Patterns of Heavy rainfall in the Mid-Atlantic Region

An examination of the patterns associated with heavy rainfall in the Mid-Atlantic region is presented. Heavy rain events were determined using two separate databases including the 24-hour cooperative observer reports and the 32-km precipitation data in the North American Regional Re-analysis data (NARR). Both data sets provided similar dates and identified two distinct heavy rainfall patterns. The NARR, with 3-hourly resolution facilitated defining periods of heavy rainfall and identifying heavy rain at discrete time intervals. The focus here will be on 12, 24 and 36 hour events.

Using the individual heavy rain events, composites were produced using both the NARR and the global re-analysis data. The global data facilitated the evaluation of pre-1979 events. The compositing process was produced on the two identifiable types including the Maddox frontal and the Maddox synoptic event types. The Maddox synoptic events dominated the heavy rain patterns in the cool season, the relatively sharp north-south frontal systems with strong southerly flow in the warm sector were the primary features used to identify these events. The low-level southerly jet and precipitable water anomalies made these events readily identifiable.

The heaviest rainfall events were observed in June and from late August to early October. The remnants of tropical cyclones interacting with north-south frontal bands accounted for the majority of the heaviest rain events over the region. These events were classified as tropical-synoptic events as they typically were observed with north-south fronts and the remnants of a tropical system.

In this paper, the composite of each event type is presented along with the key features which can be used as predictors. The emphasis is on the standardized anomalies associated with the key features such as low-level v-winds, PW, and mean-sea level pressure patterns and anomalies. These features can be applied to forecast and ensemble guidance to aid in predicting heavy rainfall event visually and from applying predictive equations to the values of the forecast anomalies.