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Launch Weather Decision Support System

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: 80NSSC18C0210
Agency Tracking Number: 170066
Amount: $745,878.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: T1
Solicitation Number: STTR_17_P2
Timeline
Solicitation Year: 2017
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-09-27
Award End Date (Contract End Date): 2020-09-26
Small Business Information
4909 Nautilus Court North,#110
Boulder, CO 80301-5414
United States
DUNS: 786293696
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Randolph Ware
 (303) 817-2063
 ware@radiometrics.com
Business Contact
 Randolph Ware
Phone: (303) 817-2063
Email: ware@radiometrics.com
Research Institution
 University of Oklahoma-Norman Campus
 
660 Parrington Oval
Norman, OK 00000-0000
United States

 Federally funded R&D center (FFRDC)
Abstract

NASA wants a cost-effective atmospheric remote sensing system providing accurate temperature and humidity profiles to least 10 km height in clear and cloudy conditions. Radiometrics Microwave Profiler (MP) products currently provide accurate temperature and humidity profiles in good agreement with radiosondes to 3 km height. Good agreement can be extended beyond 10 km height using variational retrieval methods that combine radiometer and model gridded analysis. Pressure profiles derived from MP variational retrievals and from radiosonde observations show good agreement to 10 km height.  We propose to address this NASA remote sensing need by developing a robust, automated variational retrieval system providing radiosonde-equivalent temperature, humidity and pressure profiles to 20 km height at 150 m height intervals.NASA also wants to improve lightning risk identification during cloudy conditions. Current Radiometrics MP products measure liquid water path (LWP), an important parameter for natural and triggered lightning risk Launch Commit Criteria (LCC). We propose to address this need by developing a robust, automated lightning risk identification algorithm using MP LWP data and stability indices derived from MP variational retrievals. In addition, we propose to automate demonstrated capability for lightning risk identification more than two hours in advance of traditional methods based on electric field measurements based on stability indices derived from MP observations.
 

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

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