The Department of Commerce intends to issue a sole source firm fixed priced service purchase order to the University of Edinburgh, Old College, South Bridge, EH8 9Yl, Edinburgh, United Kingdom. The statutory authority for other than full and open competition is 41 USC 253(c)(1) as implemented by FAR 6.302-1, only one responsible source. Award will be made using FAR 13, Simplified Acquisition procedures the solicitation document and incorporated provisions and clauses are those in effect through Federal Acquisition Circular 2005-58, effective May 18, 2012.  Standard Industrial Code R499, North American Industry Classification System Code 541690 and size standard dollar values is $6.5 million business size.  

 This is not a solicitation for competitive quotes.  However, if any other interested party believes that it can meet the requirements, it may submit a statement of capabilities, which if timely received, will be considered by NOAA.  The capability statement and any other information furnished must be in writing, and must contain material in sufficient detail to allow NOAA to determine if the party can meet all of the foregoing requirements.  Responses must also be accompanied by descriptive literature, warranties, and/or other information that demonstrates that the quote meets all of the foregoing requirements.  Capability statements and related materials must be e-mailed to [email protected] by July 17, 2012, 11:00 a.m. local time.

The University of Edinburgh, Dr. Christopher Merchant, in conjunction with STAR developed the Bayesian Cloud detection methodology, which has already been adopted in the GOES-SST processing system. This has resulted in a significant improvement in the detection vs. false alarm ratio, providing better coverage of oceanographically important areas without sacrificing product accuracy.  This algorithm is fully described in Merchant et al. (2005).  The University of Edinburgh, Dr. Christopher Merchant, has recently applied the Bayesian cloud detection to land applications. He is the leading expert in the application of the Bayesian Cloud Mask for both ocean and land. Without his expertise, this development of the Bayesian cloud mask to all GOES-R applications would not be accomplished.   The Bayesian method will: 1) improve on the current GOES- R cloud mask and mitigate the current risk posed by reliance on a single cloud detection algorithm; 2) will provide the GOES-R user community with some level of continuity from previous geostationary SST products; and 3) allow tailoring of the cloud detection to the requirements of the individual applications. For GOES-R, the Bayesian method will be extended beyond the current GOES-SST algorithm to take advantage of additional channels offered by the ABI. These additional channels plus recent developments in forward modeling will enable extension of the method to cloud detection over land, meaning this project offers significant risk reduction for a wide range of GOES-R applications.  During this project, the extended Bayesian method will be evaluated by applying it to proxy data (primarily Meteosat-SEVIRI) and comparing the results with respect to derived products such as sea surface temperature and land surface temperature, as well as direct observations of clouds from the CALIPSO lidar. The work will be performed at the University of Edinburgh School Of GeoSciences in collaboration with NOAA/NESDIS/STAR.

 The determination to compete or not to compete the proposed contract based upon responses to this notice is solely within the discretion of the Government.   The information received will be considered solely for the purpose of determining whether to conduct a competitive procurement.            

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