Processing and Quality Control (QC) of ADCP current data was done using QARTOD Recomendations. The following are the methods used to achieve the final, processed result. The processing was done using a code created by Cody Benton titled "ADCP_Processing.m" It and more information can be found on GithHub https://github.com/Cody-Benton/ADCP_Processing The raw binary ADCP data is imported to Matlab and the matlab function “rdradcp” written by R. Pawlowicz is used. If the ADCP was set to measure waves then RDI’s WavesMon software must be used before importing the data to Matlab. The resulting .PD0 file is used. .PD0 file. The final data has had pre and post-deployment data, faulty pressure measurements and extra fields removed. The pre and post-deployment data are found and removed using several methods. The first method is by looking for depth less than 5m but whose mean depth is greater than the deployed depth. This would correspond to the data while the ADCP is being deployed or while it is being retrieved. These intervals vary but tend to be every few months. The mean must be greater than the deployed depth because a failing pressure sensor could read any value as it drifts over time. That means the entire data set would be thrown out if say the sensor was reading 3m the whole time. So we check that the pressure sensor is recording a depth that is somewhat close to the actual depth before screening the values that correspond to a less than 5m depth. The second method for finding the pre and post-deployment data is looking for an echo intensity less than 90 decibels at the surface. The sea surface will result in the highest values for echo intensity. A low echo intensity here most likely indicates the ADCP is pinging in air and not water, and the echo intensity will be lower in air than water. 90 decibels was chosen as a threshold as this is significantly below the average for the surface. The next step is to manually determine the pre and post deployment data. This is done by visually looking at some data. Section 4 of the ADCP_Processing code will create an image with the instruments heading for the first 10 ensembles and last 10 ensembles. If the heading appears to be unstable near the beginning or end of the time series the data is screened. The echo intensity is also screened in a similar manner for the beginning and end of the time series. Although we screened some pressure at the beginning, there could still be erroneous pressure data due to a failing or compromised pressure sensor. The pressure data is plotted and checked for validity, if the pressure data appears to be wrong or drifting over a deployment, it will be deleted from the data set. After this step the pre and post deployment data should be completely screened and we are now ready to apply the QC measures. Three QC tests are applied to the data. These QC tests are a velocity error test, a correlation magnitude test and an echo intensity test. These tests were recommended by documentation provided by Quality Assurance/Control of Real Time Oceanographic Data (QARTOD). Since OB27 is not a real time mooring, not all tests are applicable. A presentation on QC of ADCP by Australia’s Commonwealth Scientific and Industrialc Research Organization (CSIRO) was also helpful in developing these QC tests. The thresholds for these tests will vary depending on the region the ADCP is deployed. The velocity error test screens any data that has an error velocity greater than the threshold of 0.05m/s. This value screened ~75% of the values above the surface, which we know are bad, while keeping the vast majority of the values in the water column. Because multiple QC tests were being implemented we could use a less stringent threshold and try to keep as many good values as possible. The second test is a correlation magnitude test. The threshold used for the correlation magnitude was set to 110. This threshold screened about 80% at the surface boundary and approached 100% the farther above the surface you go. The final QC test uses echo intensity. The echo intensity for each beam is used and the difference between bins is determined. If the echo intensity increases by more than 10 decibels between bins, all the data above that point is screened. This resulted in nearly 100% of the data being screened at the surface and above. This test is not applied to the bottom half of the water column. This is to prevent good data near the bottom from being screened. Sediment in the lower level of the water column can cause a sudden increase in echo intensity. When all three tests are applied we get a good data set with erroneous data removed. The mean depth from pressure or echo intensity is used in the final step to screen any values above the surface that may have passed by the QC tests. Echo intensity is used to calculate a conservative estimate of the surface when there is no valid pressure data. This is done by finding the first bin where the echo intensity test fails, then determining that to be the surface. This tends to be 1-2m below the actual surface.