Evaluation of the WRF model for simulating surface winds and the diurnal cycle of wind speed for the small island state of Fiji

Abstract

Evaluation of the performance of the WRF model is carried out for simulating the surface winds and the diurnal cycle of wind speed for the small island developing state of Fiji at a 1.33 km by 1.33 km grid resolution using 1deg gridded data from NCEP-FNL. Simulations are performed for an austral summer (January 2017) and an austral winter (July 2017) month using the dynamical downscaling and the two-way nested approach. A set of physics parameterization schemes together with topo_wind = 1, 2 and ysu_topdown_pblmix = 1 physics settings associated with YSU PBL scheme are used to correct the surface winds and the diurnal cycle of wind speed. The results reveal that the WRF model is able to capture the surface winds and the diurnal cycle of wind speed on the windward side. Surface winds on the leeward side and the outer islands, show positive bias especially at nighttime for January and at both the day and night time for July. The statistical evaluation of all stations for January (July) showed a bias of 1.16 m/s (1.89 m/s), RMSE of 2.40 m/s (3.14 m/s), STDE of 1.88 m/s (2.08 m/s) and diurnal cycle correlation of 0.74 (0.68) using topo_wind = 2 and ysu_topdown_pblmix = 1.

Convection‐permitting modeling with regional climate models: Latest developments and next steps

“…In another study, reduced wind speeds during heat waves and drought conditions were found, leading to potential implications for climate change studies (Jiménez et al, 2011). CPRCMs were used in different studies to evaluate wind energy potential over Iceland (Nawri et al, 2014), the United States (James et al, 2017), Hawaii (Argüeso & Businger, 2018), Fiji (Dayal et al, 2020(Dayal et al, , 2021, northeastern Spain (Fern andez-Gonz alez et al, 2018), northwestern Spain (Pr osper et al, 2019), andLesotho (D'isidoro et al, 2020). In a recent review on climate change impacts on wind power generation, Pryor, Barthelmie, Bukovsky, et al (2020) did not identify CPRCM climate change projections focusing on wind yet, but they emphasized their high potential in providing higher fidelity wind simulations.…”

“…The vast majority of studies using CPRCMs over islands have focused on rainfall (Love et al, 2011; Chan et al, 2013; Chan, Kahana, et al, 2018; Morel et al, 2014; Kendon et al, 2014; Fosser, Kendon, Stephenson, & Tucker, 2020; Dutheil et al, 2021) because it is directly affected by the way convection is represented in models. However, some experiments took advantage of convection‐permitting resolutions to study other variables such as temperature (Expósito et al, 2015; Zhang, Wang, et al, 2016a, 2016b), and wind (Nawri et al, 2014; Argüeso & Businger, 2018; Dayal et al, 2020, 2021) that also require fine spatial detail. Most studies agree that precipitation extremes are more realistic in CPRCMs (Kendon et al, 2012; Zhang, Wang, et al, 2016a; Dutheil et al, 2021), despite the fact that they tend to overestimate rainfall amounts compared to observations (Murata, Sasaki, Kawase, & Nosaka, 2017).…”

“…Modeling studies at convection‐permitting scales that span climate‐relevant periods (≥10 years) include the British Isles (Chan et al, 2013; Kendon et al, 2012, 2014, 2017; Fosser, Kendon, Chan, et al, 2020; Fosser Kendon, Stephenson, et al, 2020), Svalbard (Dobler, 2019; Dobler et al, 2020), Iceland (Nawri et al, 2014), Japanese Islands (Murata, Sasaki, Kawase, & Nosaka, 2017; Murata, Sasaki, Kawase, Nosaka, Aoyagi, et al, 2017), Hawaii (Zhang et al, 2012; Zhang, Wang, et al, 2016a, 2016b; Argüeso & Businger, 2018; Xue et al, 2020), Puerto Rico (Bhardwaj et al, 2018), Canary Islands (Expósito et al, 2015), Maritime Continent (Vincent & Lane, 2017, 2018), and New Caledonia (Dutheil et al, 2021). Other have focused on shorter periods, such as La Réunion (Morel et al, 2014), Cyprus (Zittis et al, 2017), Hainan (Zhu et al, 2017), Puerto Rico (Wootten et al, 2016), Fiji (Dayal et al, 2020, 2021), and Corsica (Barthlott et al, 2014). Overall, studies using CPRCMs have largely focused on the present climate to evaluate the model (Morel et al, 2014; Zhang et al, 2012; Zhang, Wang, et al, 2016a), determine the benefits of switching off convective schemes (Love et al, 2011; Kendon et al, 2014; Wootten et al, 2016; Murata, Sasaki, Kawase, & Nosaka, 2017; Zittis et al, 2017; Fosser, Kendon, Chan, et al, 2020; Argüeso et al, 2020) or investigate atmospheric processes such as the Madden‐Julian Oscillation (Birch et al, 2016; Vincent & Lane, 2016, 2018), gravity waves propagation (Ruppert Jr. et al, 2020), offshore convection propagation (Coppin & Bellon, 2019a, 2019b) and land‐sea breezes (Wei et al, 2020).…”

Prediction of Extreme Wind Speed for Offshore Wind Farms Considering Parametrization of Surface Roughness

“…Such parametric modelling and analysis can be readily coupled with TC risk models to estimate TC hazard risk [12]. For example, it costs three days for the 34-day Fiji island simulations when performing WRF on the New Zealand Science Infrastructure (NeSI) High-Performance Computer-Mahuika [13]. The computational costs of parametric and meteorological models differ by orders of magnitude.…”

A grid sensitivity study of the Weather Research and Forecasting (WRF) model for simulating surface winds over the small island state of Fiji

“…In a previous study for Fiji, the WRF model was evaluated for simulating the surface winds and the diurnal cycle of wind speed at a grid resolution of 1.33 km x 1.33 km against 19 Automatic Weather Stations (AWSs) [8]. The model was able to correctly capture the surface winds and the diurnal cycle of wind speed on the windward side (relative to the predominant SE winds) of the Fiji Group.…”