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 3.0, 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: http://dx.doi.org/10.3402/tellusa.v66.21564.
Oceans 3.0 API filter: dataProductCode=IBPP
This data is available as a PNG or PDF.
Oceans 3.0 API filter: extension={png,pdf}
Unlike the ice buoy time series profile plots, separate plots are not created for the temperature and heating cycles. As specified in the options above, the one plot per measurement option includes the heating cycle data, whereas the daily and no breaks options only plot the temperatures. As shown below, this data product plots sensor elevation versus temperature, showing a profile of temperature or temperature change in the case of the heating cycle data. These plots are are suited for visualizing short time ranges, complimenting the long time ranges usable with the ice buoy time series profile plots. We recommend maximum 30 days for these plots. See examples below (click to enlarge.) These plots contain additional data such as the deployment metadata that is used to calculate the sensor elevation: the top of the ice and bottom of the snow sensors are both (normally) at elevation zero and the elevation decreases or increases (respectively) from them at the sensor spacing. See device attributes: iceBuoy_BottomOfSnow, iceBuoy_TopOfIce, iceBuoy_SensorSpacing. If this is not case, the sensor elevation plotted will be specified directly by the device attribute iceBuoy_sensorElevation. The locations of the top of snow, bottom of ice are also plotted, with the values coming from the scalar sensor values (these are derived sensors, contact us or information on the algorithm, internal documentation is here). An external air temperature sensor is often used to aid in the derivation of the snow thickness. Data are from it are also plotted.
Please note that these examples shown below are from incomplete test data.
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