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

Ross, Amber; Smith, Craig D.; Barr, Alan

doi:https://doi.org/10.5194/amt-13-2979-2020

Articles | Volume 13, issue 6

https://doi.org/10.5194/amt-13-2979-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-13-2979-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 13, issue 6

Research article

|

05 Jun 2020

Research article |

| 05 Jun 2020

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

Amber Ross, Craig D. Smith, and Alan Barr

Data sets

Precipitation gauge time series used in evaluating post-processing filters A. D. Ross, C. D. Smith, and A. G. Barr https://doi.org/10.20383/101.0243

Model code and software

NAF-SEG Code (MATLAB) A. D. Ross, C. D. Smith, and A. G. Barr https://doi.org/10.20383/101.0243

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.