An improved post-processing technique for automatic precipitation gauge time series
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".