The High-Resolution Rapid Refresh (HRRR) model is run operationally by the National Oceanic and Atmospheric Administration to provide high-resolution short-range forecasts for the continental United States. The evaluation of the HRRR model off of the U.S. coasts has been challenged by the lack of suitable continuous profile observations in the marine boundary layer in the past. State-of-the art remote sensing instruments were recently deployed along the coast of New England in the northeastern United States for the multi-year Third Wind Forecast Improvement Project and provide a unique opportunity for the evaluation of temperature and wind in the marine boundary layer in the HRRR model. We used 1 year of data at three sites, two of which were on islands, to document the seasonal characteristics of the marine boundary layer and its representation in the HRRR model for different forecast hours. Overall, the HRRR model captured the seasonal and diurnal evolution of temperature and wind very well. However, low-level horizontal wind shear and static stability were too weak in the model, especially during the warmer months, which might be partly linked to errors in sea surface temperature. Low-level jets (LLJs) occurred in approximately 20 % of the hourly profiles with a maximum frequency during spring and summer. Up to 60 % of the LLJ profiles during peak seasons were correctly predicted, using the critical success index as a measure. Systematic model errors in wind and temperature were found during LLJs, when the HRRR model frequently underestimated wind speed at nose height and shear below nose height, often accompanied by static stability that was too weak. These errors resulted in low-level Bulk Richardson numbers that were consistently too large at all three sites, indicating an overestimation of dynamic stability in the boundary layer in the model. Such systematic errors in low-level wind shear and stability were largely absent during correct rejections, that is, when an LLJ was neither observed nor simulated, indicating that LLJs were responsible for a large part of the model errors.