The seasonal lightning distribution at the Gaisberg tower in Austria is very different from the distribution observed in a circular ring with a radius of 2 to 10 km around the tower. The distribution ...in the circular ring shows the typical peak during the convective season. In contrast, we find a bimodal distribution with a peak in early spring and late fall at the tower. By selecting two cases representing these two situations, the differences in the meteorological setting are discussed.
Lightning events observed solely at the Gaisberg tower are characterized by strong horizontal winds in the lower layers of the atmosphere from the northwest sector. This direction represents the open window from the Gaisberg towards the foreland, meaning there are no mountains upstream, and the flow can reach the Gaisberg unhindered. Cases of this type occur mainly during the cold season but are not limited to it.
In contrast, lightning events at the tower and in its vicinity are typical for convective events with low horizontal wind speed in the lower layers and occur mainly during the convective season.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
2.
Ensemble reduction using cluster analysis Serafin, Stefano; Strauss, Lukas; Dorninger, Manfred
Quarterly journal of the Royal Meteorological Society,
January 2019 Part B, 2019-01-00, 20190101, Volume:
145, Issue:
719
Journal Article
Peer reviewed
Ensemble reduction is the task of selecting a subset of the members of a global ensemble prediction system (EPS) to specify the initial and boundary conditions for the integration of a limited‐area ...EPS. Cluster analysis is often used for this purpose, even if random member selection would be a legitimate approach as well. Clustering algorithms organize forecasts from different ensemble members into groups, based on the degree of similarity between selected forecast fields. Reduction is performed by choosing one representative member from each cluster. Ensemble reduction degrades forecast accuracy, measured by the continuous rank probability score. The degree of degradation depends primarily on the size of the reduced ensemble and becomes larger as the ensemble gets smaller. We estimate the loss of forecast accuracy caused by different ensemble reduction methods by comparing the probabilistic forecasts obtained from the 51‐member EPS run by ECMWF with those from several reduced ensembles. We show that different ensemble reduction methods cause marginally different loss of accuracy and that, generally, clustering methods are not significantly better at ensemble reduction than random sampling. Clustering typically results in reduced ensembles with significantly lower spread than both the parent ensemble and randomly defined subsets. The effectiveness of clustering depends on the forecast range and on the variables used to cluster the global ensemble members; not all meteorological parameters are equally good clustering variables. Clustering is most effective at ensemble reduction when it detects meaningful differences between the ensemble members. This is only possible at forecast ranges beyond about 3 days and when variables with a low degree of small‐scale spatial variability are used as object descriptors.
The figure uses colour to display the result of a cluster analysis. Individual objects are the members of an ensemble forecast system and three clusters are detected. Ensemble reduction consists of choosing one representative member from each cluster. Ensemble reduction by clustering gives useful results only at forecast ranges longer than 3 days.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Forecasts of a set of three model chains characterising a variety of model versions and types are evaluated. Each model chain consists of three models with increasing resolution nested into one ...another. Rules for a fair model inter-comparison have been defined. E.g., they refer to the use of NWP-model independent analyses as reference data which, in this study, are provided by VERA (Vienna Enhanced Resolution Analysis). Observational data and model data have been collected in a combined effort of COPS (Convective and Orographically-induced Precipitation Study) and D-PHASE (Demonstration of the Probabilistic Hydrological and Atmospheric Simulation of flood Events in the alpine region). Verification parameters are precipitation and the gradient of equivalent potential temperature as front indicator. The verification domain covers Central Europe. Verification periods range from half a year to single case studies. A selection of novel and traditional verification metrics has been implemented to examine multiple aspects of the model chains. The results only partly confirm previous findings that the models with the highest resolution usually outperform their counterparts of lower resolution. We find a rather different behaviour from model chain to model chain. Additional forecast skill is not consistently added by the nested models with the highest resolution. In the case of frontal propagation it is the coarsest model, which shows the best results. Wavelet transforms are used to study phase and modulus coherence of forecast and analysis on different scales.
Meteorological events affecting the evolution of temperature inversions or cold-air pools in the 1-km-diameter, high-altitude (~1300 m MSL) Grünloch basin in the eastern Alps are investigated using ...data from lines of temperature dataloggers running up the basin sidewalls, nearby weather stations, and weather charts. Nighttime cold-air-pool events observed from October 2001 to June 2002 are categorized into undisturbed inversion evolution, late buildups, early breakups, mixing events, layered erosion at the inversion top, temperature disturbances occurring in the lower or upper elevations of the pool, and inversion buildup caused by the temporary clearing of clouds. In addition, persistent multiday cold-air pools are sometimes seen. Analyses show that strong winds and cloud cover are the governing meteorological parameters that cause the inversion behavior to deviate from its undisturbed state, but wind direction can also play an important role in the life cycle of the cold-air pools, because it governs the interaction with steep or gentle slopes of the underlying topography. Undisturbed cold-air pools are unusual in the Grünloch basin. A schematic diagram illustrates the different types of cold-air-pool events.
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In the framework of a multi-level verification experiment, the impact of different characteristics of verification reference data on NWP-model verification is evaluated. These are the analysis ...method, the grid resolution and the density of underlying observation data. A set of six limited area NWP-models is evaluated by three model-independent analysis methods, based on two different observation networks. Verification is performed on four regular grids with horizontal resolutions ranging from 4-32 km. Traditional verification measures are combined with scale-separation techniques using a 2-dimensional wavelet-transform. Verification uncertainties are estimated by four different applications: A poor man's ensemble derived from the sample of analysis variations, a resampling approach, and two different ensemble analysis tools based on random perturbations. Mechanisms of uncertainty estimation are discussed and their effectiveness is shown through various examples. Overall results indicate that the main sources of verification uncertainties due to analysis data are not the interpolation methods, but primarily observation density and grid resolution.
The 7th International Verification Methods Workshop – with a theme of “forecast verification methods across time and space scales” – was held in Berlin between 3 and 11 May 2017. The workshop and ...associated training tutorial represent two flagship activities of the World Meteorological Organization's (WMO's) Joint Working Group on Forecast Verification Research (JWGFVR) which falls under the World Weather Research Programme (WWRP) and the Working Group for Numerical Experimentation (WGNE).
This study explores the potential of a multiphysics regional ensemble prediction system to improve forecasts of wind turbine icing, examining several error‐representation schemes to capture the ...forecasting uncertainties of the icing process. An 11‐member multiphysics ensemble based on the Weather Research and Forecasting (WRF) model is run for two winter periods over Europe. Regional verification of surface variables shows that parametrization diversity makes the multiphysics ensemble less underdispersive compared with the European Centre for Medium‐Range Weather Forecasts (ECMWF) global ensemble, without deteriorating the overall forecast accuracy significantly, in particular at forecast ranges below 36 hr. Probability forecasts of active ice growth in the day‐ahead time range (12–36 hr) are derived for two wind farms on hilly terrain in Central Europe and their skill is assessed in terms of relative operating characteristics, reliability, and potential economic value (PEV). Probability forecasts enhance the maximum PEV significantly, but the improvement of the multiphysics ensemble seems modest compared with a simple neighbourhood ensemble approach. Icing forecasts are affected by a considerable degree of overconfidence, meaning that forecast probabilities cannot be used at face value, but require calibration for users to draw benefit from them. The multiphysics ensemble fares slightly better in this regard; however, results point to persistent ensemble underdispersiveness and yet underrepresented forecast uncertainty. Overall, findings show that a large portion of the gain in skill through the use of probabilistic icing forecasts is obtained with a computationally cheap neighbourhood method, a technique easily accessible to forecast users without complex ensemble prediction systems.
Icing events can reduce the power production of wind turbines, or cause their shutdown for safety reasons. The complexity of the atmospheric processes involved makes the forecasting of such events extremely challenging. In this work, high‐resolution multiphysics ensemble forecasts of icing are developed. Their added value compared with simpler forecasting techniques is assessed using icing measurements on turbine hubs.
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In this paper, a verification study of the skill and potential economic value of forecasts of ice accretion on wind turbines is presented. The phase of active ice formation on turbine blades has been ...associated with the strongest wind power production losses in cold climates; however, skillful icing forecasts could permit taking protective measures using anti-icing systems. Coarse- and high-resolution forecasts for the range up to day 3 from global (IFS and GFS) and limited-area (WRF) models are coupled to the Makkonen icing model. Surface and upper-air observations and icing measurements at turbine hub height at two wind farms in central Europe are used for model verification over two winters. Two case studies contrasting a correct and an incorrect forecast highlight the difficulty of correctly predicting individual icing events. A meaningful assessment of model skill is possible only after bias correction of icing-related parameters and selection of model-dependent optimal thresholds for ice growth rate. The skill of bias-corrected forecasts of freezing and humid conditions is virtually identical for all models. Hourly forecasts of active ice accretion generally show limited skill; however, results strongly suggest the superiority of high-resolution WRF forecasts relative to other model variants. Predictions of the occurrence of icing within a period of 6 h are found to have substantially better accuracy. Probabilistic forecasts of icing that are based on gridpoint neighborhood ensembles show slightly higher potential economic value than forecasts that are based on individual gridpoint values, in particular at low cost-loss ratios, that is, when anti-icing measures are comparatively inexpensive.
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