Abstract
Abstract. The unconditioned data retrieved from accumulating automated weighing precipitation gauges are 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 min 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 primarily focuses on cold-season (October to April) accumulating automated weighing 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 Intercomparison 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 for the cold-season (October–April) and 97 % of the control total precipitation for all noise scenarios in the warm season (May–September). Among the filters, the fully automated NAF-SEG produced the highest correlation coefficients and lowest root-mean-square error (RMSE) for all synthetic noise levels, with comparable performance to the supervised and manually intensive NAF-S method. Compared to the O15 method in cold-season testing, 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. In warm-season testing, the NAF-SEG bias was lower or similar in 7 of 11 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 h latency. Because it cannot be implemented in real time, we recommend that NAF-SEG be used in combination with a simple real-time filter, such as the O15 or similar filter.
Highlights
Accurate precipitation measurements are crucial for a variety of applications, including water resource forecasting, future water availability, and hydrological and climate analysis and modelling (Barnett et al, 2005; Bartlett et al, 2006; Wolff et al, 2015)
To overcome the limitations of the operational 15 min filter (O15), NAF, and NAF-S techniques, we evaluated a moving-window modification of the NAF, implementing the NAF on 24 h overlapping windows, which we will call the segmented neutral aggregating filter (NAF-SEG)
This study introduces the NAF-SEG technique and examines its performance compared to the O15, NAF, and NAF-S methods
Summary
Accurate precipitation measurements are crucial for a variety of applications, including water resource forecasting, future water availability, and hydrological and climate analysis and modelling (Barnett et al, 2005; Bartlett et al, 2006; Wolff et al, 2015). The cumulative precipitation data output from automated weighing gauges is subject to noise, diurnal temperature oscillations, and negative drift from evaporation, which can often mean that the precipitation signal over short sampling periods is influenced or hard to detect (Rasmussen et al, 2012). It is necessary to filter the raw data to separate real precipitation events from signal noise and identify and remove periods with evaporation (keeping in mind that evaporation reduces the precipitation amount derived from the differential in bucket weight). Duchon (2008) suggests that errors due to the diurnal oscillation in Geonor T-200B gauges could be 1 %–10 % of the precipitation total. Three post-processing challenges in the derivation of “clean” precipitation time series are the focus of this study: mechanical and electrical interference, diurnal oscillations, and evaporation of the bucket contents
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
