Submersible pressure transducers such as Onset HOBO and Van Essen Diver data loggers can provide valuable stage data when compensated with barometric pressure readings. However, when submersible transducers are deployed in the air, they often prove unreliable as verification for stage records within set uncertainty limits. This unreliability comes from overlapping uncertainties of air pressure readings made by both the absolute and barometric pressure sensors. This talk proposes implementing a new computational method and tool during the compensation process involving the average misalignment between air readings made by both pressure sensors and adjusting all readings within a file by that misalignment. Preliminary results have been promising with most of the affected data aligning closer to verification stage records. This method will enable air-deployment of submersible pressure transducers to be more reliable as a method for peak-stage verification as well as increase the accuracy of air-deployed submersible pressure transducers without secondary data sources, such as tidal records and temporary gage deployments. To streamline the processing of data collected by submersible pressure transducers, we have developed a shiny app that implements this proposed new method automatically when processing air deployments in addition to allowing the processing of submersible pressure transducers via existing methods. This application is intended to produce more accurate data records by using a consistent compensation method between different sensor models while reducing the workload of the user processing the data.
