22 Large-amplitude Gravity Waves as an Unclassified Type of Storm

Monday, 22 October 2018
Stowe & Atrium rooms (Stoweflake Mountain Resort )
Anton Seimon, Appalachian State Univ., Boone, NC; and L. Bosart and J. H. Ruppert Jr.

The hazardous weather associated with mesoscale large-amplitude inertia–gravity waves (MGWs) includes rapid fluctuations in surface pressure, precipitation rate and thunderstorm activity, along with surface winds that may reach 20–40 m s-1. Their strong wind signatures constitute a storm by standard definition, yet MGWs remain unclassified as such. Along with their relative rarity (~1–3 events per year in the conterminous USA), the lack of formal designation contributes to MGWs remaining poorly understood by most meteorologists, and effectively unknown by the general public. Operational mesoscale forecast models have successfully predicted characteristic pressure and wind patterns for several recent events, and high-temporal resolution meteorological observation networks facilitate detection and monitoring. However, the uptake of such information by human forecasters is limited by insufficient MGW pattern recognition and knowledge. Substantial benefits could potentially be derived by 1) formally classifying MGWs as a storm type, 2) improving forecaster training, and 3) developing algorithms to detect characteristic MGW signals in operational mesoscale model output and observations.

We propose that a classification scheme be developed for MGWs based on physical characteristics derived from case studies of large-amplitude (surface pressure change ≥5 hPa <30 min) events, and a methodology implemented for operational predictions and warnings. Automated detection of characteristic MGW patterns in forecast models and observations can inform forecaster-based subjective interpretation and warning issuance of potentially hazardous MGW events. Algorithms applied to forecast and observational estimates of pressure, wind, and vertical velocity can identify the potential for MGW generation and propagation. Probabilistic diagnostics generated from mesoscale ensemble simulations and graphical outputs could offer forecasters objective guidance identifying spatiotemporal MGW characteristics and magnitude of potential hazards. Given the rarity of hazardous MGW events, such a system should operate unobtrusively and only activate for significant events.For cases where detection threshold values are exceeded, subjective forecaster interpretation should be applied in evaluation of model forecasts and real-time detection of observed candidate MGW disturbances. For the rare events where the MGW threat level is deemed significant, graphical products communicating the hazard in areas at risk could then be disseminated to media and the public.

Further research is still needed to advance the state of knowledge about cause and effect processes governing the occurrence and life cycles of large- amplitude MGWs. A critical science task remaining is to distinguish quantitatively the contributions of dry upper-level jet dynamics and moist convective dynamics to the formation, amplification, and evolution of large-amplitude MGWs.

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