Detection of Rainfall Events Using Underwater Passive Aquatic Sensors and Air–Sea Temperature Changes in the Tropical Pacific Ocean

Abstract

Several years of long-term high temporal resolution ocean ambient noise data from the tropical Pacific Ocean are analyzed to detect oceanic rainfall. Ocean ambient noise generated by rainfall and wind are identified through an acoustic discrimination process. Once the spectra are classified, wind speed and rainfall rates are quantified using the empirical algorithms. Rainfall-rate time series have temporal resolutions of 1 min. These data provide a unique opportunity to study the rainfall events and patterns in two different climate regions, the intertropical convergence zone (ITCZ) of the tropical eastern Pacific (10° and 12°N, 95°W) and the equatorial western Pacific (0°, 165°E). At both locations the rain events have a mean rainfall of 15 mm h?1, but the events are longer in the eastern Pacific. After the rain event is defined, the probability that a rain event can be detected using the change in air?sea temperature often associated with the rainfall is investigated. The result shows that the rain event accompanied by the decrease of air temperature is a general feature, but that using the temperature difference to detect the rainfall has a very high false alarm rate, which makes it unsuitable for rainfall detection.