Quality Control Challenges in Saildrone Data Processing

Saildrone data processing poses challenges in quality control due to inconsistencies in defining neighboring points for QC flag computation, leading to varying flags for similar behaviors. Additionally, nonstationary statistics in the data require anticipating changes in parameters for range checks, given the time-dependent variance and coarser resolution of climatologies used for QC. Citations for further reading and resources are provided.

• Saildrone
• Quality Control
• Data Processing
• Statistics
• Challenges

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Uploaded on Sep 11, 2024 | 1 Views

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1. Using QARTOD for Moving Platforms Joshua Osborne1,2, Kevin O'Brien1,2, Eugene Burger2 University of Washington/CICOES1, NOAA/PMEL2

2. Saildrone Courtesy: https://data.pmel.noaa.gov/pmel/LAS

9. Missing at Random Saildrone Data Main Issue: For tests that depend on neighboring points to compute QC flags, there is an inconsistent definition of neighbor . May result in different flags for similar behaviors Tests cannot consistently flag points across a data stream

12. Nonstationary Statistics in Saildrone Data Main Issue: Parameters for range checks have to anticipate changes in the data Fluctuations in time are an important feature of this kind of data Time-dependent variance means that the magnitude of these fluctuations can change Secondary Issue:The resolution of the climatologies used for range checking is coarser than the features we want to QC

14. Citations Live Access Server: https://data.pmel.noaa.gov/pmel/LAS ioos_qc: https://ioos.github.io/ioos_qc/index.html plotly: Plotly Technologies Inc. Collaborative data science. Montr al, QC, 2015. https://plot.ly.