Articles | Volume 13, issue 6
Atmos. Meas. Tech., 13, 2979–2994, 2020
https://doi.org/10.5194/amt-13-2979-2020
Atmos. Meas. Tech., 13, 2979–2994, 2020
https://doi.org/10.5194/amt-13-2979-2020

Research article 05 Jun 2020

Research article | 05 Jun 2020

An improved post-processing technique for automatic precipitation gauge time series

Amber Ross et al.

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Cited articles

Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts of a warming climate on water availability in snow dominated regions, Nature, 438, 303–309, 2005. 
Bartlett, P. A., MacKay, M. D., and Verseghy, D. L.: Modified snow algorithms in the Canadian Land Surface Scheme: Model runs and sensitivity analysis at three boreal forest stands, Atmos. Ocean, 44, 207–222, 2006. 
Duchon, C. E.: Using vibrating-wire technology for precipitation measurements, in: Precipitation: Advances in Measurement, Estimation and Prediction, editied by: Michaelides, S., Springer, Berlin, Heidelberg, 33–58, https://doi.org/10.1007/978-3-540-77655-0_2, 2008. 
Geonor: T-200B series precipitation gauge manual for 600-mm, 1000-mm & 1500-mm capacity gauges, available at: http://geonor.com/live/wp-content/uploads/2014/10/T-200B-US-Manual.-rev-10.10-20150128.pdf, last access: 9 April 2019. 
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Short summary
The raw data derived from most automated accumulating precipitation gauges often suffer from non-precipitation-related fluctuations in the measurement of the gauge bucket weights from which the precipitation amount is determined. This noise can be caused by electrical interference, mechanical noise, and evaporation. This paper presents an automated filtering technique that builds on the principle of iteratively balancing noise to produce a clean precipitation time series.