Tuesday, 30 June 2015: 11:45 AM
Salon A-5 (Hilton Chicago)
On 13 August 2014, a new all-time New York State 24-h precipitation record was set when 345 mm (13.57) of rain fell at Islip, New York on Long Island (LI). This record-breaking rainfall induced high-impact flooding over parts of LI. This flooding was a direct result of the persistent training of precipitation cells in a mesoscale band of intense convection. Prior to this event, heavy rainfall was forecasted across the northeastern United States in conjunction with an amplified upper-level trough that established poleward flux of deep moisture and provided synoptic-scale forcing for widespread rainfall. However, the location and magnitude of the heaviest rainfall over LI was poorly predicted, even at short forecast lead times. One hypothesis for this low predictability is linked to mesoscale factors governing the organization of the convective band associated with the LI flooding. Lower-resolution, convection-parameterized models often poorly represent these mesoscale features, which often limit the predictability of extreme precipitation events such as the LI flash flood.
This study uses simulations from a higher-resolution, convection-resolving Weather Research and Forecasting (WRF) ensemble to diagnosis mesoscale factors that favored or inhibited heavy rainfall over LI. WRF members are stratified by their total precipitation accuracy over LI and adjacent land. A comparison of the most and least accurate WRF members shows that accurate WRF members depict strong, persistent mesoscale forcing along the LI coastline. This mesoscale forcing aids in convective band organization along a quasi-stationary surface boundary. WRF members that fail to produce an organized convective band over LI also fail to depict this critical surface boundary. The results from this study demonstrate that the location and intensity of heavy rainfall over LI is dependent on accurate representation of these mesoscale features.
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner