Daily loss per district Sample Clauses

Daily loss per district. While temporal or spatial aggregation generally leads to a convergence of model estimates and observations, strong variability is expected for daily storm loss estimates on the fine district scale. On the basis of root-mean-square error, we define a coefficient of variation RMSE x CV = 1 1 Σn ( ˆ )2 2 (22) i − x , x¯ n i=1 where, for n samples, x and xˆ denote the observations and estimates of the expected value, respectively. Values are normalised to the mean of the observations x¯. Table 14 shows regional averages of CVRMSE for each of the four competing models. These results highlight the interdependence between model and wind choice. While model H mostly Table 14: Spatial averages of the coefficient of variation (RMSE) for each model. For ease of comparison, values are sorted in ascending order. The respective model is indicated by the colour code. The spatial extent is defined by the four geographic regions (north, east, south, west) depicted in the map inset. North East South West All N E W S DWD 231 342 436 228 331 248 384 580 255 385 266 403 911 290 508 000 000 000 352 578 ERA Int. 000 000 000 286 376 401 333 469 299 387 515 342 738 305 458 842 580 745 527 665 Model color code X H P K outperforms the competing models for DWD wind data, it appears less suited for ERA Interim wind data, whose distribution properties are distinctly different from those of the DWD data. Of particular interest is the fact that, irrespective of the wind data source, model H performs best across southern Germany. With relatively complex terrain and less frequent storm events, this region poses the greatest challenge to the damage models, resulting in a wide spread of coefficient values between different models. In contrast, model K appears to be least reliable in the south. While the exponential model X fares worst overall, it scores best for DWD wind data over northern Germany. It may be assumed that in this region the probability distribution of the DWD wind data is most favourable for the steep exponential model. Overall, models H and P show the least variation throughout. While model K performs well, with the exception of southern Germany, the exponential model consistently generates the largest amount of variation and, hence, modeling error. Due to the fact that the district resolution exceeds the resolution of sampling points of the wind field, a strong influence of the choice of wind data is expected. Figure 19 shows a baseline CVRMSE estimated as the minimum value...