Abstract
The Weather, Research and Forecasting (WRF) model includes a multitude of physics parameterizations to account for atmospheric dynamics and interactions such as turbulent fluxes within the planetary boundary layer (PBL), long and short wave radiation, hydrometeor representation in microphysics, cloud ensemble representation in cumulus, amongst others. A sensitivity analysis is conducted in order to identify the optimal WRF-physics set-up and impact of temporal resolution of re-analysis dataset for the event of sudden changes in wind direction that can become challenging for reliable wind energy operations. In this context, Storm Ciara has been selected as a case study to investigate the influence of a broad combination of different interacting physics-schemes on quantities of interest that are relevant for energy yield assessment. Of particular relevance to fast transient weather events, two different temporal resolutions (1-hourly and 3-hourly) of the lateral boundary condition's re-analysis dataset, ERA5, are considered. Physics parameterizations considered in this study include: two PBL schemes (MYNN2.5 and scale-aware Shin Hong PBL), four cumulus schemes (Kain-Fritsch, Grell-Devenyi, and scale-aware Grell-Freitas and multi-scale Kain-Fritsch,) and three microphysics schemes (WSM5, Thompson and Morrison) coupled with two geospatial configurations for WRF simulation domains. The resulting WRF predictions are assessed by comparison to observational RADAR reflectivity data on precipitation. In addition, SCADA data on wind direction and wind speed from an offshore wind farm located in the Belgian North Sea is considered to assess modeling capabilities for local wind behavior at farm level. For precipitation, results are shown to be very sensitive to model setup, but no clear trends can be observed. For wind-related variables on the other hand, results show a definite improvement in accuracy when both scale-aware cumulus and PBL parameterizations are used in combination with 1-hourly temporal resolution reanalysis data and extended domain sizes.
Highlights
20 Extreme weather phenomena such as low-level jets, sudden changes in wind direction, extreme wind shear (Kalverla et al, 2017; Aird et al, 2021), wind ramps (Gallego-Castillo et al, 2015) and storms (Solari, 2020) are capable of causing severe dynamic loading on wind turbine components (Negro et al, 2014; AbuGazia et al, 2020; Chi et al, 2020)
With the addition to RADAR data and the availability of operational wind farm data (SCADA), the premise of this study provides a unique opportunity to investigate storm Ciara as felt by the Belgian offshore wind farms and formulate an informed decision on the optimum WRF set-up in the context of wind energy applications
Case 12 encompasses scale-aware Shin-Hong planetary boundary layer (PBL) scheme coupled with a double moment 6-class Morrison microphysics scheme, a scale-aware GF cumulus scheme and ERA5 1h reanalysis dataset as the lateral boundary conditions
Summary
20 Extreme weather phenomena such as low-level jets, sudden changes in wind direction, extreme wind shear (Kalverla et al, 2017; Aird et al, 2021), wind ramps (Gallego-Castillo et al, 2015) and storms (Solari, 2020) are capable of causing severe dynamic loading on wind turbine components (Negro et al, 2014; AbuGazia et al, 2020; Chi et al, 2020). Cipitation associated to these phenomena can lead to early blade degradation through leading-edge erosion (Law and Koutsos, 2020). As such, these extreme weather events (EWE) play a significant role in the wind turbine’s operational lifetime and must 25 be considered at design stage to ensure that ultimate loads are not exceeded and fatigue requirements are met. These extreme weather events (EWE) play a significant role in the wind turbine’s operational lifetime and must 25 be considered at design stage to ensure that ultimate loads are not exceeded and fatigue requirements are met Such events may cause sudden changes in power production leading to grid imbalance and economic losses. Predictions are found to be highly sensitive on the selection of these sub-grid scale models, the location and the type of weather event, the lateral boundary conditions used to drive the flow, and the simulation domain configuration
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