Forecasting of Solar Eruptions using Statistical Mechanics, Ensemble, and Bayesian Forecasting Methods

Award Information
Agency:
Department of Defense
Amount:
$149,999.00
Program:
SBIR
Contract:
FA9453-14-M-0170
Solitcitation Year:
2014
Solicitation Number:
2014.1
Branch:
Air Force
Award Year:
2014
Phase:
Phase I
Agency Tracking Number:
F141-108-0648
Solicitation Topic Code:
AF141-108
Small Business Information
NorthWest Research Associates, Inc.
P.O. Box 3027, Bellevue, WA, 98009-
Hubzone Owned:
N
Woman Owned:
N
Socially and Economically Disadvantaged:
N
Duns:
151471349
Principal Investigator
 KD Leka
 Senior Research Scientist
 (303) 415-9701
 leka@nwra.com
Business Contact
 LJ Nickisch
Title: NWRA Vice President
Phone: (831) 582-4905
Email: lj@nwra.com
Research Institution
N/A
Abstract
The technological systems on which today's war-fighters depend, reflecting today's society, are vulnerable to the effects of sudden high-energy events from our Sun. Because the propagation speed for sudden increases in X-rays from a solar flare is the speed of light, it is necessary to truly forecast these events in order to mitigate their effects; the ionospheric response and influx of damaging radiation is almost as fast, with satellite charging and communications disruptions resulting. NorthWest Research Associates (NWRA) proposes to present an autonomous, flexible, verifiable forecasting system for flares, eruptions and energetic particle events from the Sun. The forecasting system uses presently available multi-wavelength data sources and state-of-the-art research in solar physics: key components include parameterizing observations of solar magnetic fields, surface- and sub-surface plasma flows, prior event history, and chromospheric/coronal pre-event energization. A forecast of solar activity is constructed using Multi-Variate Non-Parametric Discriminant Analysis that incorporates Bayesian statistics to produce a probabilistic forecast. We propose to tailor the results of prior innovative research to the unique USAF requirements, including full autonomy, verifiable performance and high flexibility regarding forecast timing and relative error costs.

* information listed above is at the time of submission.

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