development of an optimal calibration strategy for trace gas measurements
DESCRIPTION
Development of an optimal calibration strategy for trace gas measurements. Mark Battle (Bowdoin College) Zane Davis, Ryan Hart, Jayme Woogerd, Jacob Scheckman, Eric Sofen, Becca Perry, John Carpenter. Special thanks: Britt Stephens (NCAR), Ralph Keeling (SIO), Bill Munger (Harvard) - PowerPoint PPT PresentationTRANSCRIPT
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Development of an optimal calibration strategy for trace
gas measurements
Mark Battle (Bowdoin College)
Zane Davis, Ryan Hart, Jayme Woogerd, Jacob Scheckman, Eric Sofen, Becca Perry, John
Carpenter.
Special thanks: Britt Stephens (NCAR), Ralph Keeling (SIO), Bill Munger (Harvard) Mary Lou Zeeman (Cornell/Bowdoin)
CompSust09 June 11, 2009
Funding from: DOE, Bowdoin College
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Outline
• Structure of a measurement program
• What measurements might tell us• Example of one such program• Call for help
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Measuring the composition of air
• Precision vs. Accuracy
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Precision vs. Accuracy
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Measuring the composition of air
• Precision vs. Accuracy• Differential measurements
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Benefits of differential measurements
Initial Group
1001 Women
Final group
1002 Women
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Benefits of differential measurements
Initial Group
1001 Women
Final group
1002 Women
Absolute changesInitial # women: 1001Final # women: 1002
Change in women: 0.1%
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Benefits of differential measurements
Initial Group
999 Men1001 Women
Final group
999 Men1002 Women
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Benefits of differential measurements
Initial Group
999 Men1001 Women
Final group
999 Men1002 Women
Differential changesInitial gender diff: 2Final gender diff: 3
Change in gender diff: 33%
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Benefits of differential measurements
Initial Group
999 Men1001 Women
Final group
999 Men1002 Women
Absolute changesInitial # women: 1001Final # women: 1002
Change in women: 0.1%
Differential changesInitial gender diff: 2Final gender diff: 3
Change in gender diff: 33%
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Measuring the composition of air
• Precision vs. Accuracy• Differential measurements • Measure samples relative to
“standards”
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Challenges of differential measurements
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Challenges of differential measurements
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Challenges of differential measurements
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Challenges of differential measurements
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Challenges of differential measurements
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Measuring the composition of air
• Precision vs. Accuracy• Differential measurements• Measure samples relative to
“standards”• Instrumental response
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Impact of instrumental non-linearity
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Metric
Precision & Accuracy
Constraints
Instrument time is preciousStandard air is precious
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In summary:
Optimally combine many analyses of many standards to create a virtual standard against which all samples
are measured.
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Connecting to the real world:
Measuring O2 and CO2
to constrain the carbon cycle
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Where does anthropogenic CO2 end up?
Values for 2000-2006 Canadell et al. PNAS 2007
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How do we know these numbers?
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How do we know these numbers?
• Record CO2 emissions
• Measure CO2 in the atmosphere
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How do we know these numbers?
• Record CO2 emissions
• Measure CO2 in the atmosphere
• Measure CO2 in the oceans• Estimate from small-scale land
measurements• Infer from spatial pattern and
isotopes of atmospheric CO2
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How do we know these numbers?
• Record CO2 emissions
• Measure CO2 in the atmosphere
• Measure CO2 in the oceans• Estimate from small-scale land
measurements• Infer from spatial pattern and
isotopes of atmospheric CO2
• Measure atmospheric O2
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The link between O2 and CO2
CO2 = Land biota + Industry + Ocean O2 = Land biota + Industry
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The link between O2 and CO2
CO2 = Land biota + Industry + Ocean O2 = Land biota + Industry
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The link between O2 and CO2
CO2 = Land biota + Industry + Ocean O2 = Land biota + Industry
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Goog
le m
aps
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The equipment
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The equipment
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Real data
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Real data
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Real data
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Summary
• Important questions require excellent atmospheric measurements
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Summary
• Important questions require excellent atmospheric measurements
• Excellent measurements require intelligent weighting of experimental evidence
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Summary
• Important questions require excellent atmospheric measurements
• Excellent measurements require intelligent weighting of experimental evidence
• I have abundant data. Intelligence, on the other hand…
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