NSF Org:	AGS Division of Atmospheric and Geospace Sciences
Recipient:	COLORADO STATE UNIVERSITY
Initial Amendment Date:	February 5, 2004
Latest Amendment Date:	April 14, 2004
Award Number:	0346895
Award Instrument:	Standard Grant
Program Manager:	Jay S. Fein AGS  Division of Atmospheric and Geospace Sciences GEO  Directorate for Geosciences
Start Date:	February 15, 2004
End Date:	January 31, 2006 (Estimated)
Total Intended Award Amount:	$199,116.00
Total Awarded Amount to Date:	$219,171.00
Funds Obligated to Date:	FY 2004 = $219,171.00
History of Investigator:	William Gray (Principal Investigator) amie@atmos.colostate.edu
Recipient Sponsored Research Office:	Colorado State University 601 S HOWES ST FORT COLLINS CO  US  80521-2807 (970)491-6355
Sponsor Congressional District:	02
Primary Place of Performance:	Colorado State University 601 S HOWES ST FORT COLLINS CO  US  80521-2807
Primary Place of Performance Congressional District:	02
Unique Entity Identifier (UEI):	LT9CXX8L19G1
Parent UEI:
NSF Program(s):	Climate & Large-Scale Dynamics
Primary Program Source:	app-0104
Program Reference Code(s):	1324, EGCH
Program Element Code(s):	574000
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.050

ABSTRACT

This research has several goals related to Atlantic tropical cyclone/hurricane climatology and projections: (1) Improvement of extended range seasonal Atlantic basin hurricane prediction; (2) Intraseasonal (month-to-month) prediction of Atlantic hurricane activity; (3) Development of probability forecasts for U.S. hurricane landfall; and (4) Early December and early April prediction of El Nino-Southern Oscillation (ENSO). Empirical/statistical prediction models will be further developed and applied.

Broader Impacts:
The PI's tropical cyclone forecasts are utilized by emergency managers, insurance agencies, the media and the general public. His group's forecasts turn the public attention towards the upcoming hurricane season and its potential dangers. In addition, several risk catastrophe models utilize its seasonal forecast as a factor in determining suggested insurance premiums. The development of a more skillful ENSO prediction will benefit societal planning on intraseasonal to interannual time scales.

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