wea. Rev., 136, 5116-5131.