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Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation

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Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation OCTOBER 2008 MENGANDZHANG 3671 Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part IV: Comparison with 3DVAR in a Month-Long Experiment ZHIYONG MENG* AND FUQING ZHANGϩ Department of Atmospheric Sciences, Texas A&M University, College Station, Texas (Manuscript received 30 May 2007, in final form 29 January 2008) ABSTRACT In previous works in this series study, an ensemble Kalman filter (EnKF) was demonstrated to be promising for mesoscale and regional-scale data assimilation in increasingly realistic environments. Parts I and II examined the performance of the EnKF by assimilating simulated observations under both perfect- and imperfect-model assumptions. Part III explored the application of the EnKF to a real-data case study in comparison to a three-dimensional variational data assimilation (3DVAR) method in the Weather Research and Forecasting (WRF) model. The current study extends the single-case real-data experiments over a period of 1 month to examine the long-term performance and comparison of both methods at the regional scales. It is found that the EnKF systematically outperforms 3DVAR for the 1-month period of interest in which both methods assimilate the same standard rawinsonde observations every 12 h over the central United States. Consistent with results from the real-data case study of Part III, the EnKF can benefit from using a multischeme ensemble that partially accounts for model errors in physical parameterizations. The benefit of using a multischeme ensemble (over a single-scheme ensemble) is more pronounced in the thermodynamic variables (including temperature and moisture) than in the wind fields. On average, the EnKF analyses lead to more accurate forecasts than the 3DVAR analyses when they are used to initialize 60 consecutive, deterministic 60-h forecast experiments for the month. Results also show that deterministic forecasts of up to 60 h initiated from the EnKF analyses consistently outperform the WRF forecasts initiated from the National Centers for Environmental Prediction final analysis field of the Global Forecast System. 1. Introduction mer et al. 2005; Hakim and Torn 2008; Fujita et al. 2007). The ensemble Kalman filter (EnKF; Evensen 1994), Application of the EnKF in real-world data assimi- which estimates the background error covariance with a lation with large-scale models has made significant short-term ensemble forecast, is drawing increasing at- progress. Houtekamer et al. (2005) implemented the tention in the data assimilation community (Evensen EnKF into a large-scale model that includes a standard 2003; Lorenc 2003; Hamill 2006; Zhang and Snyder operational set of physical parameterizations by assimi- 2007). Progress has lately been made toward imple- lating real observations from a fairly complete obser- menting ensemble-based data assimilation into weather vational network. The 6-h data assimilation cycles were prediction models with real observations (Dowell et al. conducted for about half a month, and model error was 2004; Whitaker et al. 2004, 2008; Barker 2005; Houteka- treated by additive random perturbations sampled from the background error statistics (BES) of a simplified version of their three-dimensional variational data as- * Current affiliation: Department of Atmospheric Sciences, similation (3DVAR) method. The performance of the School of Physics, Peking University, Beijing, China. ϩ Current affiliation: Department of Meteorology, The Penn- EnKF was found to be comparable to that of the sylvania State University, University Park, Pennsylvania. 3DVAR system. Whitaker et al. (2004) and Compo et al. (2006) demonstrated that the EnKF is well suited to historical reanalysis problems and outperforms Corresponding author address: Dr. Fuqing Zhang, Department of Meteorology, The Pennsylvania State University, 503 Walker 3DVAR. Whitaker et al. (2008) further implemented Bldg., University Park, PA 16802. the EnKF in a reduced-resolution version of the Global E-mail: [email protected] Forecast System (GFS) at the National Centers for En- DOI: 10.1175/2008MWR2270.1 © 2008 American Meteorological Society MWR2270 3672 MONTHLY WEATHER REVIEW VOLUME 136 vironmental Prediction (NCEP) with all of the real- time observations used by the NCEP operational data assimilation system except for satellite radiance. Their 40-day, 6-hourly data assimilation experiments show that the EnKF works better than the operational 3DVAR system configured in the same way. The 48-h forecast from the ensemble data assimilation system was as accurate as the 24-h forecast from the 3DVAR system. In previous parts of this study series, a mesoscale, ensemble-based data assimilation system has been shown to be promising in increasingly realistic environ- ments. First, the EnKF was used to simulate observa- tions both under a perfect-model assumption (Zhang et al. 2006, hereafter Part I) and in the presence of signif- FIG. 1. Map of model domain. The solid circles denote the icant model error (Meng and Zhang 2007, hereafter rawinsonde observations to be assimilated. The dashed box shows Part II). The EnKF was then used in a mesoscale con- where the verification is performed. vective vortex (MCV) case study of real-world data as- similation (Meng and Zhang 2008, hereinafter Part III). domain is extended from covering only the central As a natural extension of the case study involving real- United States in Part III (the dashed box in Fig. 1) to world data assimilation with the EnKF implemented in encompass most of the continental United States with ϫ the Weather Research and Forecasting (WRF) Model, 115 82 grid points and a grid spacing of 30 km (solid a month-long rawinsonde data assimilation experiment box in Fig. 1). Both model domains have 27 vertical is performed in this study to compare the behavior of layers, and the model top is set at 100 hPa. Except for the EnKF in a more general manner to WRF-3DVAR. the multischeme ensemble runs (refer to Table 2 of Part Model error is treated using the “multischeme” en- III), all forecasts use the Grell–Devenyi cumulus semble (see Parts II and III) and the relaxation method scheme (Grell and Devenyi 2002), WRF single-moment for covariance inflation [Zhang et al. 2004, their Eq. (WSM) six-class microphysics with graupel (Hong et al. (5)]. Different from the standard inflation method 2004), and the Yonsei State University (YSU) scheme (Anderson 2001) in which all points in the prior field (Noh et al. 2003) for planetary boundary layer pro- are inflated, this relaxation method only inflates the cesses. The NCEP FNL analyses are used to create the covariance at updated points via a weighted average initial and boundary conditions. between the prior perturbation and the posterior per- b. The EnKF and 3DVAR turbation. The rest of this work is presented as follows: The The EnKF and 3DVAR methods used in this study methodology is briefly introduced in section 2. The re- are the same as those in Part III (refer to sections 2b alism of the EnKF is examined in section 3. Section 4 and 2c of Part III for details). The EnKF uses an en- compares the performance of the EnKF with 3DVAR semble size of 40 and the covariance relaxation method and to the NCEP GFS final (FNL) analysis. The benefit [Zhang et al. 2004, their Eq. (5)] to inflate the analysis of using a multischeme ensemble is further demon- error covariance with a relaxation coefficient of 0.7 strated in section 5 via comparison to a single-scheme (which means 70% of the perturbation of the analysis experiment. Conclusions and discussions are presented ensemble comes directly from the prior), a value that in section 6. gives the best performance in the real-data case study of Part III. WRF-3DVAR (Barker et al. 2004) uses the 2. Methodology newly generated CV ϭ 5 (CV5) background error sta- tistics and the National Meteorological Center (NMC) a. The model method (Parrish and Derber 1992; Xiao and Sun 2007). The same version of the Advanced Research WRF This method uses statistics describing differences be- (ARW; Skamarock et al. 2005) with two domains and tween the sixty 24- and 12-h WRF forecasts valid at the one-way nesting is used in this study as in Part III. The same time, performed on the fine domain, and initial- coarse domain covers the contiguous United States ized twice daily (0000 and 1200 UTC) for the entire with 45 ϫ 64 grid points and a grid spacing of 90 km month of May 2003. The control variables are stream- (Fig. 1), which is the same as that in Part III. The inner function, velocity potential, unbalanced pressure, and OCTOBER 2008 MENGANDZHANG 3673 relative humidity (Xiao and Sun 2007). The CV5 option domain are assimilated every 12 h with locations de- is used for the control 3DVAR experiment because the noted as solid circles in Fig. 1. BES are based on the WRF forecast difference for the 2) ENSEMBLE INITIAL AND BOUNDARY preceding month, which is demonstrated to perform slightly better than using the default BES option (CV3) CONDITIONS both for the case study (Part III) and for the month- The initial reference analyses of both the coarse and long experiment (not shown). Both data assimilation inner domains are interpolated from the FNL analyses. methods are performed only in domain 2. The initial ensemble perturbations are generated with balanced perturbations randomly drawn from the 1) DATA TO BE ASSIMILATED WRF-3DVAR background uncertainty as described in For this month-long application, only standard ra- section 2b of Part III (refer also to Barker et al. 2004; winsonde observations available every 12 h are assimi- Barker 2005), which is also used in Houtekamer et al. lated. Part III showed that only marginal benefit might (2005). For the subsequent analysis and forecast cycles, be gained through assimilation of additional data from the initial and boundary conditions of the coarse do- surface and profiler observations.
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