Calibration of Ensemble Forecasts Using Reforecast Datasets

Award Information
Agency:
Department of Defense
Branch
Defense Threat Reduction Agency
Amount:
$97,601.00
Award Year:
2006
Program:
SBIR
Phase:
Phase I
Contract:
HDTRA1-06-P-0087
Award Id:
79006
Agency Tracking Number:
RDI060003360
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
P O Box 3029, Norman, OK, 73070
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
n/a
Principal Investigator:
Fanyou Kong
Senior Scientist
(405) 227-0084
fanyou.kong@atscwx.com
Business Contact:
Vicki Rose
Managing Business Director
(405) 227-0084
vicki.rose@atscwx.com
Research Institute:
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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