Articles | Volume 13, issue 6
Atmos. Meas. Tech., 13, 2979–2994, 2020
Atmos. Meas. Tech., 13, 2979–2994, 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.

Related authors

Evaluation of the WMO Solid Precipitation Intercomparison Experiment (SPICE) transfer functions for adjusting the wind bias in solid precipitation measurements
Craig D. Smith, Amber Ross, John Kochendorfer, Michael E. Earle, Mareile Wolff, Samuel Buisán, Yves-Alain Roulet, and Timo Laine
Hydrol. Earth Syst. Sci., 24, 4025–4043,,, 2020
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
Separation of convective and stratiform precipitation using polarimetric radar data with a support vector machine method
Yadong Wang, Lin Tang, Pao-Liang Chang, and Yu-Shuang Tang
Atmos. Meas. Tech., 14, 185–197,,, 2021
Short summary
An approach to minimize aircraft motion bias in multi-hole probe wind measurements made by small unmanned aerial systems
Loiy Al-Ghussain and Sean C. C. Bailey
Atmos. Meas. Tech., 14, 173–184,,, 2021
Short summary
Interpolation uncertainty of atmospheric temperature profiles
Alessandro Fassò, Michael Sommer, and Christoph von Rohden
Atmos. Meas. Tech., 13, 6445–6458,,, 2020
Short summary
Unsupervised classification of snowflake images using a generative adversarial network and K-medoids classification
Jussi Leinonen and Alexis Berne
Atmos. Meas. Tech., 13, 2949–2964,,, 2020
Short summary
Retrieval of eddy dissipation rate from derived equivalent vertical gust included in Aircraft Meteorological Data Relay (AMDAR)
Soo-Hyun Kim, Hye-Yeong Chun, Jung-Hoon Kim, Robert D. Sharman, and Matt Strahan
Atmos. Meas. Tech., 13, 1373–1385,,, 2020
Short summary

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,, 2008. 
Geonor: T-200B series precipitation gauge manual for 600-mm, 1000-mm & 1500-mm capacity gauges, available at:, last access: 9 April 2019. 
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.