Using an existing dataset of high-quality offshore wind-profile measurements from a research cruise over the Gulf of Maine, a NOAA research team completed an offshore wind-energy project, Position of Offshore Wind Energy Resources (POWER). POWER investigated the vertical structure and the spatial and temporal variability of winds aloft in the turbine rotor layer of the marine boundary layer, and determined the ability of numerical weather prediction (NWP) forecast models to simulate these wind properties.
The goal of the project was to enable better predictions of offshore wind resources to inform site selection for offshore wind farms. Specifically, this project focused on the Northeast Atlantic coastal region and relied on data from the 2004 New England Air Quality Study and wind speed predictions from NOAA’s Rapid Refresh (RAP) system and a new hourly-updated version of the North America Mesoscale forecast system (NAM) known as the NAM Rapid Refresh (NAMRR).
The two models were run at both normal resolution ('parent' model) and nested down to high-resolution versions (called High-Resolution Rapid Refresh or HRRR and the NAMRR CONUS nest). POWER also helped provide the impetus for the development of the NAMRR system, which will become a vital piece of the National Centers for Environmental Prediction (NCEP) model suite that eventually includes a rapidly updating, multi-model ensemble.
The measurements were taken on a cruise to study air quality off the coast of New England in July-August 2004, during the New England Air Quality Study (NEAQS). The instrument used to make the rotor-layer measurements was NOAA’s High-Resolution Doppler Lidar (HRDL), mounted on the deck of the R/V Ronald H Brown (RHB). To produce accurate measurements of the wind vector, the HRDL was equipped with a sophisticated custom-built motion-compensation system to remove the effects of ship motions (see Pichugina et al. 2012).
Another key instrument data set for this project was a network of 11 inland wind-profiling radars (WPRs) (Strauch et al., 1984; Wilczak et al., 1996) used for NEAQS during the summer of 2004 and one WPR located on the RHB (Wolfe et al., 2007).
Direct, high-quality measurements of winds aloft through the rotor layer over the ocean are rare. Most studies rely on near-surface measurements extrapolated upward or NWP model output, although it is not known how near-surface or model winds relate to actual winds aloft, because of the lack of measured data for comparison. The NEAQS ship-borne HRDL dataset provides high-quality profiles through the rotor layer, which can in turn provide information about the structure, variability, and ability to model offshore winds aloft, as well as whether surface or modeled winds can be used with confidence to infer rotor-level wind behavior.
What makes the NEAQS dataset especially valuable is the availability of a network of wind-profiling radars (WPR’s) deployed along the northeastern U.S. coastline—and one aboard the RHB. This mesoscale array allows the monitoring of larger-scale wind properties and evolution, the verification of model winds aloft up to 4 km above sea level, and the assimilation of wind profiles into the forecast models. An important aspect of this study is evaluating the impact of assimilated WPR data on the initialization and predictions of the models out to lead times of 18 hr, verified against the rotor-level winds over the ocean on the RHB.
A further important aspect of the POWER project was the availability of 2 years’ worth of model output from a new developmental version of the HRRR model, which ingests real-time data from a variety of asynchronous data sources, such as commercial aircraft, weather radar, and other available surface and profile measurements. This archive was used to perform a 2-yr composite of mean wind speed and direction that included the area offshore of the northeastern U.S.
These 2-yr composite wind-resource maps, combined with the results of the NEAQS measurements, were used to determine the best design for measurement arrays to sample the offshore wind resource. The strongest gradients in wind speed near the shore are in the cross-shore (or shore-perpendicular) direction. Significant variations were also evident in the along-shore (or shore-parallel) direction in both the measured and modeled results. This study presents several options for sampling arrays that address both cross-shore and along-shore variability.
Key findings of project were that NOAA Weather Prediction Models can fairly accurately predict offshore winds but they have a slow bias, which means that the models tend to underestimate wind speeds; incorporating additional data can improve the forecasts for a several-hour time period; the model-predicted variability in offshore wind speeds was consistent with observed wind measurements; and key factors related to offshore wind variability were unknown and should be measured.
The report also recommended the installation of an offshore wind-energy measurement network, consisting of an array of offshore buoys with wind-profiling sensors. These sensors, together with mobile-sensing platforms such as ships and aircraft, would increase understanding of offshore wind variability and pinpoint areas of recurring stronger and weaker flows.
- NOAA Study to Inform Meteorological Observation for Offshore Wind Positioning of Offshore Wind Energy Resources (POWER)
- Pichugina, Y.L., R.M. Banta, W.A. Brewer, S.P. Sandberg, and R.M. Hardesty, 2012: Doppler-lidar-based wind-profile measurement system for offshore wind-energy and other marine-boundary-layer applications. J. Appl. Meteor. Climatol., 51, 327-349. doi:10.1175/JAMC-D-11-040.1
- Assessment of Offshore Wind Energy Resources for the United States, Marc Schwartz, Donna Heimiller, Steve Haymes, and Walt Musial, Technical Report NREL/TP-500-45889 June 2010, http://www.nrel.gov/docs/fy10osti/45889.pdf