OOS 5-7
Uncertainty in an uncertain world: Using scientific judgment for evaluating uncertainty in measurement results

Monday, August 5, 2013: 3:40 PM
101E, Minneapolis Convention Center
Janae L. Csavina , National Ecological Observatory Network (NEON, Inc.), Boulder, CO
Jeffrey Taylor , National Ecological Observatory Network (NEON, Inc.), Boulder, CO
Joshua A. Roberti , National Ecological Observatory Network (NEON, Inc.), Boulder, CO
Background/Question/Methods

The overarching question when evaluating uncertainty in a measurement is how close the measurement is to the truth.  At the Calibration and Validation Laboratory (CVAL) of the National Ecological Observatory Network (NEON), uncertainty assessment starts with the truth (reference) and propagates to the field measurement.  Many components contribute to overall uncertainty, but where does one draw the line for quantifying uncertainty? Here I will explain methods emulated from techniques adopted by International Organization for Standardization (ISO) and National Institute of Standards and Technology (NIST) for using scientific judgment in evaluating uncertainty. 

Results/Conclusions

While consistency in expressing uncertainty is important, not all sensors are created equal in ecological observation measurements.  An intimate understanding of the sensor and nature of the measurement is necessary for identifying uncertainty components.  While repeatability and reproducibility are necessary components, other random errors must be included in the uncertainty budget such as variable ambient temperature, long-term stability, etc.  Once sources of error are identified, statistical analysis on experimental results or utilizing existing published knowledge of an uncertainty component can be used to quantify uncertainty – Type A and B approach, respectively.  Within NEON, our aim is to provide the end user with details of all components of quantified uncertainty allowing for modified interpretation of uncertainty.  This feature will allow for the separation of variance due to environmental processes and errors associated with the observation (as described by N. Thompson Hobbs).  At NEON, we are striving to reduce the number of unknowns and better understand the remaining unknowns.