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Development of a Portable 3-dimensional Variational (3DVAR) Data Assimilation Module for NOAA Operational Forecasting Systems

Award Information
Agency: Department of Commerce
Branch: National Oceanic and Atmospheric Administration
Contract: WC-133R-18-CN-0071
Agency Tracking Number: 17-2-070
Amount: $400,000.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: 8.1.4
Solicitation Number: NOAA-2017-2
Timeline
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-05-29
Award End Date (Contract End Date): 2021-05-28
Small Business Information
3179 Main Street, Unit #3, Barnstable, MA, 02630
DUNS: 133087192
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Yi Chao
 PrincipalScientist
 (508) 362-9400
 ychao@remotesensingsolutions.com
Business Contact
 James Canniff
Title: CFO
Phone: (508) 362-9400
Email: canniff@remotesensingsolutions.com
Research Institution
N/A
Abstract
TECHNICAL ABSTRACT: We propose to continue the development of a generalized 3DVAR data assimilation module, in an open-source and non-proprietary programming language, which is compatible with FVCOM and incorporated into the Lake Erie Operational Forecast System (LEOFS). We will leverage our experience and expertise in using 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 perform a hindcast experiment and demonstrate the ability of 3DVAR to 1) incorporate both in situ and satellite observational data sets into the existing LEOFS, 2) run efficiently from a computational perspective, and 3) improve over non-assimilating simulations. Our innovation is to include an Ensemble Kalman Filter (EnKF) module and develop a data assimilation roadmap towards a hybrid ensemble-variational (EnVar) data assimilation systemSUMMARY OF ANTICIPATED RESULTS: Successful completion of the proposed work will demonstrate the positive impact of 3DVAR assimilation of both in situ and satellite observations on the performance of the Lake Erie Operational Forecast System (LEOFS). Through 3DVAR data assimilation, the impact of observational data on the operational forecast system will be quantified. Specific delivery includes an advanced 3DVAR data assimilation module, in an open-source and non-proprietary programming language, which is compatible with FVCOM and incorporated into the LEOFS. The EnKF data assimilation system will also be developed for the LEOFS with a goal to formulate a data assimilation roadmap that will guide future improvement of the 3DVAR data assimilation. The outlook for the commercial need and market penetration for the ocean data assimilation technology is very promising. Since this technology fits into our company's mission statement, we will develop a more detailed business plan to expand the sales and marketing strategy for commercialization.

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

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