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The ability of two downscaling experiments to correctly simulate the instability conditions that can trigger thunderstorms over the Iberian Peninsula is compared in this paper. To do so, three instability indices are evaluated: TT index, CAPE and CIN. The WRF model is used for the simulations. The N experiment is driven by ERA-Interim's initial and boundary conditions; The D experiment has the same configuration as N, but the 3DVAR data assimilation step is additionally run at 00, 06, 12 and 18 UTC. Eight radiosondes are available over the IP, and the values for these indices calculated from the University of Wyoming were chosen as reference in the validation of both simulations. Additionally, measured variables at different pressure levels from the radiosondes provided byWyoming were used to calculate the three instability indices by our own methodology using the R package aiRthermo. According to the validation, the correlation, SD and RMSE obtained by the experiment D for all the indices in most of the stations are better than those for N. The different methodologies produce small discrepancies between the values for TT, but these are larger for CAPE and CIN due to the dependency of these indices on the initial conditions assumed for the calculation of an air parcel's vertical evolution. Similar results arise from the seasonal analysis concerning both WRF experiments: N tends to overestimate or underestimate (depending on the index) the variability of the reference values, but D is able to capture it in most of the seasons. The heterogeneity of the indices is highlighted in the mean maps over the Iberian Peninsula. According to those from D, the ingredients for the development of convective precipitation during winter are found along the entire Atlantic coast, but in summer they are located particularly in the Mediterranean coast. The chances of developing thunderstorms in those areas at 12 UTC is much higher than at 00 UTC; The convective inhibition is more extended towards inland at 00 UTC in those areas, which prevents storms from developing. However, high values are observed near Murcia also at 12 UTC. [ABSTRACT FROM AUTHOR]

A new diagram is proposed for the verification of vector quantities generated by multiple models against a set of observations. It has been designed with the idea of extending the Taylor diagram to two dimensional quantities such as currents, wind, or horizontal fluxes of water vapour, salinity, energy and other geophysical variables. The diagram is based on the analysis of the two-dimensional structure of the mean squared error matrix between model and observations. This matrix is separated in a part corresponding to the bias and the relative rotation of the empirical orthogonal functions of the data. We test the performance of this new diagram to identify the differences amongst the reference dataset and the different model outputs by using examples with wind, current, vertically integrated moisture transport and wave energy flux time series. An alternative setup is shown in the last examples, where the spatial average of surface wind in the Northern and Southern Hemispheres according to different reanalyses and realizations of CMIP5 models are compared. The examples of use of the Sailor diagram presented show that it is a tool which helps in identifying errors due to the bias or the orientation of the simulated vector time series or fields. The R implementation of the diagram presented together with this paper allows also to easily retrieve the individual diagnostics of the different components of the mean squared error. [ABSTRACT FROM AUTHOR]

This study evaluates the performance of statistical models applied to the output of numerical models for short-term (1–24 h) hourly wind forecasts at three locations in the Basque Country. The target variables are horizontal wind components and the maximum wind gust at 3 h intervals. Statistical approaches such as persistence, analogues, linear regression, and random forest (RF) are used. The verification statistics used are coefficient of determination (R2) and root mean square error (RMSE). Statistical models use three inputs: (1) Local wind observations; (2) extended EOFs (empirical orthogonal functions) derived from past local observations and ERA-Interim variables in a previous 24-h period covering a domain around the area of study; and (3) wind forecasts provided by ERA-Interim. Results indicate that, for horizons less than 1–4 h, persistence is the best model. For longer predictions, RF provides the best forecasts. For horizontal components at 4–24 h horizons, RF slightly outperformed ERA-Interim wind forecasts. For gust, RF performs better than ERA-Interim for all the horizons. Persistence is the most influential factor for 2–5 h. Beyond this horizon, predictors from the ERA-Interim wind forecasts led the contribution. Hybrid numerical–statistical methods can be used to improve short-term wind forecasts. [ABSTRACT FROM AUTHOR]