Simulation of the surface wind field by the WRF model in Oman Sea region with different initial and boundary conditions

Oman Sea and its coastlines have an important role in the international trade, coastal management and marine industries. Large weather instability and intense wind occur in Oman Sea due to tropical cyclones. The wind field simulated by atmospheric models can be used in ocean model for wave prediction. The main purpose of this research is to investigate applicability of WRF mesoscale model version 3-7-1 in surface wind simulation using various boundary and initial conditions over Oman Sea. for this aim, three data sets including Era-Interim reanalysis data, FNL and GFS analysis data have been used. Simulated wind at the coasts of Oman has been evaluated using observational data measured at synoptic stations in Iran and Oman and also data measured by buoy at Gheshm Island. Evaluation of simulated offshore wind has been done using data from National Climatic Data Center Blended Sea Winds with 0.25 degree horizontal resolution and 6-hourly time step. Moreover, SST data from NCEP dataset with 0.083 degree in horizontal resolution have been used as WRF input data. Model outputs have been improved based on nudging technique. In this research, WRF model has been run using three 3-, 9- and 27-km nests, that the smaller one covers Oman Sea and some portions of the Persian Gulf. The model has been run for a time of 60 hour with 12 hour spin-up period for June 2009. Finally, fifteen “2-day re-started” simulations were performed to complete one month simulations. Results show that all three simulations overestimate wind speed at the considered coast area and the largest error belong to simulations that used Era-Interim dataset and the smallest error occurred in simulations that used FNL dataset. Comparison of the three datasets (analysis and reanalysis ones) with observational data indicated that using GFS dataset provided more accurate data due to its higher resolution. Moreover, ECMWF datasets underestimated them, while simulations using ECMWF them data as initialization and boundary conditions overestimated the winds.
Bias-averaged values over the offshore areas demonstrated that using GFS and FNL datasets leads to underestimation, while using Era-Interim dataset resulted in overestimation in of predicted winds. Histogram of wind speed reveals that maximum error occurred for low wind speed for all three datasets (wind speed smaller than 3 m/s). In the mid-range (wind speed between 3-12 m/s), the model has an appropriate performance for simulating wind speed. Using GFS and FNL underestimates wind speed larger than 12 m/s, while using Era-Interim data overestimates that. Simulations using GFS and FNL have little discrepancy for various wind speeds, due to same model in producing these datasets. While results obtained from Era-Interim differ significantly with those from GFS and FNL datasets. Using FNL dataset produced the least error in wind direction. Since both GFS and FNL datasets are produced in NCEP with the same data assimilation techniques and forecast systems, the significant difference between these two datasets refers to the number of used observational data in producing analysis dataset (more observational datasets have been used in producing FNL dataset, comparing with those used in producing GFS dataset). Therefore, it can be concluded that dense grid of observational data in producing analysis dataset has an important role in mesoscale simulations. As a conclusion, using FNL dataset an input of WRF model led to the best performance in simulation of wind speed and wind direction for coasts and offshore part of Oman Sea.