QC Handling: Interpolation Affecting Data Quality

Introduction

In data processing, particularly within the realm of scientific and engineering applications, quality control (QC) is of paramount importance. Ensuring the accuracy and reliability of data is crucial for informed decision-making and valid conclusions. A common technique used to align datasets is interpolation, which estimates values between known data points. However, when interpolation is applied within processing steps, it can introduce complications in QC handling. This article delves into the challenges that arise when interpolation occurs mid-process, specifically how it can affect the representativeness of QC flags and potentially lead to misleading quality assessments. We will explore the intricacies of this issue, propose potential solutions, and illustrate the problem with a concrete example. Understanding these challenges is crucial for maintaining data integrity and ensuring that QC processes accurately reflect the quality of the final output.

The Challenge of Interpolation in QC

The core challenge arises when data interpolation is performed as an intermediate step in a larger calculation. Consider a scenario where two variables, A and B, are used to compute a third variable, C. To ensure proper alignment and calculation, variable A might undergo interpolation to match the data points of variable B. Each variable, A and B, has its own quality control (QC) column, denoted as A_QC and B_QC, respectively. These QC columns provide flags that indicate the quality of each data point, helping to identify potentially erroneous or unreliable values.

However, the problem emerges when variable A is interpolated within a processing step. After interpolation, the original A_QC column no longer accurately represents the quality of the values used to calculate C. The interpolated values are new estimates, and their quality is not directly reflected in the original A_QC flags. This discrepancy can lead to a situation where the QC flags for the final output, C, are misleading. For instance, a non-NaN (Not a Number) value in C might be flagged as 9, indicating a potentially severe quality issue, even if the underlying interpolated values are reasonable. This misrepresentation occurs because the QC process relies on the original A_QC, which doesn't account for the changes introduced by interpolation.

The crux of the issue is that QC handling is often designed to be step-external, meaning that QC flags are assessed and applied before or after an entire processing step. This approach works well when data transformations are straightforward and don't involve intermediate steps that alter the data's fundamental characteristics. However, when interpolation is introduced as an intermediate step, the step-external approach falls short, as it fails to capture the impact of interpolation on data quality.

To address this challenge effectively, a more nuanced approach to QC handling is required, one that is step-internal. This means integrating QC considerations into the individual steps of the processing workflow, particularly those involving interpolation. Such a shift would allow for a more accurate assessment of data quality throughout the entire process, ensuring that QC flags appropriately reflect the quality of the final output.

Proposed Solutions: Towards Step-Internal QC

To effectively address the challenges posed by interpolation within processing steps, a fundamental shift in how quality control (QC) is handled may be necessary. Moving from a step-external approach to a step-internal approach is crucial for maintaining data integrity and producing reliable results. This transition involves several key changes, including modifications to existing processing steps and the introduction of new utilities.

One potential solution involves a major overhaul of the QC handling system, re-architecting it to be inherently step-internal. This would entail a significant undertaking, requiring a careful review of existing workflows and the development of new mechanisms for tracking and propagating QC information throughout the processing pipeline. However, this approach offers the most comprehensive solution, ensuring that QC considerations are integrated into every aspect of the data processing workflow.

A more targeted approach focuses on modifying specific processing steps, particularly the Apply_QC step. This step is often responsible for applying QC flags based on various criteria. By modifying this step, it could be made aware of interpolation processes and their impact on data quality. This might involve incorporating logic to assess the quality of interpolated values and update QC flags accordingly. For instance, if a value is interpolated from neighboring high-quality data points, it could be assigned a higher QC flag than if it were interpolated from low-quality data points.

In conjunction with modifying existing steps, the development of a standard interpolation utility is essential. This utility should not only perform the interpolation but also generate a stand-alone QC column for the interpolated data. This QC column would reflect the quality of the interpolated values, taking into account factors such as the quality of the original data points and the interpolation method used. By providing a dedicated QC column for interpolated data, this utility would ensure that the quality of the interpolated values is properly tracked and can be used in subsequent QC assessments.

The key components of this interpolation utility would include:

• An algorithm for assessing the uncertainty introduced by the interpolation process itself.
• A method for propagating QC flags from the original data points to the interpolated values, taking into account the interpolation method and the distance between data points.
• A mechanism for handling edge cases, such as interpolation near data gaps or boundaries.

By implementing these solutions, we can move towards a more robust and accurate QC handling system that effectively addresses the challenges posed by interpolation within processing steps. This will ultimately lead to higher quality data and more reliable results.

BBPfromBeta Example: A Case Study

To illustrate the challenges of interpolation in quality control (QC), consider the BBPfromBeta step in a data processing workflow. This step involves converting Beta values to BBP (Backscattering Coefficient) values, a crucial calculation in oceanographic research. The conversion process relies on two key variables: TEMP (temperature) and PRAC_SALINITY (practical salinity). These variables are often interpolated to ensure they align with the Beta data points, enabling accurate calculations.

The problem arises because the interpolation of TEMP and PRAC_SALINITY occurs within the BBPfromBeta step. This means that the original QC columns for TEMP (TEMP_QC) and PRAC_SALINITY (PRAC_SALINITY_QC) no longer accurately reflect the quality of the values used in the Beta to BBP conversion. The interpolated TEMP and PRAC_SALINITY values are new estimates, and their quality is not directly represented by the original QC flags.

Subsequently, the generate_qc() call, which is responsible for generating the QC flags for the resulting BBP values (BBP_QC), lacks the necessary