Data Quality and Validation White Paper Kick-off, GSFC

White Paper on Data Quality and Validation Framework for RS data: Best practices and user perspective

1. Objective: Capture, harmonize and provide useful data quality for RS data

2. Current Status:

• No coordinate approach ... besides QA4EO, WGCV and some discipline-specific efforts
• Duplication of efforts across missions - inefficient utilization of funding
• Validation:
  • L2: validate in some areas and extrapolate globally. Issues: filtering by QC flag doesn't necessarily lead to good product
  • L3: what is L3 validation?
• Fitness for purpose: quality need should depend on data usage but the current quality is based on validation that has the specific purpose of validating instrument and retrieval algorithms

3. Suggested approach:

• Collect best practices from various communities and known efforts
• Develop consistent terminology and metrics, e.g., completeness, consistency, representativeness
• Identify main purpose classes and establish a high-level Q-metrics levels for different purposes:
  • Climate change; model validation; event monitoring; tele
• Error budget:
  • Propagate cal/L1 errors to L2 and L3
• Bias assessment:
  • Quantify Spatial/temporal/vertical sampling effect
• Provenance:
  • Capture, harmonize and deliver useful provenance

4. Decadal Survey era