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), that is, salinity at 1-m depth, as a rainfall occurrence detector for hourly precipitation using data from the Salinity Processes in the Upper-Ocean Regional Studies–2 (SPURS-2) mooring at 10°N, 125°W. Our proposed unsupervised learning algorithm consists of two stages. First, an empirical quantile-based identification of dips in NSS enables us to capture most events with hourly averaged rainfall rate of >5 mm h −1 . Overestimation 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. Significance Statement Continuous monitoring of precipitation in the ocean is challenging when a physical rain gauge is not available in the region of interest. Indirect detection of precipitation using available data, such as changes in ocean near-surface salinity (NSS) can be used to construct a virtual rainfall detector. We propose to combine data-based and model-based methods to detect rainfall without the use of a physical rain gauge. We use NSS and precipitation data from a mooring in the eastern tropical Pacific Ocean to develop and test the method.