Sub-Saharan Africa is one of the most vulnerable regions in the world to climate change. This is largely driven by the dependence on rain fed agriculture for food production. At the same time African climate observational networks have been in decline since the 1990s. A new kind of rainfall sensor (the intervalometer), which counts the arrival of drops at a piezo electric element, is tested during the Tanzanian monsoon season alongside tipping buckets and an impact disdrometer. Rainfall rates are derived from rainfall arrival rates using Marshall and Palmer’s (1948) exponential parameterisation. This parameterisation is defined independently of a notion of scale and therefore implicitly assumes that rainfall is a homogeneous Poisson process. Testing of the Poisson assumption shows that 22.5% of the total drops observed can reasonably be considered Poisson and that the main reason for Poisson deviations are non compliance with the stationarity criterion (36.7%) and the presence of correlations between drop counts (14.3%), particularly at higher arrival rates (ρa > 500 [m−2.s−1]). The total rainfall amount [mm] calculated from intervalometer measurements overestimates the tipping bucket value by a factor of approximately three. The overestimate is most likely due to poor calibration of the minimum detectable drop size (Dmin). A correction is applied to constrain the overestimates of mean drop size by the intervalometer parameterisation to the observed disdrometer measurements. The correction results in an improvement in the estimate of the total rainfall amount to within 10% of tipping bucket measurements. The total rainfall amount [mm] calculated from disdrometer rainfall arrival rates is within 5% of co-located tipping bucket measurements. The form of the mean drop size relation with arrival rate appears stable in time and space. The intervalometer shows good potential for use as a rainfall measurement instrument or to derive estimates of mean drop sizes.