Search Results
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
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
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
uncertainty in weather and climate predictions. Improving the treatment of cloud processes in models has been a research priority for many decades and is unlikely to have a quick solution. In W2W, we focus on a different question, and seek to quantify the uncertainty that our lack of knowledge of cloud processes creates, and to evaluate its contribution to limiting the overall predictability of the atmosphere. One type of error is structural uncertainty. These are uncertainties associated with errors in
uncertainty in weather and climate predictions. Improving the treatment of cloud processes in models has been a research priority for many decades and is unlikely to have a quick solution. In W2W, we focus on a different question, and seek to quantify the uncertainty that our lack of knowledge of cloud processes creates, and to evaluate its contribution to limiting the overall predictability of the atmosphere. One type of error is structural uncertainty. These are uncertainties associated with errors in
“extreme” compared with the model climatology, the observed rainfall amount shall be considered extreme when compared against the real climatology ( ECMWF 2015 ). This approach also has advantages in that problems with rain gauge densities and errors in satellite-derived rainfall estimates are circumvented. In other words, for the case under study it shall be evaluated at which lead times, if any, extreme 24-h precipitation totals were forecasted in the Gulf of Tonkin area with respect to the EPS model
“extreme” compared with the model climatology, the observed rainfall amount shall be considered extreme when compared against the real climatology ( ECMWF 2015 ). This approach also has advantages in that problems with rain gauge densities and errors in satellite-derived rainfall estimates are circumvented. In other words, for the case under study it shall be evaluated at which lead times, if any, extreme 24-h precipitation totals were forecasted in the Gulf of Tonkin area with respect to the EPS model
Aerosol Effects on Clouds and Precipitation over Central Europe in Different Weather Regimesprecipitation to changes in the aerosol content and thermodynamical conditions of the atmosphere. Nevertheless, we have evaluated the respective reference runs at least in a qualitative way to ensure that the COSMO model simulates the main weather characteristics on the analyzed days reasonably well. The simulated 24-h precipitation amount of the reference runs with continental CCN displayed in Fig. 5 show good agreement with observations ( Fig. 4 ) for all days. Not only the convective or stratiform
precipitation to changes in the aerosol content and thermodynamical conditions of the atmosphere. Nevertheless, we have evaluated the respective reference runs at least in a qualitative way to ensure that the COSMO model simulates the main weather characteristics on the analyzed days reasonably well. The simulated 24-h precipitation amount of the reference runs with continental CCN displayed in Fig. 5 show good agreement with observations ( Fig. 4 ) for all days. Not only the convective or stratiform
( IPCC 2012 ). For African countries, impacts of natural hazards are projected to be 20–30 times larger than in industrialized countries ( IPCC 2014 ). The most severe climate change impacts can be expected for regions of high population density and poverty rates ( Müller et al. 2014 ), as often observed in African cities. Hirabayashi et al. (2013) found a high consistency among global climate models predicting large increases in flood frequency in Africa under the strongest climate change scenario
( IPCC 2012 ). For African countries, impacts of natural hazards are projected to be 20–30 times larger than in industrialized countries ( IPCC 2014 ). The most severe climate change impacts can be expected for regions of high population density and poverty rates ( Müller et al. 2014 ), as often observed in African cities. Hirabayashi et al. (2013) found a high consistency among global climate models predicting large increases in flood frequency in Africa under the strongest climate change scenario
