1044 WEATHER AND FORECASTING VOLUME 19 Prediction of August Atlantic Basin Hurricane Activity

• Main Authors: Eric S. Blake, William, M. Gray
• Other Authors: The Pennsylvania State University CiteSeerX Archives
• Format: Text
• Language: English
• Published: 2003
• Subjects: Bering Sea; Pacific; North Atlantic
• Online Access: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.392.6315; http://typhoon.atmos.colostate.edu/Includes/Documents/Publications/blakegray2004.pdf

Although skillful seasonal hurricane forecasts for the Atlantic basin are now a reality, large gaps remain in our understanding of observed variations in the distribution of activity within the hurricane season. The month of August roughly spans the first third of the climatologically most active part of the season, but activity during the month is quite variable. This paper reports on an initial investigation into forecasting year-to-year variability of August tropical cyclone (TC) activity using the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis dataset. It is shown that 55%–75 % of the variance of August TC activity can be hindcast using a combination of 4–5 global predictors chosen from a 12-predictor pool with each of the predictors showing precursor associations with TC activity. The most prominent predictive signal is the equatorial July 200-mb wind off the west coast of South America. When this wind is anomalously strong from the northeast during July, Atlantic TC activity in August is almost always enhanced. Other July conditions associated with active Augusts include a weak subtropical high in the North Atlantic, an enhanced subtropical high in the northwest Pacific, and low pressure in the Bering Sea region. The most important application of the August-only forecast is that predicted net tropical cyclone (NTC) activity in August has a significant relationship with the incidence of U.S. August TC landfall events. Better understanding of August-only TC variability will allow for a more complete perspective of total seasonal variability and, as such, assist in making better seasonal forecasts. 1.