Weather Impact Probability Forecasting (WIPCast)

Award Information
Agency:
Department of Defense
Amount:
$119,807.00
Program:
SBIR
Contract:
W911QX-11-C-0005
Solitcitation Year:
2010
Solicitation Number:
2010.2
Branch:
Army
Award Year:
2010
Phase:
Phase I
Agency Tracking Number:
A102-065-0221
Solicitation Topic Code:
A10-065
Small Business Information
Impact Computing Corporation
606 Burnt Mills Avenue, Silver Spring, MD, 20901
Hubzone Owned:
N
Woman Owned:
N
Socially and Economically Disadvantaged:
N
Duns:
034155601
Principal Investigator
 Hyam Singer
 President
 (301) 593-2350
 hyam.singer@impact-computing.com
Business Contact
 Hyam Singer
Title: President
Phone: (301) 593-2350
Email: hyam.singer@impact-computing.com
Research Institution
N/A
Abstract
Impact Computing has assembled a world class team – supported by University of Washington, ZedX and Marcus Weather – uniquely qualified to develop the Weather Impact Probability Forecast (WIPCast) system proposed herein. Our approach builds on the research and work of both Dr. Tony Eckel and Dr. Adrian Raftery in the domain of probabilistic forecasting and its practical application. Key innovations and features of our WIPCast solution include: • Calculation of Weather Impact Probability (WIP) to convey the total risk of serious degradation to the complete mission from weather • Development and application of Mission Impact Functions (MIFs) that describe the uncertainty in the chance of mission failure from actual occurrence of adverse weather • Objective calculation of ambiguity in the ensemble forecast and translation into a WIP confidence interval to convey confidence in the decision input • Statistical amalgamation of the potential impact from multivariate weather sensitivities, based on a multivariate space-time probability model for the weather elements of interest, conditioned on the calibrated ensemble forecasts • Flexible Service Oriented Architectures (SOA) that maximizes interoperability with existing systems and frameworks • Data source agnostic approach that avoids reliance on the peculiarities or idiosyncrasies of any particular mesoscale ensemble data source

* information listed above is at the time of submission.

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