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Development of a portable 3-dimensional variation (3DVAR) data assimilation module for NOAA

Award Information
Agency: Department of Commerce
Branch: National Oceanic and Atmospheric Administration
Contract: WC-133R-17-CN-0084
Agency Tracking Number: 17-1-070
Amount: $120,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: 8.1.4
Solicitation Number: NOAA-2017-1
Timeline
Solicitation Year: 2017
Award Year: 2017
Award Start Date (Proposal Award Date): 2017-06-14
Award End Date (Contract End Date): 2017-12-14
Small Business Information
3179 Main Street, Unit 3, Barnstable, MA, 02630-1105
DUNS: 133087192
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Yi  Chao
 (508) 362-9400
 ychao@remotesensingsolutions.com
Business Contact
 Yi  Chao
Phone: (508) 362-9400
Email: ychao@remotesensingsolutions.com
Research Institution
N/A
Abstract
We propose to develop and deliver a generalized 3DVAR data assimilation module, in an opensource and non-proprietary programming language, which is compatible with both ROMS and FVCOM and easily incorporated into the existing NOAA operational forecast system (OFS). Our innovation is to develop a data assimilation roadmap contrasting 3DVAR with other advanced techniques such as multi-scale 3DVAR, 4DVAR or Local Ensemble Transform Kalman Filter (LETKF) in terms of accuracy and computational efficiency. We will leverage our experience and expertise in developing 3DVAR for a ROMS-based real-time forecast system for the California coastal ocean. 3DVAR has an ability to propagate observational information in both the horizontal and vertical directions while still keeping the computational overhead at a manageable level (e.g., 2X the forward model run time as compared to 20X or more for (4DVAR). Working closely with NOAA scientists, we will identify requirements for data assimilation, implement 3DVAR into the model selected by NOAA, and demonstrate the ability of 3DVAR to 1) incorporate various observational data sets into the existing NOAA OFS, 2) run efficiently from the computationally perspective with a user friendly interface, and 3) improve over unassimilated simulations.

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

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