465222 - Snyopsis_DRAFT_Statement of Work_03-28-2025.pdf
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- Solicitation number
- 465222
- Issued by
-
Department of Energy Office of Science
About this file
This is a Statement of Work (SOW) for the Brookhaven National Laboratory's Center for Multiscale Applied Sensing (CMAS) Urban Weather Research support subcontract. The document outlines four primary scope areas: 1) Field Campaign Planning and Logistics, involving pre-deployment planning, route identification, and site selection; 2) Field Deployment Support, requiring qualified personnel with expertise in atmospheric sensing technologies like Doppler lidars, cloud radars, and balloon-borne sensors; 3) Environmental Data Curation, focused on pre-processing and archiving datasets including satellite imagery, weather radar observations, and urban infrastructure layers; and 4) Application of Artificial Intelligence for Experimental Design, which involves developing AI and machine learning techniques to optimize observational strategies.
The pre-solicitation notice indicates this is a competitive subcontract opportunity with an anticipated award date of August 15, 2025, for a one-year base period with potential two-year extension. The contract is managed by Brookhaven Science Associates for the U.S. Department of Energy, with a NAICS code of 541715 for research and development in physical, engineering, and life sciences. The subcontract will be cost-type and aims to augment CMAS's internal capabilities in atmospheric research, specifically supporting field operations, data curation, and integrating advanced data science methodologies into campaign design.
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Text version
Environmental Science and Technologies Department
P.O. Box 5000 Upton, NY 11973-5000 managed by Brookhaven Science Associates for the U.S. Department of Energy www.bnl.gov
Date: May 28, 2025
Subject: Statement of Work “Support of Center for Multiscale Applied Sensing (CMAS) Urban
Weather Research”
Introduction:
The Brookhaven National Laboratory (BNL) Center for Multiscale Applied Sensing (CMAS) conducts research on atmospheric phenomena in heterogenous landscapes and energy hotspots.
CMAS primarily relies on field observations to achieves its scientific goals. To augment our internal capabilities, BNL is seeking Subcontract support to assist with field operations, data curation, and the integration of advanced data science methodologies into campaign design on a need basis.
Scope of Work:
1. Field Campaign Planning and Logistics
• Assist with pre-deployment planning for mobile and stationary observational campaigns.
• Tasks may include: identification of optimal deployment routes and sensor placement sites, support with local and municipal permitting, and coordination of lodging and local logistics for field staff.
• Provide input on deployment strategies based on prior experience with atmospheric field studies.
2. Field Deployment Support
• Provide qualified personnel (graduate students, research staff) to participate in field campaigns involving the setup, operation, and maintenance of environmental sensing platforms.
• Personnel should have familiarity with at least some of the following technologies: Doppler lidars, cloud radars, radiometric temperature sensors (RTDs, thermocouples), surface weather stations, balloon-borne sensors (e.g., radiosondes, Windsonds), and air quality instrumentation.
3. Environmental Data Curation
• Pre-process, and archive publicly available or campaign-specific environmental datasets.
• Examples of relevant datasets include:
- Satellite-derived land surface temperature products (e.g., Landsat, MODIS)
- Weather radar observations (e.g., NEXRAD Level II/III data)
- Land use/land cover classification (e.g., NLCD, Copernicus Urban Atlas)
- Urban infrastructure layers (e.g., building footprints, impervious surface maps)
4. Application of Artificial Intelligence for Experimental Design http://www.bnl.gov/
• Develop and demonstrate the application of artificial intelligence and machine learning techniques to optimize observational strategies, sensor placement, and sampling frequency.
• Techniques should include Gaussian Processes techniques that minimize information loss with enhancements using mixtures of experts
• Collaborate with BNL scientists to demonstrate the usefulness of AI-guided optimal experimental design for atmospheric science
• Provide thorough performance evaluation through synthetic case studies and/or real campaign data.
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