Remote Sensing and Modeling Recompete (C)
Forecast New Requirement Updated
- Agency
-
Navy SECNAV ONR Naval Research Lab
Office of Naval Research, Department of Defense
- Solicitation expected
- Not on record
- Estimated value
- $7,500,000 to $50,000,000
- Set-aside
- No set-aside
Forecast facts
- Anticipated award
- NAICS code
- 541715 Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)
- Place of performance
- Washington, DC
- Forecast number
- N00173-27-RFPREQ-7600-0002
Forecast details come from the Navy's Long Range Acquisition Estimates. Agencies' dates and values are estimates.
About this forecast
The Naval Research Laboratory, under the Office of Naval Research, is conducting a competitive recompete for remote sensing and modeling R&D services, estimated to award in July 2027. The contract will provide experimental and theoretical research support for NRL's Space Science Division using on-site and off-site personnel, with required expertise in numerical modeling of terrestrial and planetary atmospheric processes, active and passive remote sensing techniques across the electromagnetic spectrum, advanced data assimilation, remote sensing data analysis, and high-performance computing methods.
The estimated award value ranges from $7.5 million to $50 million under a cost-reimbursement "C" type contract, with performance taking place in Washington, DC. Personnel and facility clearances are unclassified, and the procuring contracting office is NRL (UIC N00173).
Forecast text
This contract will provide the necessary R&D expertise to support ongoing experimental and theoretical research projects at NRL-SSD using both on-site and off-site personnel. Theoretical support requires expertise in numerical modeling of terrestrial and planetary atmospheric processes, development and application of both active and passive remote sensing techniques encompassing the entire electromagnetic spectrum, advanced data assimilation techniques, analysis of both solar and terrestrial remote sensing data sets, and application of advanced computational methods in a high-performance computing environment.
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