Calibration of Ensemble Forecasts Using Reforecast Datasets
Award Information
Agency: Department of Defense
Branch: Defense Threat Reduction Agency
Contract: HDTRA1-07-C-0122
Agency Tracking Number: RDI060003360
Amount:
$749,905.00
Phase:
Phase II
Program:
SBIR
Awards Year:
2007
Solicitation Year:
2006
Solicitation Topic Code:
DTRA06-006
Solicitation Number:
2006.1
Small Business Information
P O Box 3029, Norman, OK, 73070
DUNS:
133622717
HUBZone Owned:
Y
Woman Owned:
Y
Socially and Economically Disadvantaged:
Y
Principal Investigator
Name: Fanyou Kong
Title: Sr. Research Scientist
Phone: (405) 325-0056
Email: fanyou.kong@atscwx.com
Title: Sr. Research Scientist
Phone: (405) 325-0056
Email: fanyou.kong@atscwx.com
Business Contact
Name: Vicki Rose
Title: Managing Business Directo
Phone: (405) 325-0056
Email: vicki.rose@atscwx.com
Title: Managing Business Directo
Phone: (405) 325-0056
Email: vicki.rose@atscwx.com
Research Institution
N/A
Abstract
Fine-scale, well-calibrated probabilistic weather forecasts are increasingly in demand for such weather-critical applications as battlespace management, and accurate prediction of high-value airborne operations. Built upon the Phase I effort we have just completed, the principal objective of this proposed study (Phase II) is to develop a reliable, efficient, and easily deployable reforecast-calibration system prototype based on fine-scale ensemble reforecast datasets and use it to produce well-calibrated, fine-scale probabilistic weather forecast products. This study consists of three major components. The first component is to produce a basic 20-year fine-scale ensemble reforecast dataset over the CONUS domain using the WRF-ARW modeling system. The second component is to further develop and examine various MOS-based statistical models that apply to more variables and multiple classes and to refine the Phase I KNN calibration techniques, using the large reforecast sample base, and to compare the two techniques. The third component is to conduct sensitivity experiments to study data-sparse impact and the trade-offs between ensemble size and reforecast length, in order to assess the feasibility and effectiveness of the deployable reforecast-calibration system. * Information listed above is at the time of submission. *