Determining Synoptic Air Mass Modifications for Advance Health-Effect Preparedness

As air masses move through the atmosphere, they inherit the characteristics of both the ambient air that they move through, as well as the properties of the surface they advect over. Due to the motion of said air masses, they become modified, both in temperature and moisture content. It is advantageous to trace how these air masses are modified spatially and temporally from their sources, as specific air masses have been found to be detrimental to human health with respect to the season. The goal of this project was to develop the methodology to create an automated model that will incorporate specific upper and lower level meteorological variables.

The Spatial Synoptic Classification System (SSC) will be employed to classify air masses into one of six types, plus a transition, during warm season (May-September) events. Five cities have been selected as target locations (Wilmington, DE, Raleigh-Durham, NC, Huntsville, AL, Lexington, KY and Oklahoma City, OK). These were chosen as they have readily available SSC data and are located eastward enough that air parcels will track over land for a suitable duration before ending at the target location. Using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, back trajectories from these target regions will be computed from Eta Data Assimilation System (EDAS) reanalysis data. A multinomial logistical regression model was then to incorporate both upper-air variables from HYSPLIT, surface moisture characteristics, and stability metrics to better understand how and why the air masses changed along their paths from source to target. The results provide a physical narrative and discussion of the air mass modification results, and the potential for more advanced and accurate predictions of incoming of air masses.

Predictive values for air mass modification have been calculated and the study has produced the ability to somewhat accurately discern which air mass will move into a target location four days before the event's occurrence. This knowledge becomes immensely useful when forecasting for harmful air masses moving into a region. Extreme temperatures, in particular, are associated with the highest mortality numbers, which make the presence of Dry Tropical (DT) and Moist Tropical Plus (MT+, MT++) air mass types hazardous for at-risk groups such as the extremely active, the very young, and the elderly. On the other hand, Dry Polar (DP) air masses have been shown to produce spikes in morbidity (namely, influenza) and mortality in certain populations. If these hazardous air masses can be accounted for multiple days ahead of time, policy changes can be implemented to provide more advanced alerts and planning for the public, which will further protect those most at risk to the oppressive air masses from significant health consequences.