Satellite Based Communication between Land Surface Temperature and Biophysical Variables in the Jazmourian Catchment

In this research, a deterministic forecast of 24, 48 and 72 hours of 10-meter wind speed has been produced over Iran, using BMA and EMOS methods for post-processing of raw output of ensemble systems. The main purpose of this article is to compare the deterministic forecasts obtained by using these two methods with each raw ensemble members and the mean of the raw ensemble members. The used ensemble system consists of eight different physical configurations, with changes in the boundary layer scheme of the WRF model. Other physical models in ensemble system are the same for all ensemble members. Each ensemble member includes 24, 48 and 72-hour forecasts of 10-meter wind speed with a resolution of 21 kilometers over Iran. GFS forecasts are used for the initial and boundary conditions, and the forecast start time is 12 UTC per day. Observation data of 31 synoptic meteorological stations located in the provincial capitals have been used and the corresponding values of the predictions on these stations have been interpolated by bilinear method. The model is run from 1 March to 31 August 2017, and the results from 11 April to 31 August 2017 are considered as the test period. After calculating the forecast errors with different training periods, 30 days are considered as the length of training period for prediction in both BMA and EMOS methods. Verification was performed by different methods (accuracy: PC, TS and OR; reliability and resolution: FAR, POFD and POD; skill: CSS, HSS, PSS, GSS and Q; statistical errors: RMSE and MAE) for 10-meter wind speed thresholds less than 3 and more than 5, 10 and 15 m/s for both methods in all forecast ages. The results show a 3 times improvement in accuracy scores, 2.2 times improvement in reliability and resolution scores, 3.4 times improvement in skill scores and 24% reduction in statistical error scores relative to the mean of ensemble members. Furthermore, the verification results for different climatic regions (cold, semi-arid, hot-dry, hot-humid and moderate-rainy climate) in the country separately showed that in all climates, RMSE measurement has the best performance for BMA and EMOS methods and reduces the error by 21% and 23% ,respectively. In hot and humid climates, compared to the mean of ensemble members errors, these two methods were more powerful to improve the prediction system. They reduced the error by 44% and 46%, respectively.