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Overview of Quality Control Measures
There are 4 main QC steps plus five added quantities in the Level 2 QC data, relative to Level 1 data. These are discussed in detail in what follows.
(Updated December 2023)
DATA QC1. Sensor Bias
a) Ocean Pressure Sensors
2. Drift Correction
Bias in each ocean pressure (OP) was corrected by finding a period early in the data record, when the pressure measurements were relatively constant (with calm winds and no ice ridging). The median of those pressure values was found. The nominal pressure depth was subtracted from the median. This difference was then added to the Level 1 OP data. For example, if it is known that a buoy has an OP sensor at 60m, but the median value of pressure measurements during calm winds and no ridging (when we assume that the buoy is hanging straight down) is 62 db, then the bias is 60-62= -2. This -2 would then be added to all the OP readings for that sensor.
Two other factors influence OP: (1) EFFECT OF SEA LEVEL PRESSURE (SLP) VARIABLITY ON OCEAN PRESSURE (OP) VALUES: When SLP changes, the OP sensors feel this change. For example, an SLP decrease of 5 db leads to an OP decrease of the same magnitude, which could then be misinterpreted as a rise of the OP sensor in the water column owing to wind forcing and/or ridging. Each of our three manufacturers deals with this issue differently: • MetOcean does not account for this effect; we corrected for it in the Level 2 data. • Pacific Gyre does correct for the effect in their reported OP sensor data. • Marlin-Yug does not account for the effect, but our correction generally degrades the quality of OP sensor output for reasons unknown at this time, so we did not apply the correction for these buoys. The effect of SLP on OP is generally small. An analysis of 2010-2015 SLP data from Arctic UpTempO buoys results in an average SLP of 10.14db +/- 0.13db. This means that 95% of the time, the SLP anomaly will be within +/- two standard deviations (2 * 0.13) of the mean. Thus, after biases are removed, SLP variability impacts OP by +/- 0.26 db, far less than the usual OP sensor accuracy of +/- 1 db. (2) EFFECT OF OCEAN DENSITY VARIABILITY ON OCEAN PRESSURE VALUES • Pressure = (density)(gravity)(height) Density range of top 60m of Arctic: 1021 to 1028 kg/m^3 Gravity: 9.81 m/s^2 Height: 60m --> The maximum error is then |0.41 db|, or +/- 0.2 db over the total possible range; the error is likely much smaller for any particular buoy’s drift. Some UpTempO buoys have both temperature and salinity with which to compute density at some point in the water column, but most do not. Thus a calculation of true density is impossible without additional information, and so this effect is NOT accounted for in OP sensor data. b) Thermistors
Most buoys’ thermistors are calibrated against a higher quality sensor by the manufacturer in a tank.
Additionally, CTD profiles are often (but not always) available for further calibration, either in
seawater near the manufacturer’s facility or at the deployment site or both. Bias calculated using
field profiles typically uses only the part of the profile that is nearly isothermal, discounting
depths with rapid temperature changes. Calibration in the field is very useful, but typically involves
space and time offsets between buoy and CTD data which limit the accuracy of this procedure. Nonetheless,
the comparisons are generally pretty good. When this information is available, it is included in the data file header.
Bias corrections and information will appear in the data file headers as follows.
"Overall bias" for thermistors refers to buoy minus CTD values.The thermistors on our early buoys from 2010 and 2011 had rather large biases across all the thermistors with respect to field CTD casts. For these buoys, thermistor data have been de-biased in the Level 2 QC data. From 2012 onward, all buoys' thermistors have generally performed within “spec” i.e., with biases relative to CTD data that are within the manufacturers’ quoted accuracy. For these buoys, Level 2 QC data have not been modified from the Level 1 data. 
a) Ocean Pressure Sensors
OP data from 2010 and 2011 buoys drifted over time toward deeper values. This was corrected by
fitting a geometric function to the mean drift, and then adjusting the original data. Subsequent
ocean pressure sensors used on the UpTempO buoys did not have this problem.
b) Thermistors
Drift in the ocean pressure sensors was corrected by fitting a geometric function to the pressure time series:  Where P-hat = modeled pressure (dB), t = time (days), and A,B, and C are the parameters found to model the drift in the pressure sensor. Drift was corrected for a total of 12 buoys. Eleven of these buoys were equipped with two pressure sensors, one at 20m, and the other at 60m nominal. The remaining buoy had only one pressure sensor at 25m nominal. For the 20m pressure sensors, "A" ranged from 0.036 to 0.61 dB/day, "B" ranged from 0.17 to 0.64, and "C" ranged from 20.79 to 26.8 dB. For the 60m pressure sensors, "A" ranged from 0.21 to 1.34 dB/day, "B" ranged from 0.16 to 0.46, and "C" ranged from 61.32 to 63.19 dB. To correct for the drift, we then found the difference between the modeled pressure values and the first ocean pressure value in the time series (after initial adjustment upon deployment, see Section 4c). We then corrected the ocean pressure values by subtracting this time series of differences from the ocean pressure time series.  Where P is the pressure that the ocean pressure sensor reported. The A, B, and C parameters appear as follows in the headers of the data files for the buoys that required this corrections: 
High-accuracy thermistors from Seabird are used on some buoys, and these have very small drift
that we have assumed is negligible over the lifetime of our buoys. The drift in other thermistors
has not been assessed at this time (summer, 2023) and so has been assumed to be negligible as well.
We have not been able to recover a buoy at the end of a long drift for recalibration. Preliminary
analysis of long-lived buoys of the difference in winter SSTs from one year to the next is generally
less than +/- 0.1 degC.
3. Range check
a) Capped Values
4. Miscellaneous
Each quantity an UpTempO buoy reports has a range of acceptable values associated with it.
These upper and lower values are referred to as the "caps"
in this report. Often when there is an error in data reporting for a quantity, the upper or lower
cap is returned. For example, MetOcean UpTempO buoy thermistors have an acceptable reporting range from -45C to 36.91C.
Values of -45C and of 36.91C are clearly bad readings for temperature. Buoys from different
manufacturers and batches can have varying acceptable ranges of data. To make our data more
uniform across different buoy makes and models, almost all of these "capped values" have been
replaced with the value -999. There are some exceptions in cases where a reported value realistically
b) "Wrapped" Values
hit the cap value, in which cases we did not change the value to -999. i) Ocean Pressure Sensors
Ocean pressure sensors have a minimum allowed value of 0db. Since these sensors are typically at 20, 40, or 60m,
a 0db report is usually filtered out by giving it a value of -999. At deployment, however, 0db readings are physically
realistic. Ocean pressure sensors can also be pulled up to surface in rare cases of extreme ridging. These events are
recognizable in the data, and the resulting 0db reading are left in the data.
ii) Thermistors
Some of our older buoys had a minimum thermistor range of -5C.
While ocean temperatures can never obtain a temperature this low, ai
r/ice/snow temperatures easily get much lower. Surface and near-surface thermistors of a buoy locked in ice can expose these thermistors to air or ice temperatures and can therefore realistically drop below this -5C threshold. Newer buoys don't have a minimum temperature cut off. Reported temperatures have been as low as -25C. All of these data remain as the reported temperature values
in the level 2 data files.
iii) Conductivity sensors
Salinity measurements generally range between 20 and 40 psu, but can get as low as 10 psu at the surface
in ice melt areas. We are conservative in our editing out of salinity data based on range.
This is only an issue for thermistor data.
c) Unphysical Values
Buoys report within a range of allowed values. For most buoys, if the temperature is outside of that range, the buoy will report the upper or lower limit allowed, i.e., a "capped values." Some Pacific Gyre buoys since 2014 behave a little differently. Instead of "capping" at the minimum or maximum allowed value, the reported temperature will "wrap around" to the other end of the allowed range. As an example of how this occurs, let's consider a case where the actual temperature is -6C, and the allowable range of temperatures for the sensor is between -5C and 40C. The value the buoy will report in this case is (-6 - (-5)) + 40 = -1 + 40 = 39C. To figure out the true temperature then we use the following formula: True Temperature = reported value - maximum allowed value - minimum allowed value From the example above this works out as: True Temperature = 39 - 40 - 5 = -6 In practice, the only wrap-around we see is for temperatures colder than the allowed minimum, which happens when thermistors are in winter ice/snow/air, which is the case for some deployments or when ice grows and/or ridges around a buoy. 
i) Ocean Pressure Sensors
The sensors on the UpTempO string can easily ride above their respective nominal
depths when subjected to winds or currents, but they do not typically get pushed
below nominal. Ice can push them under, but when this happens, the buoy cannot
report. Therefore, after pressure biases have been applied
and pressure spikes have been smoothed out, pressure measurements reading deeper than nominal are
set to the nominal value.
ii) Thermistors
Temperature values were filtered out (given a value of -999) when the values were
deemed unreasonably warm:
iii) Conductivity sensors
• Temperatures greater than 20C • Temperatures greater than 10C below 20m • Temperatures greater than 6C during the months Jan to May, Oct to Dec
Salinity values were filtered out (given a value of -999) when the values were grossly
discontinuous.
Range Check information will appear in the data file header as follows:
a) Inaccurate Buoy Locations
Inaccurate buoy 2. I need sensor information for several buoys. Let's start with 2022: 04, 06, 07, 08
manufacturer, model #, and webpage (if you give me the first two, I'll get the third)Methodlocations are identified as follows:
Prior to 2021:
We remove all data from the buoy when an inaccurate location is found.
• Visual inspection of discontinuous jumps in the buoy track. 2021 and later: • Pacific Gyre buoys: GPSquality variable < 3. • MicroSWIFT buoys: Maximum instantaneous speed > 2 m/s b) Initial Adjustment
It often takes a buoy a few reports before its thermistors
and conductivity sensors equilibrate with their surroundings. When this appears to be
the case, we have filtered these first few values for all sensors in the string.
c) Noisy Values
i) Ocean Pressure Sensors
Ocean Pressure values can vary quite a bit, especially if the weight at the bottom
of the string is light. But it almost always varies smoothly, with a high degree
of correlation between all the pressure sensors on the string. Ocean pressure values
that are uncorrelated visually from one reading to the
next and are highly variable are filtered out.
ii) Thermistors
One mode of failure for a buoy is for it to get concussed with ice causing damage
to its electronics. The evidence of this failure is a sudden jump in the time series, followed by
either unphysical values, or unusual behavior. This is recognizable by eye, but is
difficult to filter automatically. In these situations, each thermistor time series
was analyzed to identify at exactly the point in time the failure occurred, and the
remainder of the series was removed since the sensor can no longer be trusted. This type of
failure is much more common near the end of a buoy's life.
d) Steady Values
 iii) Conductivity Sensors
Ocean salinity values that are uncorrelated visually from one reading to the next are filtered out.
Usually we expect that when a thermistor or conductivity sensor fails,
it will begin reporting either the minimum or the
maximum value of its allowed range, or report no value at all. In some cases though, the buoy will choose to
report some other constant value for the remainder of its life, as shown below. We filter such values out of our Level 2 datasets.
e) Spikes
Definition:
f) Empty Data Columns RemovedA spike is defined as a point in a time series that either rises above or drops below its immediate neighbors before and after. For our analysis, spike magnitude is measured in units of ΔTemperature(C)/ΔHour (or dT/dHr) for thermistors, and ΔPressure(dB)/ΔHour (or dP/dHr) for ocean pressure. For each spike, we get two measures for dT/dHr for example, one with respect to the spike’s left neighbor, and one with respect to its right. The dT part of the calculation always proceeds by taking the spike point temperature minus its left neighbor’s temperature, and then the spike point temperature minus its right neighbor’s temperature. The dHr part of the calculation is calculated so that it is always positive. So positive spikes always end up with dT/dHr positive, and the opposite for negative spikes. We then choose the minimum of the two measurements for each spike to define the magnitude of the spike.  i) Ocean Pressure Sensors
Detecting errant spikes in the ocean pressure (OP)sensors depends on the how much vertical
movement can be reasonably expected given the thermistor string's bottom weight and the position of the OP sensor in the water column. Marlin-Yug
buoys, for example, have fairly light weight on the end of the thermistor string, and vertical movement as
much as 30dB/Hr in the 60m OP sensor is not unprecedented. An isolated spike of this magnitude could be bad data, though.
So how can we tell whether a 30dB/Hr spike genuinely reflects the motion of the thermistor string? We can tell by looking
at the other OP sensors on the string, if they exist. The plots below show the relative motion between OP sensors located at
different nominal depths on the same string. The x-axis on both plots is showing how much shallower than nominal the
60m OP sensor is, while the y-axis is showing the same for the 20m OP sensor in the first plot, and the 40m OP sensor in the second.
The colors indicate the buoy's manufacturer.
ii) Thermistors
The first plot below (∆OP60 vs. ∆OP20) can be fit pretty well with a polynomial model of the form ∆OP20 ~= A * ∆OP60 + B * ∆OP60^2 + C. This is shown as the dashed green line. The farther from this line a data point falls the more suspect it is. For simplicity, the solid green lines enclose 99% of the data shown, and any point falling between them is passed as good. Points that fall outside of this range are not necessarily bad, but they are flagged for closer inspection. 
 Not all of the points in these plots are spikes, and not all spikes that fall outside the green lines are bad. For instance, in the first plot above we see many points above the upper green line that are actually the result of "ridging", where the buoy has been physically pulled up in the water column by an ice ridging event. For these data points, the deepest possible values for the 20m and 60m sensors has been moved up in the water column by the same amount. If we subtracted this amount from both, we would see that the points fall into the expected band. All points outside of the green 99% bands are visually inspected for bad data. Some buoys may have only one functional pressure sensor. An analysis of the behavior of OP sensors depending on the buoy manufacturer and the sensor depth was done to determine reasonable spike magnitudes. An example of this analysis is shown in the plot below where the difference from nominal is plotted for each of the 4 possible OP nominal depths for Marlin-Yug buoys. The red line for each OP sensor shows the point below which 99% of the data exists. Spike magnitudes greater than these values are flagged as suspect. A similar analysis was done for Pacific Gyre and MetOcean buoys. Results are listed in the table that follows.  The table below lists the spike magnitude cut-offs for the various buoys and OP sensors in situations where other OP data is not available.
Under the influence of strong winds or ice ridging, the
thermistors can be pulled upward into different thermal stratification regimes. This can
cause "spikes" that are real and which should not be removed. In order to deal with this,
we consider not just an individual thermistor's time series, but also data from other
thermistors on the sensor string. A spike is defined as a single point that is at least 1C above the values immediately before and after. The spike's temperature is set to -999 C if its dT amplitude is greater than the range of "non-spiky" values found within +/- 1 day and +/- 3 meters of the thermistor. A complete explanation of what we did follows. When to filter a spike: The next issue to address is at what magnitude do we choose to flag a spike for filtering? Choosing a constant threshold based on what should be physically possible seems wise, but it’s not. The problem is that thermistors cannot be relied upon to stay at their nominal depths, and there can be wide variations in temperatures at different depths. On a windy day the bottom thermistor of a buoy with a light weight on the end can rise in the water column as much as 20m. Depending on the thermocline, thermistors in the transition zone can experience a wide range of temperatures resulting in wild swings in readings that would be unexpected in a stationary thermistor. To address this issue, we use the data from the buoys to tell us what should reasonably be expected. This is done by first finding all NON-SPIKE values at every calculated depth in the time series. Then at any given spike point, we find the minimum and maximum temperature from the surrounding non-spike values over a constant ∆time and ∆depth. These surrounding points are centered on the spike point in time, and also in depth if we are using calculated depths. When calculated depths are not available, we have to use nominal depths. In this case, we only search above the spike point in depth since we don’t expect the buoy to ever go deeper than the nominal depth of the thermistor. The threshold for filtering this spike would then be the (max Temp – min Temp) for all points found around the spike point in question. If the magnitude of the spike exceeds this threshold, it is a candidate for filtering. To be filtered, it must in addition have a spike magnitude greater than 1 C/hr, and it must have a temperature value outside of the minimum and maximum temperature range of the surrounding points. Here is a summary of steps in the process: 1. Find the spike magnitude of all points in the time series data from all thermistors (BOX 1 in the figure below). Non-spike points should be assigned a spike magnitude of 0. Using the non-spike points and calculated depths of each thermistor, construct gridded temperature profiles at 1 m resolution by linearly interpolating between the calculated depths. 2. For each point in a temperature time series, search the gridded temperatures (found in step 2) based on the chosen ∆time and ∆depth criteria. I will refer to this as the “search box”. The search box is centered on the (time, calc depth) point. Identify the maximum and minimum temperatures found in the search box. (BOX 2 in the Figure below). 3. The threshold spike magnitude at each time series point is then: max Temp – min Temp Where we are using the temperatures found in step 3. (BOX 3 in the Figure below). 4. For a spike to be filtered, it must meet three criteria: • The magnitude of the spike exceeds the spike threshold found in step 3. (BOX 4 in the figure below) • The magnitude of the spike is greater than 1 C/hr (BOX 5 in the figure below). • The value of the spike is outside of the minimum and maximum temperature range found in step 2. (BOX 5 in the figure below) 5. If a temperature data point is removed because it exhibits the spike criteria above and there is an associated conductivity sensor at the same depth, that salinity measurement is also removed, regardless of whether it looks reasonable or not. 
DETERMINING THE SEARCH BOX PARAMETERS Before this method of flagging spikes can be applied, we must determine what values of ∆time and ∆depth to use. For the ∆depth parameter, we can base our choice on the accuracy of the calculated depths for our thermistors. Analysis has indicated that we do not expect the calculated depths to be off by more than 2m. Since we would rather miss filtering a bad point than filter a good one, we use 3m, giving a total ∆depth of 6m (+/- 3m). The ∆time parameter becomes especially important when a buoy is near the boundary of two different water masses. To get the full character of the possible temperature fluctuations in space we use a total ∆time of 2 days (+/- 1 day). To get an idea of how sensitive this spike flagging algorithm is to different values of ∆depth and ∆time, we applied it using a grid of (∆depth, ∆time) parameter pairs. The results are shown below. A dot on the plot below shows that our chosen parameters of ∆Depth = 6m and ∆Time = 2 days results in about 1 spike per 1000 being flagged for filtering.  WHERE THE SPIKE ALGORITHM FAILS There are a couple situations where the spike algorithm fails. 1. Double spikes: This is when two spikes in a row occur, which technically doesn't even qualify them for the title "spike". 2. Sparse data: If the points on either side of a spike are distant from it in time, the spike magnitude can not be accurately assessed. In situations such as these, where it is obvious to the eye that the spikes should be removed even though the algorithm fails to flag them, the points were filtered out by hand.  
If a thermistor or ocean pressure sensor reported no good data, i.e. all values were set to -999, then
the column of data for that sensor was removed.
g) Duplicate Data Rows Removed
If the raw data contain two or more rows with identical date/time and lon/lat we keep only the last one.
Information about miscellaneous filtering and removed data columns appears in the data file header as follows:  ADDED QUANTITIES5. Temperature Sensor Depth Calculation
Thermistor depths are presented in three ways:
6. Open Water or Ice/Near Indicator1. Nominal Depths: This is the default depth provided in Level 1 Raw Data, assuming that the buoy is hanging straight vertical in the water column. 2. Estimated depth: This is a depth calculated by Marlin-Yug buoys, based on a cable model. However, this cable model is based on their original 80m long drogued buoy, which we used in some years and thus are reported in Level 2 QC data for those buoys. In later years, Marlin-Yug made special buoys for us, with no drogue and a different arrangement of sensors. Thus this cable model may not be optimal for these buoys. The estimated depths are not reported if the calculated depths are available. 3. Calculated depth: For buoys measuring both pressures and temperatures we use linear interpolation between ocean pressure sensors to calculate thermistor and conductivity sensor depths for Level 2 QC Data. Details of this calculation follow:
Depths for the thermistors were calculated based on the values reported by the Ocean Pressure (OP) sensors.
The OP sensors give the “true depths” at their respective nominal depths. The calculated depth at the top most thermistor
was generally assumed to be equal to the nominal depth.
Linear interpolation to the other thermistors was then applied.
Special Cases A. Thermistors can occasionally be pulled up into the ice by ridging events. Ridging is determined by looking at the pressure time series. The depth of the ridged thermistors were set to 0 m depth before the interpolation. In the case of no pressure measurements, but valid temperature or salinity data, depths were set to the nominal depth value. However, if temperature data were colder than freezing, the associated depth is set to 0m. Same for salinity depths at or shallower than the below freezing temperature depth. We basically assume the sensors are in ice. B. If an OP sensor fails but one or more are still working, we set the missing pressure to invalid and perform the depth calculation normally, interpolating through the bad pressure sensor. This gives a depth value at the invalid pressure sensor.
A column has been added to the UpTempO time series indicating whether the buoy is in/near ice,
or in open water.
7. First Wet ThermistorThe primary predictor for this "Open Water or in/near Ice" indicator is the buoy’s proximity to ice concentration values greater than or equal to 0.15. The ice data used for this analysis comes from NSIDC’s ice concentration fields, which are derived from satellite passive microwave values. This field has some inherent error especially near the ice edge, so a secondary criteria for determining whether a buoy is in/near ice is the temperature of the uppermost thermistor. If the temperature is less than -1.2C, we bias the ice indicator toward “in/near ice”. We do this by increasing the allowable distance to satellite derived ice while maintaining an “in/near ice” classification. The satellite ice data is on a 25km grid, so the buoy can be up to 17.7km away from a grid point while being in an ice pack (see the schematic below). If the buoy is less than this distance from an ice concentration grid cell with a value greater than 0.15, it is labeled as being in/near ice. If it is farther than 17.7km away but less than 35.4km away, it is only labeled as "in/near ice" if the uppermost thermistor value is less than -1.2C. 
The first wet thermistor (FWT), or First tpod in water (name used in older data files), is our best guess
for which thermistor on the buoy is the uppermost one in water (as opposed to in air or snow or ice).
Thus it points to the thermistor that gives our best
estimate of Sea Surface Temperature (SST). For example, if a buoy with a thermistor in the
hull is deployed on an ice floe (with the sensor string hanging down through a hole into the ocean),
then this hull thermistor is providing air or snow or ice values, not SST. Another example might
be if a buoy deployed in open water is later engulfed in ice, and then experiences sensor string
uplift owing to an ice ridging event. In this case, there might be a number of thermistors on the
string that start reading icy values. The FWT value itself is the zero-based index of the
shallowest thermistor in water. So, for example, FWT=0 if the
uppermost thermistor on the buoy is in the water. FWT=1 if the uppermost
thermistor is in air/ice/snow, but the next one down is in water. FWT=2 if the top two thermistors
are in air/ice/snow, etc.
8. Sea Surface Temperature (SST)Methodology for determining FWT
The FWT is defined as the shallowest thermistor measuring the
temperature of the ocean. The data from the FWT should read warmer
than -1.9C. Sea Water freezes at temperatures generally no colder than -1.8C depending on salinity. We use
a limit of -1.9C to allow some sensor inaccuracy.
If the shallowest thermistor is reading warmer than the freezing temperature of ocean water, FWT is set to 0. If the Water/Ice indicator says the buoy is in/near ice, the FWT gets set to 1. However, if T0 is reading warmer than -1.9C FWT and the Water/Ice indicator is 1, we set FWT to 0, because a Water/Ice indicator of 1 can mean near ice, not necessarly in ice. The temperature and the depth of that measurement are then the sst and depth of sst. If, however, there is an ice ridging event, the thermistors engulfed in ice are obviously not measuring ocean temperature. The FWT is then zero-based index of the first thermistor below those. If the temperature reading at the FWT is invalid or missing we look to the next thermistor deeper down the thermistor chain, until we find a valid temperature measurement. The sst and depth of sst are set to the temperature and depth of the FWT. If there are no valid temperature data reading warmer than -1.9C then all three variables: FWT, SST and Depth of SST are all set to invalid. Frequently the thermistor chain is ripped away from the buoy during its lifetime, where only the hull sensor remains. During summers the hull thermistor can read air temperature or melt pond temperature. This is evident when the hull data are unphysically high and when diurnal cycles are pronounced. The water/ice indicator can also say the buoy is in/near ice, but there is no evidence of ridging. At the end of summer when the measurements shown little or no diurnal cycle, we can say the hull thermistor is reading ocean temperatures and set those values to SST, FWT=0 and depth of SST to the depth of the hull thermistor.
SST is our best estimate of the shallowest ocean temperature. It is the temperature measurement from
the first wet thermistor, discussed above.
9. Depth of Sea Surface Temperature
This depth is the calculated depth of the first wet thermistor (FWT) in meters.
10. First Wet Conductivity Sensor
The first wet conductivity sensor (FWC) is our best guess
for which conductivity sensor on the buoy is the uppermost one in water (as opposed to in air or snow or ice).
Thus it points to the sensor that gives our best
estimate of Sea Surface Salinity (SSS). As discussed in 7. above, buoys can be pushed out of the water
by ice in a ridging event. There might then be a number of sensors on the
string that start reading icy/air/snow values. The FWC value itself is the zero-based index of the
shallowest conductivity sensor in water. So, for example, FWC=0 if the uppermost thermistor on the
buoy is in the water. FWC=1 if the uppermost conductivity sensor is in air/ice/snow, but the next one
down is in water. FWC=2 if the top two conductivity sensors are in air/ice/snow, etc.
Methodology for determining FWC
The FWC is defined as the shallowest conductivity sensor measuring the salinity of the ocean. The salinity and the depth of that measurement are then the sss and depth of sss. To find the FWC we look to pressure
data and the water/ice indicator. If the pressure data tells us there is no ridging of the sensors into ice
(and the ice conc bares that out (?)), then FWC = 0, the first
conductivity sensor in the buoy chain.
11. Sea Surface Salinity (SSS)If there is an ice ridging event, all sensors engulfed in ice are obviously not measuring the ocean. The FWC is set to the (zero-based) index of the first conductivity sensor below those. For instance, if there is 1 meter of ridging and the first conductivity sensor is 0.38m down the chain, then we set FWC = 1. If there are no valid salinity data reading at FWC, then we look down the chain of conductivity sensors until we find valid data. If there are no valid data from the ocean, then all three variables: FWC, SSS and Depth of SSS are all set to invalid. Occasionally at the end of a conducivity sensor's lifetime salinity measurements drop off rather precipitously. When this occurs we retain the salinity data in the level 2 data file but set FWC, SSS and depth of SSS to invalid. Frequently the thermistor chain is ripped away from the buoy during its lifetime, where only the hull sensor remains. During summers the hull thermistor can read air temperature or melt pond temperature. This is evident when the hull data are unphysically high and when diurnal cycles are pronounced. The water/ice indicator can also say the buoy is in/near ice, but there is no evidence of ridging. At the end of summer when the measurements shown little or no diurnal cycle, we can say the hull thermistor is reading ocean temperatures and set those values to SST, FWT=0 and depth of SST to the depth of the hull thermistor.
SSS is our best estimate of the shallowest ocean salinity. It is the salinity measurement from
the first wet conductivity sensor, discussed above.
12. Depth of Sea Surface SainityFrequently, when the conductivity sensor encounters ice, the measurements drop off preocipitously. Occassionally the buoy and sensor survive until the following summer when we see a similar precipitous rise in salinity values. Although we retain the entire salinity time series we only consider the data before the drop off and after the rise to be SSS. This example of the Level 1 salinity time series from the 2023_05 buoy shows reasonable ocean salinity values until the end of 2023 when the sensor freezes up. The values plummet until later the following July when again the sensor shows reasonable ocean salinity values. The reasonable values are considered SSS and are shown in red. 
This depth is the calculated depth of the first wet conductivity sensor (FWC) in meters.
Here's an example of what the QC portion of the final data file header will look like:  | ||||||||||||||||||||||||||||||||||||||||||||||||
UpTempOMeasuring the Upper layer Temperature of the Polar Oceans |
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