Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic

Abstract

Abstract. As offshore wind farm development expands, accurate wind resource forecasting over the ocean is needed. One important yet relatively unexplored aspect of offshore wind resource assessment is the role of sea surface temperature (SST). Models are generally forced with reanalysis data sets, which employ daily SST products. Compared with observations, significant variations in SSTs that occur on finer timescales are often not captured. Consequently, shorter-lived events such as sea breezes and low-level jets (among others), which are influenced by SSTs, may not be correctly represented in model results. The use of hourly SST products may improve the forecasting of these events. In this study, we examine the sensitivity of model output from the Weather Research and Forecasting model (WRF) 4.2.1 to different SST products. We first evaluate three different data sets: the Multiscale Ultrahigh Resolution (MUR25) SST analysis, a daily, 0.25∘ × 0.25∘ resolution product; the Operational Sea Surface Temperature and Ice Analysis (OSTIA), a daily, 0.054∘ × 0.054∘ resolution product; and SSTs from the Geostationary Operational Environmental Satellite 16 (GOES-16), an hourly, 0.02∘ × 0.02∘ resolution product. GOES-16 is not processed at the same level as OSTIA and MUR25; therefore, the product requires gap-filling using an interpolation method to create a complete map with no missing data points. OSTIA and GOES-16 SSTs validate markedly better against buoy observations than MUR25, so these two products are selected for use with model simulations, while MUR25 is at this point removed from consideration. We run the model for June and July of 2020 and find that for this time period, in the Mid-Atlantic, although OSTIA SSTs overall validate better against in situ observations taken via a buoy array in the area, the two products result in comparable hub-height (140 m) wind characterization performance on monthly timescales. Additionally, during hours-long flagged events (< 30 h each) that show statistically significant wind speed deviations between the two simulations, both simulations once again demonstrate similar validation performance (differences in bias, earth mover's distance, correlation, and root mean square error on the order of 10−1 or less), with GOES-16 winds validating nominally better than OSTIA winds. With a more refined GOES-16 product, which has been not only gap-filled but also assimilated with in situ SST measurements in the region, it is likely that hub-height winds characterized by GOES-16-informed simulations would definitively validate better than those informed by OSTIA SSTs.

On Predicting Offshore Hub Height Wind Speed and Wind Power Density in the Northeast US Coast Using High-Resolution WRF Model Configurations during Anticyclones Coinciding with Wind Drought

“…In terms of evaluating the model's accuracy in forecasting offshore wind, one of the key factors is the SST, as it has a direct impact on atmospheric stability conditions. Though WRF can use the SST input from the relatively coarse initialization data, previous studies like Redfern et al (2023) and Hawbecker et al (2022) [43,44] have performed simulations with external SST inputs, which have a higher resolution compared to the initial and boundary conditions (ICs and BCs). They have observed that the differences in both temporal and spatial resolution between SST products have an impact on wind speed characterization, with higher resolutions tending to enhance model performance.…”

The Effects of Wind Farm Wakes on Freezing Sea Spray in the Mid-Atlantic Offshore Wind Energy Areas

“…Constant time steps are set to 18 s and 6 s in the outer and inner domains, respectively. Initial and boundary conditions are also supplied by the hourly 30 km ERA5 dataset (Hersbach et al, 2020) are provided as SST by the UK Met Office Operational Sea Surface Temperature and Sea Ice Analysis dataset (Donlon et al, 2012) and show good agreement during validation against mid-Atlantic bight buoys (Redfern et al, 2023). Physics parameterizations include the MYNN2 planetary boundary layer and surface layer (Nakanishi and Niino, 2006), the Noah Land Surface Model (Niu et al, 2011), the New Thompson microphysics (Thompson et al, 2008), the rapid radiative transfer model for longwave and shortwave radiative transfer (Iacono et al, 2008), and the Kain-Fritsch Cumulus (Kain, 2004) schemes.…”

On predicting offshore hub-height wind speed and wind power density in the Northeast US coast using high-resolution WRF model configurations during anticyclones coinciding with wind drought

“…In terms of evaluating the model's accuracy in forecasting offshore wind, one of the key factors is SST, as it has a direct impact on atmospheric stability conditions. Though WRF can use the SST input from the relatively coarse initialization data, previous studies like Redfern et al (2023) and Hawbecker et al (2022), have performed simulations with external SST inputs which have higher resolution compared to the initial and boundary conditions (ICs and BCs). They have observed that the differences in both temporal and spatial resolution between SST products have an impact on wind speed characterization, with higher resolutions tending to enhance model performance.…”