1. J a n a e L . C s a v i n a
J o s h u a A . R o b e r t i
J e f f r e y T a y l o r
E S A A N N U A L M E E T I N G - A U G U S T 5 , 2 0 1 3
Uncertainty in an Uncertain World:
Using scientific judgment for evaluating
uncertainty in measurement

2. NEON: Overview
Nationwide ecological observatory fully funded by the National
Science Foundation that:
 Collects and provides data on the drivers
and responses of climate and ecological
change
 Serves as an experimental infrastructure
and backbone for research and experiments
 Develops and provides educational resources
to engage communities in working with
scientific data

3. NEON: Measurements
Inform biological, climate, carbon, water and
energy cycles/balances.
 Terrestrial Analysis
 Sensor: tower, soil
 Sampling: biogeochemistry, biodiversity
 Aquatic Analysis
 Sensor: ground water, lakes, streams
 Sampling: aquatic and sediment chemistry,
biodiversity
 Remote Sampling
 Airborne: canopy characteristics, land cover,
disturbance, etc.

4. NEON: Challenges
Maintaining consistency in measurement for:
 106 Sites
 20 domains
 Unique eco-climate
 3 terrestrial/domain
 3 aquatic/domain
 30 years of
observations

5. Calibration, Validation & Audit Laboratory:
NEON’s in-house consistency enforcer
 Calibrations:
 Performed at one lab for all sensors providing consistency
throughout network
 Traceable to a nationally recognized standard (NIST, NREL, etc.)
 All sensors annually calibrated

6. Calibration, Validation & Audit Laboratory:
NEON’s in-house consistency enforcer
 Validations:
 Ensure manufacturer’s calibration
 Sensor health
 Consistency with other instruments

7. Calibration, Validation & Audit Laboratory:
NEON’s in-house consistency enforcer
 Audits:
 Ensure internal laboratory performance
 Ensure external laboratory performance
 Field observation and collection consistency

8. Maintaining consistency in measurement for:
 ~12,000 Terrestrial
and Aquatic sensors
 44 different types
of sensors
Calibration, Validation & Audit Laboratory:
NEON’s in-house consistency enforcer

9. Parameterize Inconsistencies with Uncertainty
Uncertainty: informing the quality of measurement
 Assessment Standard: JCGM, 2008, Evaluation of measurement
data – Guide to the expression of uncertainty in measurement
(GUM), JCGM 100:2008.
 Type A: evaluated by statistical methods.
Experimental variance of independent observations
which differ in value because of random variations.
 Type B: evaluated by other means. Estimates
obtained using available published knowledge
such as manufacturer’s specs, calibration
certificates, reference data from handbooks, etc.

10. Parameterize Inconsistencies with Uncertainty
Uncertainty Assessment: Quantify this picture
DAQ:
Data
Acquisition
System

11. Truth & Trueness


12. Repeatability & Reproducibility


13. Combined and Expanded Uncertainty


14. Example: Temperature Sensor - Platinum Resistance
Thermometer (PRT) Calibration
 Utruth: SPRT = reference calibrated with first principles
(TPW, TPHg, MPGa)
 Quantify by Type B analysis:
Strouse, G.F., (2008) Standard Platinum
Resistance Thermometer Calibrations from
the Ar TP to the Ag FP, NIST Special
Publication 250-81.
Utruth= 0.000115 °C
SPRT = Truth
PRT = Measurement

15. Example: Temperature Sensor - Platinum Resistance
Thermometer (PRT) Calibration

• If 150 measurements taken n = 150
• Over a range of temperatures
Utrueness= 0.0004 °C

16. Example: Temperature Sensor - Platinum Resistance
Thermometer (PRT) Calibration

UDAQ= 0.0002 °C

17. Example: Temperature Sensor - Platinum Resistance
Thermometer (PRT) Calibration

Urepeatability = 0.0001 °C

18. Example: Temperature Sensor - Platinum Resistance
Thermometer (PRT) Calibration

Ureproducibility 2 = 0.0016 °C
Ureproducibility 1= 0.0007 °C
Ureproducibility 1= 0.0005 °C

19. Example: Temperature Sensor - Platinum Resistance
Thermometer (PRT) Calibration

UCombined= 0.0019 °C
UExpanded= 0.0040 °C

20. Learning from Uncertainty
 Annual calibration of all sensors
 Analyze sensor health
 Annual assessment of uncertainty
 Analyze drift of the sensors
 Uncertainty used for quality
checks to determine sensor
performance
 Identify non-conforming sensors

21. Learning from Uncertainty
 At the end of the day, uncertainty is an estimation…..
 " ... as far as the propositions of mathematics refer to
reality, they are not certain; and as far as they are
certain, they do not refer to reality."
Albert Einstein, Geometry and Experience, Lecture before the Prussian Academy
of Sciences, January 27, 1921

22. 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
jcsavina@neoninc.org