Concatenate Deployments

This step combines multiple mooring deployments at the same site (e.g., WB1, WB2) into a single continuous time series on a regular time-pressure grid.

Purpose

  • Interpolate individual deployment periods onto a unified pressure grid (e.g., every 20 dbar from 0–4820 dbar).

  • Construct a consistent time axis (e.g., half-daily jg from April 1, 2004 to October 11, 2015).

  • Interpolate all segments onto this unified time grid, filling short gaps between deployments.

  • Output gridded .mat files for later use in AMOC transport code.

Process Overview

  1. Define a global pressure grid PG for all moorings.

  2. Load gridded temperature and salinity profiles from each deployment, e.g.: - wb1_2_200527_grid.mat - wb1_3_200607_grid.mat - …

  3. Interpolate each deployment’s TGfs (temperature) and SGfs (salinity) to the common pressure grid PG.

  4. Collect and concatenate the corresponding jd (Julian day) values for each deployment.

  5. Linearly interpolate all values onto the global time grid jg.

  6. Save the final combined profiles for each mooring site (e.g., TG_wb1, SG_wb1, etc.) along with metadata like location and pressure grid.

Notes

  • This process assumes that all individual deployment files have already been vertically interpolated (i.e., gridded in pressure).

  • The output is suitable for subsequent merging across mooring sites and dynamic height calculation.

  • Time interpolation smooths over deployment transitions and data gaps.

Original Source

Legacy Context (RAPID)

Adapted from MATLAB routines by Kanzow (2005) and later modified.

The MATLAB script mooring_interp_wb2_3_5.m from the RAPID processing workflow interpolates individual deployments together in time using matlab’s interp1 function. It combines multiple mooring deployments at the same site (e.g., WB2) into a single continuous time series on a regular time-pressure grid. Note that filenaming within the RAPID project has changed several times without changing the core functionality, but the legacy code provided here may not be consistent with the current RAPID processing.

Excerpt from mooring_interp_wb2_3_5.m
 1%%  Code to interpolate the mooring data together prior to loading into the
 2%%  MOC code
 3%
 4%
 5%  Interpolates the temperature and salinity gridded data produced by Julie
 6%  Collins onto a common time and pressure grid at each of the principal
 7%  locations.   The anomaly plots at the end are to check for obvious
 8%  errors in the data processing.
 9%
10%  The output file is saved in the amoc/grdat/ directory for use by the MOC
11%  code.
12%
13%  This code WILL need to be edited for every update of the time series and
14%  the time stamp, jg, is a key variable, that will be an input to set the
15%  length of the time series.
16%
17%
18% adapted from an original code by Kanzow 2005
19
20% Code History
21% -------------------
22% v2 - removed the separate pressure grid for WB2 and standardised to the
23% normal grid PG
24%
25% just mochab, mochae, wb2
26
27clear all
28close all
29clc
30
31warning off
32
33basedir = '/noc/mpoc/';
34datadir = [basedir '/rpdmoc/rapid/data/amoc/grout/OCT_2012/annual/'];
35% basedir = '/Volumes/';
36nowrite = 1;
37
38jlim        = julian([[2004,4,1,0];[2012,11,18,0]]);
39jg          = jlim(1):.5:jlim(end);