Developing a robust quality control process for tipping bucket rain gauge data in R

We need an R workflow that could be applied to tipping bucket rain gauge data to flag suspect gauge tips that may be the result of gauge malfunction or snowmelt. Description The research team within the Ministry of Forests maintains a long-term climate dataset at the West Arm Demonstration Forest that includes tipping bucket rain gauges. We have implemented a semi-automated quality control process for daily air temperature data where suspect values are flagged using R code and either deleted or retained during manual review by a climate data expert. We are looking for a similar process for tipping bucket rain gauge data. The successful proponent will develop an R workflow to flag suspect tips due to gauge malfunction or snowmelt. This will include the following steps: 1. Import data from Campbell Scientific and Hobo data logger files. 2. Run data through a series of tests that will flag data as either good, suspect, erroneous or missing. 3. Create data visualizations that will support experts make a final determination during the manual review of the suspect data. The work will require close collaboration throughout the project between research scientists in the Ministry of Forests and the proponent to work through the logical framework behind the code, including the types of tests to use.