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( 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
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
-tropopause, the tropospheric-deep, and the divergent terms, respectively. e. Computation of LWA and its budget from model data First, LWA is calculated using the algorithm of Ghinassi et al. (2018) . Thereafter, the terms in the budget equations, (9) and (15) , are computed as follows. For a given time step n we consider data at time n − 1, n , and n + 1. The time derivatives are computed using centered differences between time steps n + 1 and n − 1. We then evaluate the integrals for T C and
-tropopause, the tropospheric-deep, and the divergent terms, respectively. e. Computation of LWA and its budget from model data First, LWA is calculated using the algorithm of Ghinassi et al. (2018) . Thereafter, the terms in the budget equations, (9) and (15) , are computed as follows. For a given time step n we consider data at time n − 1, n , and n + 1. The time derivatives are computed using centered differences between time steps n + 1 and n − 1. We then evaluate the integrals for T C and
( 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
of the North Atlantic waveguide, NAWDEX also offered a unique opportunity to explore HIW predictability. To the best of our knowledge, the NAWDEX period provides the most complete set of combined wind, humidity, temperature, and cloud profile observations of the North Atlantic jet stream yet assembled. This dataset will form the basis of detailed case studies and evaluations of weather and climate prediction models for many years. The widespread coverage of high-resolution multivariate cross
of the North Atlantic waveguide, NAWDEX also offered a unique opportunity to explore HIW predictability. To the best of our knowledge, the NAWDEX period provides the most complete set of combined wind, humidity, temperature, and cloud profile observations of the North Atlantic jet stream yet assembled. This dataset will form the basis of detailed case studies and evaluations of weather and climate prediction models for many years. The widespread coverage of high-resolution multivariate cross
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
