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boosting. However, initial tests indicated slightly worse predictive performance; we thus focus on maximum likelihood-based methods instead. 7 To account for the intertwined choice of scoring rules for model estimation and evaluation ( Gebetsberger et al. 2017 ), we have also evaluated the models using LogS. However, as the results are very similar to those reported here and computation of LogS for the raw ensemble and QRF forecasts is problematic ( Krüger et al. 2016 ), we focus on CRPS
boosting. However, initial tests indicated slightly worse predictive performance; we thus focus on maximum likelihood-based methods instead. 7 To account for the intertwined choice of scoring rules for model estimation and evaluation ( Gebetsberger et al. 2017 ), we have also evaluated the models using LogS. However, as the results are very similar to those reported here and computation of LogS for the raw ensemble and QRF forecasts is problematic ( Krüger et al. 2016 ), we focus on CRPS
statistical postprocessing methods, whose predictive performance is evaluated in section 4 . A meteorological interpretation of what the models have learned is presented in section 5 . Section 6 concludes with a discussion. R code ( R Core Team 2021 ) with implementations of all methods is available online ( https://github.com/benediktschulz/paper_pp_wind_gusts ). 2. Data and notation a. Forecast and observation data Our study is based on the same dataset as Pantillon et al. (2018) and we
statistical postprocessing methods, whose predictive performance is evaluated in section 4 . A meteorological interpretation of what the models have learned is presented in section 5 . Section 6 concludes with a discussion. R code ( R Core Team 2021 ) with implementations of all methods is available online ( https://github.com/benediktschulz/paper_pp_wind_gusts ). 2. Data and notation a. Forecast and observation data Our study is based on the same dataset as Pantillon et al. (2018) and we
that scales to γ = 1/2 km −1 for nondamped waves and to max ( γ 0 , − m ) for damped waves. Mathematically, this can be expressed as (7) w ′ = w 0 f ( z ) , (8) where f ( z ) = { e − γ ( z − z max ) , if z ≥ z max 1 z max z , if z < z max , (9) with γ = { γ 0 ω 2 ≤ N 2 ( nondamped ) max ( γ 0 , − m ) ω 2 ≥ N 2 ( damped ) . Further details are described in appendix A . 3. Model simulations, observations, and simulation period To evaluate the impact of the PSP variants and the
that scales to γ = 1/2 km −1 for nondamped waves and to max ( γ 0 , − m ) for damped waves. Mathematically, this can be expressed as (7) w ′ = w 0 f ( z ) , (8) where f ( z ) = { e − γ ( z − z max ) , if z ≥ z max 1 z max z , if z < z max , (9) with γ = { γ 0 ω 2 ≤ N 2 ( nondamped ) max ( γ 0 , − m ) ω 2 ≥ N 2 ( damped ) . Further details are described in appendix A . 3. Model simulations, observations, and simulation period To evaluate the impact of the PSP variants and the
of the prevailing synoptic-scale weather regime in combination with orography? The outline of the article is as follows: section 2 describes the ensemble data assimilation and forecasting systems, the setup, and the observations. Section 3 briefly introduces measures and scores used to evaluate the experiments. Section 4 presents the results with a focus on predictable scales in NWP model configurations with different levels of realism. Concluding remarks and a comparison to previous
of the prevailing synoptic-scale weather regime in combination with orography? The outline of the article is as follows: section 2 describes the ensemble data assimilation and forecasting systems, the setup, and the observations. Section 3 briefly introduces measures and scores used to evaluate the experiments. Section 4 presents the results with a focus on predictable scales in NWP model configurations with different levels of realism. Concluding remarks and a comparison to previous
bust” for the majority of the operational forecast models, showing a huge drop in the medium-range forecast skill over Europe ( Rodwell et al. 2013 ). The authors associated this poor performance to the misrepresentation of moist convective processes over North America a few days earlier, and this error was subsequently communicated downstream embedded in a RWP. Data are retrieved from the ERA-Interim reanalyses ( Dee et al. 2011 ) with a horizontal resolution of 2° × 2° on 20 pressure levels
bust” for the majority of the operational forecast models, showing a huge drop in the medium-range forecast skill over Europe ( Rodwell et al. 2013 ). The authors associated this poor performance to the misrepresentation of moist convective processes over North America a few days earlier, and this error was subsequently communicated downstream embedded in a RWP. Data are retrieved from the ERA-Interim reanalyses ( Dee et al. 2011 ) with a horizontal resolution of 2° × 2° on 20 pressure levels
nondivergent wind field by inverting the vorticity enclosed in a circle of radius R = 600 km, centered at TC location in IBTrACS. Second, the algorithm does not consider the axis of the troughs but evaluates so-called “trough objects,” contiguous regions of cyclonic vorticity advection (CVA) larger than , where and is the component of vorticity due to the curvature of the flow only. Finally, unlike African easterly waves, midlatitude troughs propagate along the westerly jet stream, and therefore
nondivergent wind field by inverting the vorticity enclosed in a circle of radius R = 600 km, centered at TC location in IBTrACS. Second, the algorithm does not consider the axis of the troughs but evaluates so-called “trough objects,” contiguous regions of cyclonic vorticity advection (CVA) larger than , where and is the component of vorticity due to the curvature of the flow only. Finally, unlike African easterly waves, midlatitude troughs propagate along the westerly jet stream, and therefore
; moreover, the forecast became rather poor after a lead time of as little as 5 days. The latter result seems to be at odds with the commonly held view that large-scale phenomena such as RWPs should be predictable on a rather long time scale. However, this evaluation was for a single case only involving a single forecast model; further systematic studies are required to possibly generalize these results. Regarding the waveguide, several operational forecast models are fraught with a spurious decrease of
; moreover, the forecast became rather poor after a lead time of as little as 5 days. The latter result seems to be at odds with the commonly held view that large-scale phenomena such as RWPs should be predictable on a rather long time scale. However, this evaluation was for a single case only involving a single forecast model; further systematic studies are required to possibly generalize these results. Regarding the waveguide, several operational forecast models are fraught with a spurious decrease of
pre-Chris cyclone. An ensemble member thus belongs to the cyclone (no-cyclone) group when it predicts (lacks) such a “similar track.” Group names are italicized hereafter to better distinguish them from text. c. Cyclone tracking and evaluation of forecast tracks The simple cyclone tracking algorithm described by Hart (2003) is employed here because its performance compared to manual tracking was found to be acceptable. Based on mean sea level pressure data, this approach successively evaluates 5
pre-Chris cyclone. An ensemble member thus belongs to the cyclone (no-cyclone) group when it predicts (lacks) such a “similar track.” Group names are italicized hereafter to better distinguish them from text. c. Cyclone tracking and evaluation of forecast tracks The simple cyclone tracking algorithm described by Hart (2003) is employed here because its performance compared to manual tracking was found to be acceptable. Based on mean sea level pressure data, this approach successively evaluates 5
appropriate in the current context is the wave activity flux of Takaya and Nakamura (2001) . One particular feature of this formulation is its phase independence; this means that it discounts individual troughs and ridges and focuses on the dynamics of the entire wave packet instead ( Danielson et al. 2006 ). Focus on the entire wave packet is desirable, for instance, when studying model errors as opposed to initial condition errors ( Gray et al. 2014 ), and it would be interesting to find out whether
appropriate in the current context is the wave activity flux of Takaya and Nakamura (2001) . One particular feature of this formulation is its phase independence; this means that it discounts individual troughs and ridges and focuses on the dynamics of the entire wave packet instead ( Danielson et al. 2006 ). Focus on the entire wave packet is desirable, for instance, when studying model errors as opposed to initial condition errors ( Gray et al. 2014 ), and it would be interesting to find out whether
