Observational Classification and Climatology of Nocturnal Low-Level Jets in the Mid-Atlantic Region of the United States

The Mid-Atlantic region of the U.S. is known to host nocturnal low-level jets (referred to hereafter as NLLJ). These NLLJ events contribute to the advection and/or vertical mixing of boundary layer pollutants during the nighttime. This greatly affects not only the air quality of the stable nocturnal layer but can also influence the chemical budget of the next day. Previous studies on the NLLJ have resulted in an important conceptual understanding of NLLJ physical features, identification, and influence on chemical budgets. Zhang et al. (2005) and Delgado et al. (2013) identified the NLLJ as a shallow layer of fast-moving air in the residual layer of at least 2 m s^-1 greater than winds at the levels above and below the maximum, or “nose,” with flow parallel to the Appalachian Mountains (i.e., perpendicular to the sloping terrain). With Zhang et al. (2005) attributing its key mechanisms for development are the diurnal heating and cooling of the surface across the sloped terrain in the presence of light synoptic flow. These events have been observed with strength great enough to transport pollutants over mesoscale distances in a single night. Their mixing potential also being a prominent mechanism for introducing residual layer pollutants into the stable nocturnal layer, causing increases in surface concentrations of pollutants, especially ozone.

That withstanding, NLLJs in this region have been understudied compared to their importance in understanding and quantifying the concomitant pollutant distribution of pollutants in the Mid-Atlantic states. This study has established an observation-based classification and climatology of jet events using the record of wind profiles in the Maryland Department of Environment’s (MDE) radar wind profiler (RWP) network. This analysis has leveraged the available datasets with machine learning techniques to present key trends in NLLJ events for the benefit of furthering our understanding of societal impacts and predictive capability.