Quantifying sensitivity in numerical weather prediction-modeled offshore wind speeds through an ensemble modeling approach
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- The State University of New Jersey, New Brunswick, NJ (United States)
A decade of research has shown that numerical weather prediction (NWP)-modeled wind speeds can be highly sensitive to the inputs and setups within the NWP model. For wind resource characterization applications, this sensitivity is often addressed by constructing a range of setups and selecting the one that best validates against observations. However, this approach is not possible in areas that lack high-quality hub height observations, especially offshore wind areas. In such cases, techniques to quantify and disseminate confidence in NWP-modeled wind speeds in the absence of observations are needed. We address this need in the present study and propose best practices for quantifying the spread in NWP-modeled wind speeds. We implement an ensemble approach in which we consider 24 different setups to the Weather Research and Forecasting (WRF) model. We construct the ensemble by considering variations in WRF version, WRF namelist, atmospheric forcing, and sea surface temperature (SST) forcing. Our analysis finds that the standard deviation produces more consistent estimates compared to the interquartile range and tends to be the more conservative estimator for ensemble variability. We further find that model spread increases closer to the surface and on shorter time scales. In conclusion, we explore methods to attribute total ensemble variability to the different ensemble components (e.g., atmospheric forcing and SST product) and find that contributions by components also vary depending on time scale. We anticipate that the methods and results presented in this paper will provide a reasonable foundation for further research into ensemble-based wind resource data sets.
- Research Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE); Rutgers Univ., New Brunswick, NJ (United States)
- Grant/Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1769802
- Report Number(s):
- NREL/JA--5000-77555; MainId:27491; UUID:d3dfdfd5-3bea-4be9-a4d7-18965f9d7fae; MainAdminID:18596
- Journal Information:
- Wind Energy, Journal Name: Wind Energy Journal Issue: 9 Vol. 24; ISSN 1095-4244
- Publisher:
- WileyCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Assessing boundary condition and parametric uncertainty in numerical-weather-prediction-modeled, long-term offshore wind speed through machine learning and analog ensemble
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journal | November 2021 |
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