Difference between revisions of "Data Quality and Validation White Paper Kick-off, GSFC"

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(Created page with "==White Paper on Data Quality and Validation Framework for RS data: Best practices and user perspective== # Objective: Capture, harmonize and provide useful data quality for RS d...")
 
 
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Participants: Joanne Nightingale, Chris Lynnes, Greg Leptoukh
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==White Paper on Data Quality and Validation Framework for RS data: Best practices and user perspective==
 
==White Paper on Data Quality and Validation Framework for RS data: Best practices and user perspective==
# Objective:
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1. ''Objective'':
 
Capture, harmonize and provide useful data quality for RS data
 
Capture, harmonize and provide useful data quality for RS data
  
# Current Status:
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2. ''Current Status'':
 
* No coordinate approach ... besides QA4EO, WGCV and some discipline-specific efforts
 
* No coordinate approach ... besides QA4EO, WGCV and some discipline-specific efforts
 
* Duplication of efforts across missions - inefficient utilization of funding
 
* Duplication of efforts across missions - inefficient utilization of funding
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* 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
 
* 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
  
# Suggested approach:
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3. ''Suggested approach'':
 
* Collect best practices from various communities and known efforts
 
* Collect best practices from various communities and known efforts
 
* Develop consistent terminology and metrics, e.g., completeness, consistency, representativeness
 
* 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
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* Identify main purpose classes and establish a high-level Q-metrics levels for different purposes:
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** Climate change; model validation; event monitoring; tele
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* Error budget:
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** Propagate cal/L1 errors to L2 and L3
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* Bias assessment:
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** Quantify Spatial/temporal/vertical sampling effect
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* Provenance:
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** Capture, harmonize and deliver useful provenance
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4. ''Decadal Survey era''

Latest revision as of 16:01, December 22, 2010

Participants: Joanne Nightingale, Chris Lynnes, Greg Leptoukh

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