Mesoscale Structure

of a thesis presented to the Faculty of the University at Albany, State University of New York in partial fulfillment of the requirements for the degree of Master of Science College of Arts and Sciences Department of Earth and Atmospheric Sciences Matthew D. Greenstein 2006 ABSTRACT While forecasters can predict likely areas of precipitation, problems remain inWhile forecasters can predict likely areas of precipitation, problems remain in correctly anticipating mesoscale precipitation patterns within those areas. In northeastern United States snowstorms, precipitation takes on numerous patterns, or modes, in radar reflectivity imagery, e.g., relatively uniform, fractured, and banded. Better forecasts of these mesoscale characteristics would allow for enhanced prediction of snowfall amount and variability and for the differentiation between high-impact and low-impact snows. Twenty “heavy snow” events in the Northeast from the winters of 2002–03 through 2004–05 are selected for analysis using several criteria based on precipitation type, snowfall, time of year, and location. High-resolution WSI Corporation NOWrad composite reflectivity radar mosaics are used to identify five main precipitation modes, or mesoscale patterns, among the cases. The NCEP North American Regional Reanalysis is used to create plan-view maps and cross sections in order to ascertain which aspects of the ingredients—lift, instability, moisture, and microphysics—can assist in distinguishing the observed precipitation modes. Ultimately, it is shown that the frontogenesis pattern and conditional instability help to differentiate between the five modes. Some aspects of the frontogenesis pattern that accomplish this goal are its strength, steepness, and whether it is surface-based. Notably absent from a discussion of the distinguishing features is weak moist symmetric stability, which appears to be a favorable condition for heavy snow but is not a precipitation mode-distinguishing parameter. Lastly, conceptual models and a flowchart are developed that a forecaster can use operationally to improve the mesoscale forecast of these heavy snow events.