1 / 28

Post-Calibration of Fluorescence Data from Continuous Monitors

Post-Calibration of Fluorescence Data from Continuous Monitors (To adjust or not to adjust, and if so, how?). A “Gang of N” Production Elgin Perry - Statistics Consultant Marcia Olson - NOAA/CBP Beth Ebersole - MD DNR Bill Romano - MD DNR.

desma
Download Presentation

Post-Calibration of Fluorescence Data from Continuous Monitors

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Post-Calibration of Fluorescence Data from Continuous Monitors (To adjust or not to adjust, and if so, how?) A “Gang of N” Production Elgin Perry - Statistics Consultant Marcia Olson - NOAA/CBP Beth Ebersole - MD DNR Bill Romano - MD DNR Presented to the Tidal Monitoring and Analysis Workgroup on 3 December 2003

  2. (Kim Mikita’s data)

  3. Diel cycle Tidal cycle

  4. log(x/y)=log x - log y LNRAT = LNCHL_F – LNCHL_A

  5. Model Evaluated Log ratio as a function of: • Season • Sonde deployed • Light • Turbidity

  6. Model Results

  7. At higher light levels, extractive samples exceed fluorescence data?

  8. Fluorescence exceeds extractive at all light levels, so no light effect.

  9. Fluorescence exceeds extractive under all light levels, so no light effect.

  10. Similar pattern to Turville Creek and Shelltown.

  11. Chlorophyll Adjustment Methods • Regression method – CF(adj) = ß0 + ß1CF (Model CS = CF) • Multiple regression – CF(adj) = ß0 + ß1CF + ß2Turb (Model CS = CF, Turbidity) • Ratio method – CF(adj) = CF x (CS/CF) • Subtract “background fluorescence”

  12. Linear Regression Assumptions • Y is linearly related to X • Expected value of the error term is zero • Constant variance in the error terms, which are uncorrelated • Independent variable(s) is measured without error • Independent variables are not linearly related

  13. Correction Using Ratio of Extractive to In Vivo

  14. Conclusions • Stop collecting data • Full speed ahead (one size fits all) • Test various models and select one that minimizes root mean square error on a per station basis • Hire an expert to assess covariate measurement error problem • Log transform for correction and then back transform

More Related