A -- special study

The U. S. Department of Commerce/National Oceanic Atmospheric Administration/NESDIS, intends to negotiate with the University of California at Irvine, Irvine, CA to provide services for the Modification and Testing of the Precipitation Estimation using Remotely Sensed Information from Artificial Neural Network (PERSIANN) Algorithm on Advanced Baseline Imager (ASBI) Data. Radiance information in the visible, infrared, and microwave portions of the electromagnetic spectrum have been used to estimate rainfall for decades, first using manual techniques and more recently via fully automated retrieval algorithms. Improvement in satellite-based instrumentation and the development of more advanced algorithms progress has been made in increasing the skill of these estimates. The potential for further improvement to these rainfall estimates is anticipated with the introduction of the advanced baseline imager (ABI) on board the GOES-R generation of platforms beginning in the next decade. Several techniques for estimating rainfall and now castings brightness temperature fields will be incorporated into the operational GOES-R ground processing system to produce these Environmental Data Records. One of the techniques is the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) algorithm developed at the University of California at Irvine. PERSIANN uses artificial neural networks to develop and update relationships between GOES-measured parameters and rainfall rates derived from microwave data in order to produce high-quality estimates of rainfall than would be possible using GOES data alone. The Objectives of this task will be to modify PERSIANN as needed to take advantage of the Advanced Baseline Imager brightness temperatures to the Hydrology Algorithm Team for evaluation. Deliverables: 1) Spatial fields of estimated rain rate derived from SEVIRI data (as a proxy for ABI) and microwave rainfall rates that have previously been provided the Hydrology Algorithm Team. 2) As needed, documentation describing the provided SERVIRI forecast fields and any caveats that should be noted when evaluating the data. 3) If PERSIANN will be provided along with accompanying documentation. Assistance will also be provided as needed in implementing the code at NESDIS and in modifying the source code and producing documentation to meet Algorithm Working Group AWG coding and documentation standard. 4) Monthly progress reports describing accomplishments, planned work, and any difficulties encountered. This procurement is being conducted per FAR Part 13 Simplified Acquisition Procedures (NTE $100K) NAICS 611310. Interested persons may identify their interest and must demonstrate their capability in order to be considered for the opportunity. This is not a request for competitive proposal. No competitive solicitation is planned, information submitted in response to this notice will be used solely to determine whether or not to open up to competitive procedures. Note 22 applies.