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Validation of Offshore Winds in the ERA5 Reanalysis and NREL NOW-23 WRF Analysis Using Two Floating Lidars in the New York Bight

  • Stony Brook University

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Low-level winds from two numerical weather prediction (NWP) model analyses are evaluated offshore at two floating lidars over the New York Bight (NYB) region. These analyses are often used to evaluate models and for offshore wind resource assessments, so it is important to understand the errors and the environmental conditions and flow patterns associated with these errors. Lidar winds in the lowest 20–200 m MSL are used to validate the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) (ERA5) and the National Renewable Energy Laboratory’s 2023 National Offshore Wind (NOW-23) wind product. Validation is performed for different seasons, months, and times of day, as well as different marine atmospheric boundary layer (MABL) stabilities and for large wind speed error events during the warm season. Both the ERA5 and NOW-23 struggle to accurately predict the lower MABL winds (i.e., speed and speed shear), especially during the warm season, and they significantly differ in the sign of and temporal nature of wind speed errors. MABL stability and model depiction of MABL mixing are one of the most important factors influencing the magnitude and sign of wind errors. Stable MABLs are associated with significantly large model analysis underestimations in wind speed and vertical shear. ERA5 and NOW-23 tend to perform better under near-neutral to unstable conditions, but ERA5 can overestimate winds under these conditions. This work has implications for future offshore wind energy assessment and marine forecasting in the NYB.

Original languageEnglish
Pages (from-to)1307-1323
Number of pages17
JournalWeather and Forecasting
Volume40
Issue number8
DOIs
StatePublished - Aug 2025

Keywords

  • Marine boundary layer
  • Model comparison
  • Model errors
  • Model evaluation/performance
  • Wind

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