Tropical cyclone (TC) landfalls over the U.S. mid-Atlantic region, which include the so-called Sandy-like, or westward-curving, tracks, are among the most infrequent landfalls along the U.S. East Coast. However, when these events do occur, the resulting economic and societal consequences can be devastating. A recent example is Hurricane Sandy in 2012. Multimodel ensemble seasonal hindcasts conducted with a high-atmospheric-resolution coupled prediction system based on the ECMWF operational model (Project Minerva) are used here to compile the statistics of these rare events. Minerva hindcasts are found to exhibit skill in reproducing climatological characteristics of the mid-Atlantic TC landfalls particularly at the highest atmospheric horizontal spectral resolution of T1279 (16-km grid spacing). Historical forecasts are further interrogated to identify regional and large-scale environmental conditions associated with these rare TC tracks to better quantify their predictability on synoptic time scales, and their dependence on model resolution. Evolution of the large-scale atmospheric flow patterns leading to mid-Atlantic TC landfalls is analyzed using local finite-amplitude wave activity (LWA). We have identified large-amplitude quasi-stationary features in the LWA and sea surface temperature (SST) anomaly distributions that persist up to about a week leading to these land-falling events. A statistical model utilizing indices based on the LWA and SST anomalies as predictors is developed that exhibits skill (mostly at T1279) in predicting mid-Atlantic TC landfalls several days in advance. Implications of these results for longer time-scale predictions of mid-Atlantic TC landfalls including climate change projections are discussed.
George Mason Univ, Ctr Ocean Land Atmosphere Studies, Fairfax, VA 22030 USA
Recommended Citation:
Manganello, Julia V.,Cash, Benjamin A.,Swenson, Erik T.,et al. Assessment of Climatology and Predictability of Mid-Atlantic Tropical Cyclone Landfalls in a High-Atmospheric-Resolution Seasonal Prediction System[J]. MONTHLY WEATHER REVIEW,2019-01-01,147(8):2901-2917