Clock Offset Analysis

Clock offset analysis is a critical intermediate step in oceanographic data processing that occurs between Stage 1 (standardization) and Stage 2 (trimming to deployment) of the processing pipeline. This analysis identifies and corrects timing errors in instrument clocks to ensure accurate temporal alignment of data from multiple instruments on the same mooring.

Overview

Oceanographic instruments deployed on moorings may have clock offsets due to incorrect setup, clock drift, or other timing issues. Since scientific analysis often requires precise temporal correlation between instruments at different depths, these timing errors must be identified and corrected.

The clock offset analysis provides two complementary methods to detect timing discrepancies:

  1. Deployment Period Detection: Uses temperature profiles to identify when instruments were actually deployed on the seafloor

  2. Lag Correlation Analysis: Compares temperature time series between instruments to detect systematic timing offsets

Methods

Deployment Period Detection

This method leverages the characteristic temperature signature that occurs when instruments transition from surface conditions to deep-water conditions during deployment:

  • Temperature Threshold: Identifies “cold” water temperatures using statistical analysis (typically mean ± 3 standard deviations of deep values)

  • Start/End Times: Determines when each instrument first and last recorded temperatures within the cold water range

  • Consensus Analysis: Groups instruments with similar deployment timing to establish reference times

  • Offset Calculation: Computes timing differences relative to the consensus group

This approach is effective for detecting large clock offsets (hours to days) and works well when instruments show clear temperature transitions during deployment and recovery.

Lag Correlation Analysis

This method performs cross-correlation analysis between temperature time series from different instruments:

  • Reference Selection: Uses one instrument as a temporal reference (typically the deepest or most reliable)

  • Subsampling: Reduces computational load by subsampling time series while maintaining correlation structure

  • Cross-Correlation: Computes lag correlations to find the temporal offset that maximizes correlation between instruments

  • Peak Detection: Identifies the lag corresponding to maximum correlation as the estimated clock offset

This approach is sensitive to smaller timing errors (seconds to minutes) and works well when instruments record similar environmental variations.

Implementation

The analysis is implemented in the oceanarray.clock_offset module with the following key functions:

Core Functions

  • load_mooring_instruments(): Load instrument data and enrich with YAML metadata

  • analyze_deployment_timing(): Perform temperature-based deployment period detection

  • calculate_timing_offsets(): Calculate offsets using consensus grouping approach

  • perform_lag_correlation_analysis(): Execute cross-correlation analysis

  • print_timing_offset_summary(): Generate human-readable offset summary

Workflow Functions

  • create_common_time_grid(): Generate interpolation grid for temporal alignment

  • interpolate_datasets_to_grid(): Interpolate instrument data to common time base

  • combine_interpolated_datasets(): Merge data into multi-level dataset structure

Usage

The analysis is demonstrated in the demo_clock_offset.ipynb notebook, which provides a streamlined workflow:

  1. Configuration: Specify mooring name and data paths

  2. Data Loading: Load instrument datasets and YAML metadata

  3. Preprocessing: Create common time grid and interpolate data

  4. Deployment Analysis: Identify deployment periods using temperature profiles

  5. Visualization: Plot temperature time series with deployment bounds

  6. Offset Calculation: Compute timing offsets using both methods

  7. Results Summary: Generate recommendations for YAML configuration

Output and Application

The analysis produces:

  • Offset Estimates: Recommended clock_offset values (in seconds) for each instrument

  • Quality Metrics: Correlation coefficients and confidence measures

  • Visualizations: Time series plots showing deployment periods and timing relationships

  • Summary Tables: Tabular results comparing both analysis methods

Important: The calculated offsets should be entered as negative values in the YAML configuration file, as they represent the correction needed to align instrument times with UTC.

After updating the YAML file with clock_offset values, Stage 2 processing applies these corrections to create *_use.nc files. The clock offset analysis can then be re-run using the corrected data to verify that timing discrepancies have been resolved.

Best Practices

  • Method Comparison: Always compare results from both deployment detection and lag correlation methods

  • Visual Inspection: Examine temperature time series plots to validate automated detection

  • Iterative Refinement: Re-run analysis after applying corrections to verify success

  • Documentation: Record analysis decisions and unusual findings in processing logs

  • Validation: Cross-check results with deployment/recovery logs when available

The clock offset analysis ensures that subsequent processing stages work with temporally aligned data, which is essential for accurate calculation of transport estimates and other derived quantities that depend on precise temporal relationships between measurements at different depths.