چكيده به لاتين
Weather is an important factor in quantities of wind advection. Reliable estimates and appropriate resolution of surface wind on the sea in many scientific and economic activities is required. In this study, simulation and evaluation of surface wind fields prediction of 4.2 WRF in the Caspian Sea region is done. All simulations were performed for 9 configured differently in terms of physical parameterization. Evaluation of simulation results using data from synoptic stations available as well as data from a buoy in deep waters of the Caspian Sea in two periods of 10 days on behalf of both winter and summer in a gust of wind and the storm is done. To evaluate the performance of numerical models of statistical measures such as Willmott agreement index, root mean square error and bias error was used. The results show that the assessment buoy data model performance results have been good and acceptable. Simulations for offshore areas that have been evaluated using measurement data buoy observations are closer to the values of the synoptic data. The results of performance of the model near the coastal strip also is of acceptable quality but lower quality results in open waters and offshore areas are. The horizontal resolution can also be effective in achieving the results. It's expected by increasing the horizontal resolution, the results also improved to areas near the coast. Results of Physical ensemble (5), which is based on the physics of planetary boundary layer physics and surface layer physics with the local approach and nonlocal - sample physics ( MYNN ) as well as the Noah - mp surface model , has been better than the other ensembles in most of the time periods , especially for the winter .Results for winter generally have greater solidarity in measured wind speed and direction parameters over the summer, likely due to more accurately input data model Weather is an important factor in quantities of wind advection. Reliable estimates and appropriate resolution of surface wind on the sea in many scientific and economic activities is required. In this study, simulation and evaluation of surface wind fields prediction of 4.2 WRF in the Caspian Sea region is done. All simulations were performed for 9 configured differently in terms of physical parameterization. Evaluation of simulation results using data from synoptic stations available as well as data from a buoy in deep waters of the Caspian Sea in two periods of 10 days on behalf of both winter and summer in a gust of wind and the storm is done. To evaluate the performance of numerical models of statistical measures such as Willmott agreement index, root mean square error and bias error was used. The results show that the assessment buoy data model performance results have been good and acceptable. Simulations for offshore areas that have been evaluated using measurement data buoy observations are closer to the values of the synoptic data. The results of performance of the model near the coastal strip also is of acceptable quality but lower quality results in open waters and offshore areas are. The horizontal resolution can also be effective in achieving the results. It's expected by increasing the horizontal resolution, the results also improved to areas near the coast. Results of Physical ensemble (5), which is based on the physics of planetary boundary layer physics and surface layer physics with the local approach and nonlocal - sample physics ( MYNN ) as well as the Noah - mp surface model , has been better than the other ensembles in most of the time periods , especially for the winter .Results for winter generally have greater solidarity in measured wind speed and direction parameters over the summer, likely due to more accurately input data model is in winter than in summer. The model input data is taken from the output of the global ECMWF model and the difference in the error rate in these two seasons is related to the accuracy of the initial input condition data to the model. The results are based on the influence of local parameters. is in winter than in summer. The model input data is taken from the output of the global ECMWF model and the difference in the error rate in these two seasons is related to the accuracy of the initial input condition data to the model. The results are based on the influence of local parameters.
