A methodology for maximum precipitation (MP) estimation that uses a physically based numerical atmospheric model is proposed in this paper. As a case study, the model-based 72-h MP was estimated for the American River watershed (ARW) in California for the December 1996–January 1997 flood event. First, a regional atmospheric model, MM5, was calibrated and validated for the December 1996–January 1997 historical major storm event for the ARW, on the basis of the U.S. National Center for Atmospheric Research (NCAR) reanalysis data to demonstrate the model capability during the historical period. Then, the model-simulated historical storm event was maximized by modifying its boundary conditions. The model-simulated precipitation field in the ARW was successfully validated at nine individual rain gauge stations in the watershed. The computed basin-averaged precipitation was somewhat higher than observations obtained by the spatial interpolation of the rain gauge observations. This result suggests a limitation o...

Wind energy is a rapidly growing industry in Turkey. Wind power potential studies revealed that the most promising region for electricity generation is the western part of Turkey. Wind speed forecasting is necessary for power systems because of the intermittent nature of wind. Thus, accurate forecasting of wind power is recognized as a major contribution to reliable wind power integration. This paper assesses the performance of the weather research forecasting (WRF) model for wind speed and wind direction predictions up to 72 h ahead. The wind speeds and wind directions are evaluated based on the mean absolute error (MAE). Evaluations were also performed seasonally. Moreover, in order to improve the WRF simulations, a multi-input–single output artificial neural network (ANN) approach is applied to both wind speeds of the WRF model and wind power estimates, which are estimated from the wind speeds of the WRF model by using a power curve for the Soma wind power plant. Traditional error metrics were used for validations using wind tower mast data installed nearby the wind farm. The results from up to 72 h forecast horizon show that the WRF model slightly overpredicts the wind speeds. Wind speed predictions by the WRF model are found highly depending on the season, location, and wind direction. The model is also able to reproduce wind directions except for low wind speeds. Large MAEs are found for the winds less than 5 m/s. The performance of the WRF model for wind power prediction decreases with the increasing runtime. Root mean square error and normalized root mean square error (nRMSE) in wind powers range in between 123–261 kW and 13%–32% without performing the ANN approach, respectively. The improvement of the ANN depends on the forecast horizon, season, and location of turbine groups, as well as its application on either the wind speed outputs of the WRF model or wind power estimations. The ANN significantly improves the WRF at large forecast horizons for wind power estimations, for which it gives better results in the summer and reaches 29% improvement for summer on average for nRMSE. On the other hand, ANN adjusts the wind speed outputs of the model better than that of wind power estimations. For instance, the nRMSE is approximately 13% for 24 h winter wind speed simulations of the WRF for the turbine groups G1 and G4, after ANN adjustment. The ANN improves the results better for turbine group 1, because of less complexity of this group in the direction of prevailing wind. The evaluation of the ANN suggests that the approach can be used for improving the performance of the wind power forecast for this power plant.

The present study aims at comparing total cloud cover (TCC) as simulated by regional climate models (RCM) from CORDEX project with the same variable as simulated by the driving global climate models (GCM), which are part of the fifth phase of the Climate Model Intercomparison Project ensemble. The comparison is performed for the Mediterranean region, and for the 1971–2005 period, when results from the “historical” scenario can also be compared with two data sets of ground‐based cloud observations. We work with 14 modeling results (resolution, 0.11° × 0.11°), which are a combination of five GCMs and five RCMs. In general, RCMs improve only very slightly the climatic estimation of TCC when compared with observations. Indeed, not all RCMs behave the same, and some indicators (monthly evolution of the relative bias) show an enhancement, while other indices (overall mean bias and annual range difference) improve only very slightly with respect to GCMs. Changes in the estimate of TCC in summer might be the most relevant value added by RCMs, as these should describe in a more proper way several mesoscale processes, which play a more relevant role in summer. Noticeably, RCMs are unable to capture the observed decadal trend in TCC. Thus, TCC simulated by RCMs is almost stable, in contradiction with observations and GCMs, which both show statistically significant decreasing trends in the Mediterranean area. This result is somewhat unsatisfactory, as if RCMs cannot reproduce past trends in TCC, their skill in projecting TCC into the future may be questioned.