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

Denis E.K Dzebre,Muyiwa S Adaramola

doi:10.3390/en12193670

Abstract

This paper examines the impacts of five planetary boundary layer (PBL) parameterization schemes paired with several compatible surface layer (SL) parameterization schemes in the Weather Research and Forecasting Model on wind hindcasts for resource assessment purposes in a part of Coastal Ghana. Model predictions of hourly wind speeds at 3 × 3 km2 and 9 × 9 km2 grid boxes were compared with measurements at 40 m, 50 m, and 60 m. It was found that the Mellor-Yamada Nakanishi and Niino Level 3 (MYNN3) PBL scheme generally predicted winds with a relatively better combination of error metrics, irrespective of the SL scheme it was paired with. When paired with the Eta surface layer scheme, it often produced some of the relatively fewest errors in estimated mean wind power density (WPD) and Weibull cumulative density. A change in the simulation grid size did not have a significant impact on the conclusions of the relative performance of the PBL-SL pairs that were tested. The results indicate that the MYNN3 PBL and Eta SL pair is probably best for wind speed and energy assessments for this part of coastal Ghana.

Highlights

Over the years, there has been increasing interest in the use of numerical weather prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model [1], for wind resource assessment
It can be seen from the error metrics that the impacts of all the tested planetary boundary layer (PBL)-surface layer (SL) pairs on model performance were mostly within the acceptable limits: root mean square error (RMSE) < 2 m/s, mean error (ME) < 0.5 m/s
It can be observed that for the same PBL scheme, the choice of an SL scheme did have some impact on the average wind speed prediction, the impact was less on the error metrics

Summary

Introduction

There has been increasing interest in the use of numerical weather prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model [1], for wind resource assessment. By numerically downscaling meteorological datasets, these models are used to generate wind data (wind speeds and directions) at relatively low cost for areas lacking ground measurements of such data for preliminary assessments of wind resources. Predictions of surface winds by NWP models such as WRF are sensitive to model options such as simulation grid size, model physics, initial and boundary data, and parameterization of processes at the subgrid scale [2,3]. This paper focuses on selected parameterization options in the Advanced Research WRF (ARW).

Methods

Results

Conclusion