taylor: estimating uncertainty for continental scale measurements
TRANSCRIPT
ESTIMATING UNCERTAINTY FOR CONTINENTAL SCALE MEASUREMENTS
Jeffrey R. Taylor, Josh Roberti, Derek Smith, Steve Berukoff, and Hank Loescher
What can NEON do to help?!?
• Offer Standardized Approaches
• Make Data and Algorithms Freely Available to Everyone
• Provide Forums/Workshops to Focus on Uncertainty
• Emphasize this is Papers/Classes/Labs
• Help Educate Policy Makers
NEON Design
1. Biodiversity
2. Biogeochemical cycles
3. Climate change
4. Ecohydrology
5. Infectious disease
6. Invasive species
7. Land use
Grand Challenges in Environmental Sciences
The overarching goal of NEON is to enable understanding and forecasting of climate change, land use change, and invasive
species on continental-scale ecology by providing infrastructure to support research in these areas.
Propagation of Uncertainty
Data Products at a SiteContinental Scale
Data Products
• Intergovernmental Panel on Climate Change Guidance Notes
How Do We Manage This Problem?
• Intergovernmental Panel on Climate Change Guidance Notes
How Do We Manage This Problem?
Very Qualitative, but Digestible…
NEON Data Portal
All Data, Data Quality Information, Uncertainty Calculations, and Algorithm Details are freely available to everyone
data.neoninc.org
Biggest Challenge
• Even the best attempts at discussing uncertainty can be twisted…
Source: Skeptical Science adapted from Knutti and Hegerl (2008)
Summary
• Uncertainty is (finally) gaining attention as an important part of informing environmental data
• Standards will be necessary to compare uncertainty between different network measurements and continental scale estimates
• Transparency and open communication are essential for effective understanding of uncertainty
• Advocacy at the Policy Maker level will advance the impact of uncertainty estimates
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The National Ecological Observatory Network is a project sponsored by the National Science Foundation and managed under cooperative agreement by NEON Inc.
www.neoninc.org
In-house performance verification / LabAcceptance testing
Level 1 (data ingest)
Automated QA/QCConv. to SI units
Dynamic SOPsSwapping sensors*Transfer standards
Level 2‘eyes on’ QA/QC
Sophisticated QA/QC
Level 3Instrument MentorsReview panelReprocessing data
Problem tracking field tech report report user community
QA/QC: Existing Data Quality Control and Assurance Models
In-house performance verification / LabAcceptance testing
Level 1 (data ingest)
Automated QA/QCConv. to SI units
Dynamic SOPsSwapping sensors*
Level 2‘eyes on’ QA/QC
Sophisticated QA/QC
Problem tracking field tech report user community
In-house performance verificationFact. Accept.
Level 1 (data ingest)
Automated QA/QCConv. to SI units
Dynamic SOPs Level 2‘eyes on’ QA/QC
Sophisticated QA/QC
Level 3User community
Problem tracking field tech report report user community
Level 3NOAA Dept analyses
NCDC / NWS /report/user community
US Climate Reference Network
PI-driven, ad hoc performance verification
Level 1 (data ingest)
Automated QA/QCConv. to SI units
Dynamic SOPsTransfer standards
Level 2‘eyes on’ QA/QC
Sophisticated QA/QC
Level 3Env. Canada
QA/QC User community
Problem tracking field tech report report user community
Level 1 (data ingest)
Conv. to SI unitsNon-standard. flags
Ad hoc SOPsTransfer standardsRoving system
Problem tracking field tech user community
PI-driven, ad hoc performance verification
Level 2Ad hoc QA/QC<site post doc>
Level 3CDIAC QA/QC
User community
ad hoc
In-house performance verificationFact. Accept.
Level 1 (data ingest)
Automated QA/QCConv. to SI units
Dynamic SOPs Level 2‘eyes on’ QA/QC
Sophisticated QA/QC
Problem tracking field tech report report
Level 3USDA data ArchiveUser community
report/user community
Agricultural Research Service
Data flow
Cal lab activities Field acquisition Data quality control
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