A sensitivity study of Surface Wind simulations over Coastal Ghana to selected Time Control and Nudging options in the Weather Research and Forecasting Model

Abstract

Over the years, the Weather Research and Forecasting Model (WRF) has been gaining popularity as a low-cost alternative source of data for wind resource assessments. This paper investigates the impact of selected time control, and nudging options on wind simulations in WRF. We conducted 15 numerical experiments, combining 5 simulation run-times and 3 options for disabling nudging in the Planetary Boundary Layer (PBL) in WRF. Hourly wind speed and direction predictions were compared with actual measurements at 40 m, 50 m and 60 m a.g.l. From our results, we recommend that, for optimum performance, the method of disabling nudging in the PBL should be chosen with simulation run times in mind. For wind simulations in our study area, up to 2 days run-times with nudging disabled below 1600 m in model configurations gives the best wind speed predictions. However, disabling nudging below the model-calculated PBL height offers more consistent results and produces relatively less prediction error with longer run times.

Impact of Selected Options in the Weather Research and Forecasting Model on Surface Wind Hindcasts in Coastal Ghana

“…GBM [27] The selected PBL parameterization schemes were paired with compatible SL schemes as recommended by [31,48] (except the old MM5 scheme). Other required parameterization schemes were selected based on other wind sensitivity studies in coastal Ghana [49,50], and practices from other wind sensitivity studies (mostly in the tropics) [17][18][19][20][21]. The Eta Microphysics, New Rapid Radiative Transfer Model [37] and Dudhia [38] schemes were used for MP, LW-Rad, and SW-Rad parameterizations, respectively.…”

“…All three simulation domains were nudged during all the simulations, following practices of previous studies [20,52,53]. Nudging options and simulation lengths were chosen based on recommendations from a previous study in coastal Ghana [49].…”

“…Post-processing of results generally followed the procedure used in previous studies in the study area [49,50]. However, hourly predictions of winds (at 10 m and other relevant half vertical levels) were bilinearly interpolated to the mast location.…”

“…Furthermore, winds were interpolated to the heights of analysis (40 m, 50 m, and 60 m), with log-linear interpolation [54]. Scheme performance was assessed in terms of four error metrics, which were calculated with procedures from previous studies [49,50]: mean error (ME), root mean square error (RMSE), standard deviation of the error (STDE), as well as the correlation coefficient (CC) of the predictions. The error metrics were combined into a prediction skill score (SS) (as was done in previous studies [49,50]), which was used to rank the options.…”

“…Scheme performance was assessed in terms of four error metrics, which were calculated with procedures from previous studies [49,50]: mean error (ME), root mean square error (RMSE), standard deviation of the error (STDE), as well as the correlation coefficient (CC) of the predictions. The error metrics were combined into a prediction skill score (SS) (as was done in previous studies [49,50]), which was used to rank the options. In addition, the ME, RMSE, and CC were compared to values that were considered as indicators of good model performance in studies [18,19,55].…”