1. Introduction1 Studies of Tropical Cyclone (TC)

6A.1 INITIALIZING THE WRF MODEL WITH TROPICAL CYCLONE VITAL RECORD FOR TYPHOON FORECASTS BASED ON THE ENSEMBLE KALMAN FILTER ALGORITHM Tien Duc Du(1), Thanh Ngo-Duc(2), and Chanh Kieu(3)* (1)National Center for Hydro-Meteorological Forecasting, 4 Dang Thai Than Street, Hoan Kiem, Ha Noi, Vietnam (2) Department of Space and Aeronautics, University of Science and Technology of Hanoi, Vietnam (3)Department of Geological Sciences, Indiana University, Bloomington, IN 47405, USA 1 will be presented. In addition to the real-time 1. Introduction TCvital information, the atmospheric motion vector (AMV) data provided by the Cooperative Institute Studies of tropical cyclone (TC) for Meteorological Satellite Studies (CIMSS) of the initialization for real-time forecasts have shown University of Wisconsin will be also assimilated to that incipient inner-core structure of TCs can have improve the large-scale environment that controls substantial impacts on TC initial adjustment and the TC steering flows, which could have significant subsequent track and intensity development (see, effects on the TC track forecasts (Velden et. al. e.g., Kurihara et al. 1993; Wang 1998; Hendricks 1992; Kieu et al. 2012). et al. 2011, 2013; Nguyen and Chen 2011). Because of such sensitivity of TC development to 2. Experiment design the storm initial structure, enormous effort has been carried out to improve initial representation a. Model description and LETKF algorithm of TC inner-core structure in TC modeling In this study, the Weather Research and applications. General approaches to improve the Forecasting model (WRF-ARW, version 3.2, TC initial inner-core structure can be roughly Skamarock et al. 2008) was used with lateral divided into three categories including i) TC boundary conditions updating every 6 hours from bogussing methods that replace an initially weak the National Centers for Environmental Prediction cyclonic perturbation by a bogus vortex with a (NCEP) Global Forecasting System (GFS) real- prescribed intensity, ii) dynamical vortex time forecasts at resolution of 0.50 x 0.50. For the initialization methods that use a numerical model purpose of multiple-physics ensemble design, a to spin-up an initial vortex until the intensity of the set of physical parameterizations used in all model vortex matches with observed intensity, and ensemble experiments, more information see Kieu iii) assimilation methods that directly ingest TC et al. (2012). observations to enhance the TC representation in model initial condition. The LETKF algorithm (Hunt et al 2007) Issues of complicated bogussing process was used with a fixed multiplicative inflation factor and the related artificial numerical effects in the of 1.1. To reduce spurious cross correlations in the TC initialization have been effectively addressed LETKF algorithm, homogeneous covariance with recent advances in data assimilation schemes localization with a horizontal scale of 700 km is (see, e.g., Zhang et al. 2011). Given demonstrated applied in all experiments such that far-field capability of a variant of the Ensemble Kalman observations outside the TC main circulation will filter algorithm, the so-called Local Ensemble have minimum impacts on the TC inner region. Transform Kalman Fitter (LETKF), in context of TC forecasts (see, e.g., Miyoshi and Kunii 2012; Kieu b. TCvital-based synthetic profile et al. 2012, 2014; Cecelski et al. 2014), this study This TCvital-based synthetic wind profile attempts to examine the effectiveness of the used in this study was based on a tangential wind LETKF algorithm in assimilating the TCvital profile proposed by Kieu and Zhang (2009), which information into the WRF-ARW model for real-time is given by the following tangential wind structure: TC forecasts in the north Western Pacific.basin Specifically, an innovative approach based on the 푉 푟, 휎 = real-time TCvital-synthetic observation that does 푟 휋 1−훿 휋 훿 1 푟 푏 푉푚 sin 휎 cos 휎 exp⁡{ [1 − ]} (1) not require the storm relocation or a bogus vortex 푟푚 2 2 푏 푟푚 where Vm is the VMAX, rm is the RMW, r is the * Corresponding author: Chanh Kieu, Atmospheric Program, radius from the vortex center, b is a parameter that GY428A Geological Building, Department of Geological determines the horizontal shape of the wind Sciences, Indiana University, Bloomington, IN 47405. Tel: profile, which is set equal to 0.7 by default, and 812-856-5704. Email: [email protected]. 1 훿 ∈ [0, 1] is a free parameter determining the capture a consistently deep wind structure at all height of the VMAX relative to the surface (i.e., levels with strong cyclonic wind components 훿 = 0 corresponds to the maximum wind at the coming from two main reasons. First, the storm surface, whereas 훿 = 1 corresponds to the intensity in the GFS background is generally much maximum wind at the middle level, see Kieu et al. weaker than the observed intensity when TCs are 2014b). With the tangential wind distribution as sufficiently strong (cf. Figure 1). Secondly, the given by Eq. (1), the three-dimensional distribution large synthetic increments are due to the fact that of geopotential is then calculated by iteratively the GFS storm centers are not the same as those solving the nonlinear balance equation of the form reported in TCvitals. Given the above distribution (see Holton 2004). of the synthetic wind increments with a dipole structure for both the zonal and meridional winds 휕휓 휕휓 ∇2Φ = ∇ ⋅ 푓∇휓 − 퐽 , (2) around the vortex center as seen in Figs. 2 -4, it is 휕푥 휕푦 of importance to notice that the LETKF analysis increments could exhibit similar patterns and In the final step, the balanced temperature profile magnitudes at all levels. Specifically, consistent is obtained by integrating the hydrostatic equation enhancement of the TC inner-core circulation with from the top down, based on the balanced two peaks of analysis wind increments > 15 m s-1 geopotential distribution as obtained from Eq. (2). (negative to the north and positive to the south of c. Observation data and TC studied cases the vortex center) is observed in all three cases, Three TC cases in 2013 over the north which indicate the influence of the synthetic Western Pacific basin are chosen (Super Typhoon increments in correcting both the storm intensity Usagi on 18-19 September 2013, Typhoon Krosa and the storm initial location. Of further interest is from on 30-31 September 2013, and Super that the analysis increments could capture strong Typhoon Nari on 10-11 October 2013, see Figure asymmetry of the zonal wind due to existence of 1). The Tropical Cyclone Vital Statistics Records strong easterly mean flows in the WPAC basin (cf. provided by US Joint Typhoon Warning Center Figs 2a, d, g). In this regard, the TCvital-based (JTWC) are used to provide all necessary inputs synthetic profile data appears to not only enhance for constructing synthetic bogus structures and the the TC intensity but also help re-locate the GFS AMV dataset from CIMSS-University of Wisconsin. initial storm center as expected. d. Ensemble experiments The centers of the wind dipoles in all Three sets of ensemble experiments are analysis increments do not completely coincide carried out to examine effects of the TCvital-based with the synthetic increments at higher levels (see, synthetic observation, the CIMSS-AMV data, as e.g., Fig. 2h or 3h). This is due to the fact that real well as the effectiveness of the LETKF algorithm. storms are often tilted with height that the TCvital- The control experiment (CTRL) uses 21 members based synthetic profiles given by Eq. (1) cannot with different initials by adding noise to capture. Such influence of vortex tilting on GFS/NCEP analysis. The second experiment assimilating a storm vertical structure and the DAMV also uses 21 members with different extent to which the TCvital-based synthetic physical options, the same initial conditions from information can be assimilated in the GFS analysis GFS/NCEP analysis and having CIMSS-AMV is seen most clearly in the vertical cross sections assimilation with LETKF. The last experiment through the storm center (Figure 5). In addition to DABV uses 21 members with different physical the vortex tilting issue, the storm size could play options, having the same initial conditions of CTRL some role in assimilating the synthetic information. and blending CIMSS-AMV and synthetic bogus Figs 3 and 5 show the consistent of analysis vortex observation assimilation with LETKF. The increments and synthetic increments for the cases same approach as in Zhang et al. (2006) for of STYs Usagi and Krosa, whereas the case of initialize cold-start background ensembles was STY Nari tends to have a larger discrepancy used. between the synthetic and the analysis increments due to Nari’s smaller inner-core size (~ 30 × 30 instead of 50 × 50 as for the cases of Usagi and 3. Results Krosa). Another significant benefit of assimilation of TCvital-based synthetic profiles is that TCvitals a. Impact of the TCvital-based synthetic data could allow for spread of the surface information to Figures 2 and 3 show that the TCvital- higher levels (cf. Figure 4), which is expected if the based synthetic increments (shaded) indeed TCs are sufficiently strong that they possess a 2 coherent structure thorough the troposphere as influence of the interference of these data sources implied from the surface observed information. is thus minimal. Of course, there are some disadvantages of the synthetic data in effectively b. Impact of the AMV data correcting storm initial locations, or uncertainties in In Figure 5, consistent with characteristics the storm vertical structures that the TCvital-based of the AMV data, much higher density of the AMV synthetic profiles may not fully capture. However, data points is found at upper levels above 300 the best evaluation of the effectiveness of our hPa, whereas data points at lower levels are blending approach could be justified from mainly in the peripheral area of the storm central examination of the track and intensity forecasts region.

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