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Florian Harnisch and Martin Weissmann

1. Introduction Tropical cyclones (TCs) usually develop over data-sparse regions of the tropical oceans. The limited number of observations and the rapid development of TCs increases uncertainties of the model analysis in these regions, which can lead to significant forecast errors ( Langland 2005 ). Surveillance programs deploying dropsonde observations in and around TCs have been operated for the Atlantic ( Burpee et al. 1996 ; Aberson 2002 ) and the western North Pacific basin ( Wu et al

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Kirstin Kober, Annette M. Foerster, and George C. Craig

be obtained by postprocessing. The results would depend on the methods used, but quite sophisticated techniques based on spatial statistics are being developed ( Scheuerer 2014 ). On the other hand, if there are significant dynamical feedbacks from the convective variability onto the larger-scale flow, there is potential for a physically based parameterization to improve an ensemble forecast in ways that postprocessing could not. This too is an important topic for future research. Acknowledgments

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Julia H. Keller

; Torn 2010a ; Keller et al. 2014 ; Quinting and Jones 2016 ). To the author’s knowledge, the present study is the first that combines the two analysis techniques to make use of the analysis for an operational EPS in a way that goes beyond the investigation of several forecast scenarios. The paper is organized as follows. Section 2 introduces the database used in the study, while an outline of the analysis methods is provided in section 3 . The investigation and interpretation of the results

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Julia H. Keller, Sarah C. Jones, and Patrick A. Harr

to identify the dominant processes during the interaction, describe their representation in the scenarios and thus elucidate their influence on the distinct developments. After a brief introduction of the analysis technique and the selection of particular ensemble members in the next section, an overview of the two tropical cyclones is provided in section 3 . We then analyze the different forecast scenarios for the ET of the two storms using the K e framework ( section 4 ). Conclusions are

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Julian F. Quinting and Sarah C. Jones

wave dispersion. The dispersion of Rossby waves into downstream regions has the ability to cause surface cyclogenesis ( Agusti-Panareda et al. 2004 ; Cordeira and Bosart 2010 ) or high-impact weather events such as severe precipitation (e.g., Martius et al. 2008 ; Grams et al. 2011 ; Grams and Blumer 2015 ). Furthermore, initial perturbations and ET-related forecast uncertainties tend to grow and spread into downstream regions in connection with the development of midlatitude Rossby wave

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Julia H. Keller, Christian M. Grams, Michael Riemer, Heather M. Archambault, Lance Bosart, James D. Doyle, Jenni L. Evans, Thomas J. Galarneau Jr., Kyle Griffin, Patrick A. Harr, Naoko Kitabatake, Ron McTaggart-Cowan, Florian Pantillon, Julian F. Quinting, Carolyn A. Reynolds, Elizabeth A. Ritchie, Ryan D. Torn, and Fuqing Zhang

research on the representation of model errors arising from diabatic processes using techniques such as stochastic physics. The research summarized in this review primarily focused on assessing the impact of ET on the short-to-medium-range forecast horizon. Preliminary results reveal a statistically significant correlation between monthly mean values of selected teleconnection indices and ET event counts, as well as significant departures from climatology on the subseasonal to seasonal time scale in

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Volkmar Wirth, Michael Riemer, Edmund K. M. Chang, and Olivia Martius

to surface weather that falls into the tail(s) of the respective local distribution (e.g., precipitation exceeding the 95th percentile). To the extent that weather events inherit predictability from larger-scale dynamical features such as RWPs ( Anthes et al. 1985 ), a better understanding of the RWPs may help to improve the weather forecast, and this is particularly relevant in case of extreme weather. An example is the episode in August 2002, when a quasi-stationary low pressure system over

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Gabriel Wolf and Volkmar Wirth

distinguish complex demodulation from the Hilbert transform technique. Incidentally we note that envelope reconstruction is a nonlinear operation working on a function υ ; this means that generally . As can be seen in Fig. 2a , both methods reconstruct the envelope A ( λ ) very well for the almost-plane wave packet (thick solid and thick dashed lines). When complex demodulation is applied with s 0 ≠ s c (not shown), the quality of the reconstruction decreases. As long as the reference wavenumber

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Hilke S. Lentink, Christian M. Grams, Michael Riemer, and Sarah C. Jones

, and R. L. Elsberry , 2000 : Extratropical transition of western North Pacific tropical cyclones: An overview and conceptual model of the transformation stage . Wea. Forecasting , 15 , 373 – 395 , https://doi.org/10.1175/1520-0434(2000)015<0373:ETOWNP>2.0.CO;2 . 10.1175/1520-0434(2000)015<0373:ETOWNP>2.0.CO;2 Kossin , J. P. , and C. S. Velden , 2004 : A pronounced bias in tropical cyclone minimum sea level pressure estimation based on the Dvorak technique . Mon. Wea. Rev. , 132

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Christian M. Grams and Heather M. Archambault

1. Introduction The large-scale midlatitude flow is dominated by the upper-level jet stream that serves as a waveguide for Rossby waves (e.g., Martius et al. 2010 ). Because their general evolution follows dry dynamics that can be represented at grid scale in numerical weather prediction (NWP) models, Rossby waves may be expected to feature a high degree of predictability ( Grazzini and Vitart 2015 ). However, major forecast uncertainty and error in the midlatitudes in current NWP models have

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