Automatic detection of rainfall at hourly time scales from mooring near-surface salinity in the eastern tropical Pacific

We explore the use of ocean near-surface salinity (NSS), i.e. salinity at 1 m depth, as a rainfall occurrence detector for hourly precipitation using data from the SPURS-2 (Salinity Processes in the Upper-ocean Regional Studies - 2) mooring at 10°N,125°W. Our proposed unsupervised learning algorithm consisting of two stages. First, an empirical quantile-based identification of dips in NSS enables us to capture most events with hourly averaged rainfall rate > 5 mm/hr. Over-estimation of precipitation duration is then corrected locally by fitting a parametric model based on the salinity balance equation. We propose a local precipitation model composed of a small number of calibration parameters representing individual rainfall events and their location in time. We show that unsupervised rainfall detection can be formulated as a statistical problem of predicting these variables from NSS data. We present our results and provide a validation technique based on data collected at the SPURS-2 mooring.