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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/amt-2019-423
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2019-423
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 20 Dec 2019

Submitted as: research article | 20 Dec 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

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

Amber Ross1,2, Craig D. Smith1, and Alan Barr1,2 Amber Ross et al.
  • 1Environment and Climate Change Canada, Climate Research Division, Saskatoon, SK
  • 2Global Institute for Water Security, University of Saskatchewan, Saskatoon, SK

Abstract. The unconditioned data retrieved from automated accumulating precipitation gauges is inherently noisy due to the sensitivity of the instruments to mechanical and electrical interference. This noise, combined with diurnal oscillations and signal drift from evaporation of the bucket contents, can make accurate precipitation estimates challenging. Relative to rainfall, errors in the measurement of solid precipitation are exacerbated because the lower accumulation rates are more impacted by measurement noise. Precipitation gauge measurement post-processing techniques are used by Environment and Climate Change Canada in research and operational monitoring to filter cumulative precipitation time series derived from high-frequency, bucket-weight measurements. Four techniques are described and tested here: 1) the operational 15-minute filter (O15), 2) the Neutral Aggregating Filter (NAF), 3) the Supervised Neutral Aggregating Filter (NAF-S), and 4) the Segmented Neutral Aggregating Filter (NAF-SEG). Inherent biases and errors in the first two post-processing techniques have revealed the need for a robust automated method to derive an accurate noise-free precipitation time series from the raw bucket-weight measurements. The method must be capable of removing random noise, diurnal oscillations, and evaporative (negative) drift from the raw data. This evaluation focuses on cold-season (October to April) accumulating-precipitation-gauge data at 1-min resolution from two sources: a control (pre-processed time series) with added synthetic noise and drift; and raw (minimally-processed) data from several WMO Solid Precipitation Inter-Comparison Experiment (SPICE) sites. Evaluation against the control with synthetic noise shows the effectiveness of the NAF-SEG technique, recovering 99%, 100%, and 102% of the control total precipitation for low, medium, and high noise scenarios respectively. Among the filters, the fully-automated NAF-SEG produced the highest correlation coefficients and lowest RMSE for all synthetic noise levels, with comparable performance to the supervised and manually-intensive NAF-S method. Compared to the operational O15 method, NAF-SEG shows a lower bias in 37 of 44 real-world test cases, a similar bias in 5 cases, and a higher bias in 2 cases. The results indicate that the NAF-SEG post-processing technique provides substantial improvement over current automated techniques, reducing both uncertainty and bias in accumulating-gauge measurements of precipitation, with a 24-hour latency. Because it cannot be implemented in real time, we recommend that NAF-SEG be used in consort with a simple real-time filter, such as the operational O15 or similar filter.

Amber Ross et al.
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Short summary
The raw data derived from most automated accumulating precipitation gauges often suffers from non-precipitation related fluctuations in the measurement of the gauge bucket weights from which precipitation amount is determined. This noise can be caused by electrical interference, mechanical noise, and evaporation. This paper presents and automated filtering technique that builds on the principle of iteratively balancing noise to produce a clean precipitation time series.
The raw data derived from most automated accumulating precipitation gauges often suffers from...
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