Using TIGGE Data to Diagnose Initial Perturbations and Their Growth for Tropical Cyclone Ensemble Forecasts

Abstract

Ensemble initial perturbations around Typhoon Sinlaku (2008) produced by ECMWF, NCEP, and the Japan Meteorological Agency (JMA) ensembles are compared using The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) data, and the dynamical mechanisms of perturbation growth associated with the tropical cyclone (TC) motion are investigated for the ECMWF and NCEP ensembles. In the comparison, it is found that the vertical and horizontal distributions of initial perturbations as well as the amplitude are quite different among the three NWP centers before, during, and after the recurvature of Sinlaku. In addition, it turns out that those variations cause a difference in the TC motion not only at the initial time but also during the subsequent forecast period. The ECMWF ensemble exhibits relatively large perturbation growth, which results from 1) the baroclinic energy conversion in a vortex, 2) the baroclinic energy conversion associated with the midlatitude waves, and 3) the barotropic energy conversion in a vortex. Those features are less distinctive in the NCEP ensemble. A statistical verification shows that the ensemble spread of TC track predictions in NCEP (ECMWF) is larger than ECMWF (NCEP) for 1-(3-) day forecasts on average. It can be inferred that while the ECMWF ensemble starts from a relatively small amplitude of initial perturbations, the growth of the perturbations helps to amplify the ensemble spread of tracks. On the other hand, a relatively large amplitude of initial perturbations seems to play a role in producing the ensemble spread of tracks in the NCEP ensemble.

Impact of perturbation methods in the ECMWF ensemble prediction system on tropical cyclone forecasts

“…Most of the structures of the SVINI perturbations are located in the mid to lower troposphere in the outer region of the TC (Figure 2(b)). This is consistent with the findings of Yamaguchi and Majumdar (2010) for Typhoon Sinlaku (2008). In addition to the differences in the location of the maxima with respect to the TC core, the initial amplitude of the SVINI perturbations is in general much smaller than that of the EDA perturbations, as reported by Buizza et al (2008) for extratropical perturbation patterns.…”

“…The spatial distribution of the SVINI perturbations ( Figure 5(b)) is dominated by upshear-tilted structures Yamaguchi and Majumdar, 2010;Lang et al, 2012). The upshear tilt of the SVINI perturbations is in contrast to the downshear-tilted structures of the EDA perturbations (compare Figure 5 The EDA perturbations possess a significantly higher effective resolution than the T42 SV perturbations, because they are generated by subtracting two high-resolution 6 h forecasts (see sections 2.1 and 2.2).…”

“…This was also observed by Buizza et al (2008) for extratropical perturbations. The SVINI perturbations experience transient growth and grow because of baroclinic and barotropic processes (Yamaguchi and Majumdar, 2010;Lang et al, 2012). The displacement pattern of the EDA perturbations, on the other hand, can directly shift the TC relative to the background flow that steers the TC, and thereby result in a track deviation.…”

Medium- and Extended-Range Ensemble Weather Forecasting

“…A linear combination of these SVs, scaled to have amplitudes consistent with the analysis error, is added to the best-estimate analysis to make the starting conditions for each ensemble member. The SV method is used operationally at the European Centre for Medium-Range Weather Forecasts (ECMWF) (Leutbecher and Palmer 2008) and the Japan Meteorological Agency (JMA) (Yamaguchi and Majumdar 2010).…”

A study on the predictability of the transition day from the dry to the rainy season over South Korea

“…Pappenberger et al (2008) investigated flood forecasts of a multi-model ensemble for Romania in October 2007 using the LISFLOOD model of the European Flood Forecasting System (EFAS) as the hydrological component and found that this technique would have led to a correct flood warning about 8 days in advance of the severe weather. Yamagughi and Majumdar (2010) investigated the dynamic mechanism of perturbation growth in a tropical cyclone (Sinlaku, the typhoon in 2008) using ECMWF, NCEP, and JMA ensembles and found that the vertical and horizontal distributions of the initial perturbations, as well as the amplitude, were quite different among three NWP centers before, during, and after the recurvature of Typhoon Sinlaku. Hwang et al (2012) compared the performances of six ensemble models and a grand ensemble (GE) of the three best ensemble models (ECMWF, UKMO, and CMA) for inconsistencies, jumpiness, and rootmean square difference (RMSD) for 500 hPa geopotential height, 850 hPa temperature, and mean sea-level pressure and verified that the GE was more consistent than each of the single ensemble models.…”