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). Conventional observations such as surface stations and weather balloons are scarce at low latitudes, particularly over the vast tropical oceans. Consequently, the observing system is dominated by satellite data, which are heavily skewed toward measuring atmospheric mass variables rather than wind (e.g., Baker et al. 2014 ). However, data denial experiments for periods with a much enhanced radiosonde network during field campaigns over West Africa have shown a relatively small impact on model performance
). Conventional observations such as surface stations and weather balloons are scarce at low latitudes, particularly over the vast tropical oceans. Consequently, the observing system is dominated by satellite data, which are heavily skewed toward measuring atmospheric mass variables rather than wind (e.g., Baker et al. 2014 ). However, data denial experiments for periods with a much enhanced radiosonde network during field campaigns over West Africa have shown a relatively small impact on model performance
–STV relationship over the Asian–Pacific–American region is still unclear. In addition, phase 6 of the Coupled Model Intercomparison Project (CMIP6; Eyring et al. 2016 ) has recently been released. Whether the models of the new version can produce a more realistic ENSO–STV simulation than the last generation (CMIP5) also needs to be evaluated. In this study, we first aim to examine the relationship between ENSO and STV over the Asian–Pacific–American region with CMIP5/6 models in a historical simulation and
–STV relationship over the Asian–Pacific–American region is still unclear. In addition, phase 6 of the Coupled Model Intercomparison Project (CMIP6; Eyring et al. 2016 ) has recently been released. Whether the models of the new version can produce a more realistic ENSO–STV simulation than the last generation (CMIP5) also needs to be evaluated. In this study, we first aim to examine the relationship between ENSO and STV over the Asian–Pacific–American region with CMIP5/6 models in a historical simulation and
regression and the limitations of this approach. In section 4 we evaluate the performance of the models during Northern Hemisphere winter and demonstrate their applicability to an operational ECMWF ensemble forecast of a WCB event during January 2011. The study ends with concluding remarks and an outlook in section 5 . 2. Data a. Predictor dataset The predictor selection as well as the development and evaluation of the logistic regression models is based on ECMWF’s interim reanalysis data (ERA
regression and the limitations of this approach. In section 4 we evaluate the performance of the models during Northern Hemisphere winter and demonstrate their applicability to an operational ECMWF ensemble forecast of a WCB event during January 2011. The study ends with concluding remarks and an outlook in section 5 . 2. Data a. Predictor dataset The predictor selection as well as the development and evaluation of the logistic regression models is based on ECMWF’s interim reanalysis data (ERA
the perturbation method is applicable in any atmospheric model that allows for calculation of the relevant physical process information. The observational data used to evaluate the forecasts and the selected case studies in which the parameterization is tested will be introduced briefly as well as the analysis strategy for the suggested method. a. Physically based stochastic perturbations in the boundary layer We propose a concept of process-based model error representation in terms of a
the perturbation method is applicable in any atmospheric model that allows for calculation of the relevant physical process information. The observational data used to evaluate the forecasts and the selected case studies in which the parameterization is tested will be introduced briefly as well as the analysis strategy for the suggested method. a. Physically based stochastic perturbations in the boundary layer We propose a concept of process-based model error representation in terms of a
NWP forecasts for TC activity in many oceans (e.g., Vitart 2009 ; Belanger et al. 2010 ; Camp et al. 2018 ). Several studies have systematically evaluated these models in terms of predictive skill for different TC occurrence measures ( Lee et al. 2018 , 2020 ; Gregory et al. 2019 ). Lee et al. (2018) found that the Subseasonal to Seasonal (S2S; Vitart et al. 2017 ) models generally have little to zero skill in predicting TC occurrence from week 2 on for all basins relative to
NWP forecasts for TC activity in many oceans (e.g., Vitart 2009 ; Belanger et al. 2010 ; Camp et al. 2018 ). Several studies have systematically evaluated these models in terms of predictive skill for different TC occurrence measures ( Lee et al. 2018 , 2020 ; Gregory et al. 2019 ). Lee et al. (2018) found that the Subseasonal to Seasonal (S2S; Vitart et al. 2017 ) models generally have little to zero skill in predicting TC occurrence from week 2 on for all basins relative to
of the form with nonnegative weights , , and that sum to 1, and reflects the members’ performance during the training period. 4 Each of the component distributions, , , and , contains a point mass at zero and a density for positive accumulations. The point mass at zero specifies the probability of no precipitation and is estimated in a logistic regression model, where the cube root of the member forecast and a binary indicator of the member forecast being zero are used as predictor
of the form with nonnegative weights , , and that sum to 1, and reflects the members’ performance during the training period. 4 Each of the component distributions, , , and , contains a point mass at zero and a density for positive accumulations. The point mass at zero specifies the probability of no precipitation and is estimated in a logistic regression model, where the cube root of the member forecast and a binary indicator of the member forecast being zero are used as predictor
ENS is computed in a similar manner as ( section 2f ) ensures that both quantities are representative of the same environment. Parameter H ENS is finally evaluated in millimeters per day per kilometer, as E is computed as a water flux rate in millimeters per day [see section 2a , Eq. (2) ] and the grid spacing for the spatial derivate is provided in kilometers. 3. Results a. Model validation In this section the performance of the ensemble and ensemble subsets ( section 2b ) in reproducing
ENS is computed in a similar manner as ( section 2f ) ensures that both quantities are representative of the same environment. Parameter H ENS is finally evaluated in millimeters per day per kilometer, as E is computed as a water flux rate in millimeters per day [see section 2a , Eq. (2) ] and the grid spacing for the spatial derivate is provided in kilometers. 3. Results a. Model validation In this section the performance of the ensemble and ensemble subsets ( section 2b ) in reproducing
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
of forecast errors and uncertainties from small to large scales ( Grams et al. 2018 ). On the medium range, the representation of WCBs in NWP models was first evaluated by Madonna et al. (2015) for three winter periods [December–February (DJF)] in the operational high resolution deterministic forecast of the ECMWF Integrated Forecasting System (IFS) model. They used a novel feature-based verification technique that was originally developed to verify precipitation forecasts ( Wernli et al. 2008
of forecast errors and uncertainties from small to large scales ( Grams et al. 2018 ). On the medium range, the representation of WCBs in NWP models was first evaluated by Madonna et al. (2015) for three winter periods [December–February (DJF)] in the operational high resolution deterministic forecast of the ECMWF Integrated Forecasting System (IFS) model. They used a novel feature-based verification technique that was originally developed to verify precipitation forecasts ( Wernli et al. 2008
