Improving CERES Low-Latency Surface Radiation Fluxes with Machine/Deep Learning
National Aeronautics and Space Administration · LaRC: Langley Research Center
Overview
- Development stage
- In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
- High-impact designation
- Not high-impactSource: c) Not high-impact
- Impact justification
- Not reported
- Start date
- Not reported
- Withheld from public reporting
- NoSource: a) No
Mission
- Topic area
- Science
- Operational functions
- No function tag matched Derived by keyword rules; see methodology
Problem the AI is intended to solve
In conjunction with sophisticated radiative transfer simulations, the CERES (Clouds and the Earth's Radiant Energy System) team is using machine and deep learning methods to improve near real-time surface radiative fluxes for clean energy, infrastructure energy use, and agricultural applications.
Expected benefits
In conjunction with sophisticated radiative transfer simulations, the CERES (Clouds and the Earth's Radiant Energy System) team is using machine and deep learning methods to improve near real-time surface radiative fluxes for clean energy, infrastructure energy use, and agricultural applications.
System outputs
improve near real-time surface radiative fluxes
Technology
- AI classification
- Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
- System name(s)
- Not reported
- Custom-developed code
- Not reported
- Public source code
- Not reported
Sourcing
- How it was built
- Not reported
- Vendor (as reported)
- Not reported
- Vendors (standardized)
- None on the standard list
A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.
Data and privacy
- Involves PII
- Not reported
- Privacy Impact Assessment
- Not reported
- Authorization to Operate
- Not reported
- Demographic features
- Not reported
- Training and evaluation data
- Not reported
- Federal Data Catalog
- Not reported
Governance
0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.
Potential impacts and how they were identified
Not reported