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nominal level of α = 0.05 of the corresponding one-sided tests. The ( i , j ) entry in the i th row and j th column indicates the ratio of cases where the null hypothesis of equal predictive performance of the corresponding one-sided DM test is rejected in favor of the model in the i th row when compared with the model in the j th column. The remainder of the sum of ( i , j ) and ( j , i ) entry to 100% is the ratio of cases for which the score differences are not significant. Fig . 7
nominal level of α = 0.05 of the corresponding one-sided tests. The ( i , j ) entry in the i th row and j th column indicates the ratio of cases where the null hypothesis of equal predictive performance of the corresponding one-sided DM test is rejected in favor of the model in the i th row when compared with the model in the j th column. The remainder of the sum of ( i , j ) and ( j , i ) entry to 100% is the ratio of cases for which the score differences are not significant. Fig . 7
distributions on the performance of the SEC is evaluated in section 5e . Figure 1 shows the sampling error corrected correlation r ^ sec as a function of the sample correlation r ^ for different ensemble sizes and a uniform prior. For example, applying the SEC using a 40-member ensemble, a sample correlation of 0.5 is corrected to approximately 0.42. This study mainly uses the SEC table provided by the Data Assimilation Research Testbed (DART; Anderson et al. 2009 ) that is based on a uniform prior
distributions on the performance of the SEC is evaluated in section 5e . Figure 1 shows the sampling error corrected correlation r ^ sec as a function of the sample correlation r ^ for different ensemble sizes and a uniform prior. For example, applying the SEC using a 40-member ensemble, a sample correlation of 0.5 is corrected to approximately 0.42. This study mainly uses the SEC table provided by the Data Assimilation Research Testbed (DART; Anderson et al. 2009 ) that is based on a uniform prior
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
subgrid scale. In the context of convective-scale data assimilation, it is important that the background error covariance captures uncertainties on the smallest resolvable scales as well as effects on the resolved scales of subgrid-scale uncertainties. Deficient representation of model error will usually lead to overconfidence of the ensemble, eventually deteriorating the performance of convective-scale data assimilation and consequently the quality of the subsequent forecasts. To account for model
subgrid scale. In the context of convective-scale data assimilation, it is important that the background error covariance captures uncertainties on the smallest resolvable scales as well as effects on the resolved scales of subgrid-scale uncertainties. Deficient representation of model error will usually lead to overconfidence of the ensemble, eventually deteriorating the performance of convective-scale data assimilation and consequently the quality of the subsequent forecasts. To account for model
( Schäfler et al. 2018 ). The influence of the collected observational data on forecast performance during the entire campaign period is investigated via cycled data denial experiments with the global model of the European Centre for Medium-Range Weather Forecasts (ECMWF) and assessing the Forecast Sensitivity to Observation Impact (FSOI) method. This enables an assessment of the accumulated observation impact as well as the relative importance of different observation types and observed parameters. The
( Schäfler et al. 2018 ). The influence of the collected observational data on forecast performance during the entire campaign period is investigated via cycled data denial experiments with the global model of the European Centre for Medium-Range Weather Forecasts (ECMWF) and assessing the Forecast Sensitivity to Observation Impact (FSOI) method. This enables an assessment of the accumulated observation impact as well as the relative importance of different observation types and observed parameters. 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
