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Ice Buoy Times Series Profile Plots

This data product is specialized for the SRSL Ice Mass Balance Buoy (SIMBA). SIMBA is simply a chain of temperature sensors (thermistors), string from a pole above and through and below the ice. There are normally 240 sensors spaced every 2 cm, and some may lay flat along the ice. The positioning of the sensors is documented in the device attributes for each deployment and device.  The sensor elevation as plotted is normally set with zero being ice level at the time the sensors are deployed, however, ice can be generated on top of the sensors with melt and re-freeze events. By observing the temperature gradients, one can detect the ice and snow thickness, which we have automated as derived scalar sensors. The same physical 240 temperature sensors are also used in heating experiments where a small current is applied to heat the environment and observe the temperature change. In Oceans 2.0, the heating cycles (1 & 2) get their own sensors, so in all, this type of device has over 720 sensors. Currently, the temperature data has a sampling rate of 6 hours and each heating cycle has a sampling rate of 24 hours; this may change in other deployments. Here is a good paper for reference:

Oceans 2.0 API filterdataProductCode=IBTSPP

Revision History

  1. 20170201: Initial Release
  2. 20180601: Major revision and made available to all

Data Product Options

Quality Control

For time series scalar data:

Raw Data

When this option is selected, raw data will be supplied in the data products: no action is taken to modify the data. In general, all scalar data is associated with a quality control flag. These flags are stored adjacent to the data values.

Oceans 2.0 API filterdpo_qualityControl=0

Clean Data

Selecting this option will cause any data values with quality control failures (QAQC flags 3, 4 and 6) to be replaced with NaNs. If the do not fill data gaps option is selected, data values with quality control failures will be removed. For all data products, when resampling with the clean option, any data with quality control failures are removed prior to the resampling (this rule applies to all resampling types: average, min/max, etc).

This is the default option for all data products.

Oceans 2.0 API filterdpo_qualityControl=1

File-name mode field

'clean' is added to the file-name when the quality option is set to clean data.

Resampling / Processing

For time series scalar data:

Resample Type:

  • None - no resampling. This is the default option for time series scalar data.

    Oceans 2.0 API filterdpo_resample=none

  • Average - the mean value within resample period (fixed-window averaging without overlap). This is also known as a 'box-car' or ensemble average. It is subject to the 70% data completeness QAQC check (see below) with the exception of engineering data or data from irregular or scheduled sampling. Only available with the clean data product option.

    Oceans 2.0 API filterdpo_resample=average

Resample Period:

Visible when an actionable resample type is selected, immediately to the right of the resample type. Current periods offered:

When resampling is selected:

  • The timestamps in the data series correspond to the centre of each resampling interval. (Data downloaded prior to 13 Feb 2013: timestamps were at beginning of interval). The resample interval always begins and ends at an integer multiple of the resample period, so minutes on the minute, hours on the hour, days on the day, etc.
  • If the date/time range on the search has limits that are within a resampling interval, the date/time endpoints are extended to include the entire resampling interval. For example, when daily resampling is selected from 03:00:00.000 on Monday to 20:00:00.000 on Thursday, the date range is extended to 00:00:00.000 on Monday to 23:59.59.999 on Thursday.
  • Note that tides are not filtered out in resampled products.
  • No anti-alias filtering is done. This is why only averaging and min/max are offered at this time. Box-car / ensemble averaging is an easily understood and ubiquitous process that is an effective low-pass anti-alias filter. For more information, see this page on data reduction and time-averaging.
  • All resampled data products are subject to an additional QAQC check on data completeness (except engineering data or data from irregular or scheduled sampling). If any resample period does not contain at least 70% of the expected data, the QAQC flag for this period will be a failure (6), unless overridden by a manual QAQC flag, see the QAQC page. For live data, it is quite likely that the last resample period will not be complete and will be flagged; this is especially obvious for plots. Future improvements will allow users to modify the data completeness threshold.

More options will be available in the future as we work to improve the data products, an example is Min/Max resampling (only available for plots). Feedback is welcomed and encouraged. For custom resampling, users can develop their own matlab code in CodeRunner in the ONC computing environment (users can access all production data and code). See CodeRunner Help for more details.

File-name mode field

The resample type and period are added to the file-name when resampling is selected. Example: 'avg1hour'.


This data is available as a PNG or PDF.

Oceans 2.0 API filterextension={png,pdf}

The temperature, heating cycle 1 and heating cycle 2 data are plotting in separate plots for the ice buoy time series profile plots.  These plots show the temperature as a heat map by elevation and time. These plots are best suited for visualizing long time ranges, complimenting the short time ranges usable with the Ice Buoy Profile Plots. We recommend minimum 30 days for these plots. For shorter time ranges less than 7 days or 4 days of complete data, the plots will switch from an image to a waterfall to provide more detail in individual profiles. See examples below (click to enlarge.) Please note that these example are from incomplete test data and shorter than normal time ranges.


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