Validation of RU-WRF, the Custom Atmospheric Mesoscale Model of the Rutgers Center for Ocean Observing Leadership

Optis, Mike; Kumler, Andrew; Scott, G.; Debnath, Mithu; Moriarty, Patrick

doi:10.2172/1599576

Cited by 13 publications

(10 citation statements)

References 5 publications

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“…Similar in scope to the WIND Toolkit, the NEWA is a 30-year WRF-based data set covering all of Europe. NREL recently completed an offshore study of WRF model sensitivity in partnership with Rutgers Center for Ocean Observing Leadership(Optis et al 2020). Results from this study support previous research findings and found that the PBL scheme, reanalysis forcing, and SST forcings were all key drivers of model sensitivity, especially on short timescales.Based on this literature review and NREL's previous experience on WRF sensitivity in offshore wind resource modeling, a total of 16 model setups are constructed based on variations in four key model components.…”

supporting

confidence: 78%

Offshore Wind Resource Assessment for the California Pacific Outer Continental Shelf (2020)

Optis

Rybchuk

Bodini

et al. 2020

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supporting

confidence: 78%

Offshore Wind Resource Assessment for the California Pacific Outer Continental Shelf (2020)

Optis

Rybchuk

Bodini

et al. 2020

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“…The main data source is the network of buoy-based wind speed measurements from the National Data Buoy Center, maintained by the National Oceanic and Atmospheric Administration (National Data Buoy Center, 1971). These data have been used to characterize the wind resource in offshore California (Wang et al, 2019;Optis et al, 2020c), the US offshore Atlantic (Optis et al, 2020b), and the Great Lakes (Doubrawa et al, 2015). These buoys generally provide years worth of wind speed measurements at heights of less than 5 m and are of high quality.…”

Section: Introductionmentioning

confidence: 99%

New methods to improve the vertical extrapolation of near-surface offshore wind speeds

et al. 2021

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Abstract. Accurate characterization of the offshore wind resource has been hindered by a sparsity of wind speed observations that span offshore wind turbine rotor-swept heights. Although public availability of floating lidar data is increasing, most offshore wind speed observations continue to come from buoy-based and satellite-based near-surface measurements. The aim of this study is to develop and validate novel vertical extrapolation methods that can accurately estimate wind speed time series across rotor-swept heights using these near-surface measurements. We contrast the conventional logarithmic profile against three novel approaches: a logarithmic profile with a long-term stability correction, a single-column model, and a machine-learning model. These models are developed and validated using 1 year of observations from two floating lidars deployed in US Atlantic offshore wind energy areas. We find that the machine-learning model significantly outperforms all other models across all stability regimes, seasons, and times of day. Machine-learning model performance is considerably improved by including the air–sea temperature difference, which provides some accounting for offshore atmospheric stability. Finally, we find no degradation in machine-learning model performance when tested 83 km from its training location, suggesting promising future applications in extrapolating 10 m wind speeds from spatially resolved satellite-based wind atlases.

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“…The Rutgers University Center for Ocean Observing Leadership (RU-COOL) at Rutgers University runs a daily real-time version of WRF, called RU-WRF, which is tailored to the U.S. Mid and North Atlantic OSW energy [10], and has been continously evaluated and updated since then [16]. RU-WRF runs a parent nest at 9 km resolution out to 120 hours and a child nest at 3 km resolution out to 48 hours, generating hourly forecasts of multiple meteorological variables, which are listed in Table 1.…”

Section: Numerical Weather Predictions From Ru-wrfmentioning

confidence: 99%

“…One criticism often pointed at statistical and ML methods, however, is that they are, by and large, physics-agnostic, i.e., they are formulated with little consideration of the physics of wind field formation and propagation, and hence, may be susceptible to model specifications that violate those first principles. This has driven an active area of research in ML Figure 1: Two days of meso-scale NWPs from the RU-WRF meso-scale model [10], along with co-located wind speed observations. Both data and forecasts are obtained in proximity to the planned OSW energy areas in the NY/NJ Bight.…”

Section: Introductionmentioning

confidence: 99%

AIRU-WRF: A Physics-Guided Spatio-Temporal Wind Forecasting Model and its Application to the U.S. North Atlantic Offshore Wind Energy Areas

Ye¹,

Brodie²,

Miles³

et al. 2023

Preprint

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The reliable integration of wind energy into modern-day electricity systems heavily relies on accurate short-term wind forecasts. We propose a spatiotemporal model called AIRU-WRF (short for the AI-powered Rutgers University Weather Research & Forecasting), which fuses numerical weather predictions (NWPs) with local observations in order to make wind speed forecasts that are short-term (minutes to hours ahead), and of high resolution, both spatially (site-specific) and temporally (minute-level). In contrast to purely data-driven methods, we undertake a "physics-guided" machine learning approach which captures salient physical features of the local wind field without the need to explicitly solve for those physics, including: (i) modeling wind field advection and diffusion via physically meaningful kernel functions, (ii) integrating exogenous predictors that are both meteorologically relevant and statistically significant; and (iii) linking the multi-type NWP biases to their driving meso-scale weather conditions. Tested on real-world data from the U.S. North Atlantic where several offshore wind projects are in-development, AIRU-WRF achieves notable improvements, in terms of both wind speed and power, relative to various forecasting benchmarks including physics-based, hybrid, statistical, and deep learning methods.

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Validation of RU-WRF, the Custom Atmospheric Mesoscale Model of the Rutgers Center for Ocean Observing Leadership

Abstract: Department of Energy (DOE) reports produced after 1991 and a growing number of pre-1991 documents are available free via www.OSTI.gov.

Cited by 13 publications

References 5 publications

Offshore Wind Resource Assessment for the California Pacific Outer Continental Shelf (2020)

Offshore Wind Resource Assessment for the California Pacific Outer Continental Shelf (2020)

New methods to improve the vertical extrapolation of near-surface offshore wind speeds

AIRU-WRF: A Physics-Guided Spatio-Temporal Wind Forecasting Model and its Application to the U.S. North Atlantic Offshore Wind Energy Areas

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