Calibration of Ensemble Forecasts Using Reforecast Datasets

Award Information
Agency: Department of Defense
Branch: Defense Threat Reduction Agency
Contract: HDTRA1-06-P-0087
Agency Tracking Number: RDI060003360
Amount: $97,601.00
Phase: Phase I
Program: SBIR
Awards Year: 2006
Solicitation Year: 2006
Solicitation Topic Code: DTRA06-006
Solicitation Number: 2006.1
Small Business Information
P O Box 3029, Norman, OK, 73070
DUNS: N/A
HUBZone Owned: Y
Woman Owned: Y
Socially and Economically Disadvantaged: Y
Principal Investigator
 Fanyou Kong
 Senior Scientist
 (405) 227-0084
 fanyou.kong@atscwx.com
Business Contact
 Vicki Rose
Title: Managing Business Director
Phone: (405) 227-0084
Email: vicki.rose@atscwx.com
Research Institution
N/A
Abstract
The accurate numerical prediction of hazardous airborne plumes requires two important capabilities. First, meteorological conditions at fine spatial scale both at the time of plume release as well as a few hours into the future, and second, quantification of this information in a statistically reliable probabilistic framework. The proposed study will use a combination of fine-scale ensemble re-forecasts, as well as historical surface observations, to achieve the goals of the solicitation. Because no unique method exists for doing so, we examine several and will pursue that which is most accurate, efficient and adaptable to future needs. The first approach, arguably the most simple and computationally efficient, involves using a 20-year history of surface observations to create regression equations that yield statistically reliable probabilistic point forecasts given current conditions. At the other extreme, fine-scale reforecasts will be generated from historical re-analyses and both linear and nonlinear regression approaches applied for calibration. Uniquely, we combine the 20-years of historical observations with this framework such that the final outcome is a combination of dynamical forecast and observation-based statistics. Finally, we expand the nearest-neighbor analog method using ensemble reforecasts alone or in combination with historical surface observations.

* Information listed above is at the time of submission. *

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