An assessment of two automated snow water equivalent instruments during the WMO Solid Precipitation Intercomparison Experiment
Bibliographic record
Abstract
Abstract. During the WMO Solid Precipitation Intercomparison Experiment (SPICE), automated measurements of snow water equivalent (SWE) were made at the Sodankylä (Finland) and Caribou Creek (Canada) SPICE sites during the northern hemisphere winters of 2013/2014 and 2014/2015. Supplementary intercomparison measurements were made at Fortress Mountain (Kananaskis, Canada) during the 2013/2014 winter. The objectives of this analysis are to assess automated SWE measurements against a reference, comment on their performance, and make recommendations on how to best use the instrument and interpret its measurements. Sodankylä, Caribou Creek and Fortress Mountain hosted a Campbell Scientific CS725 passive gamma radiation SWE sensor. Sodankylä also hosted a Sommer Messtechnik SSG1000 snow scale. The CS725 measurement principle is based on measuring the attenuation of soil emitted gamma radiation by the snowpack and relating the attenuation to SWE. The SSG1000 measures the mass of the overlying snowpack directly by using a weighing platform and load cell. Manual SWE measurements were obtained at the SPICE sites on a bi-weekly basis over the accumulation/melt periods using bulk density samplers. These manual measurements are considered to be the reference for the intercomparison. Results from Sodankylӓ and Caribou Creek showed that the CS725 generally overestimates SWE as compared to manual measurements by roughly 30 to 35 % with correlations (r2) as high as 0.99 for Sodankylӓ and 0.90 for Caribou Creek. The RMSE varies from 30 to 43 mm water equivalent (w.e.) and 18 to 25 mm w.e. at Sodankylӓ and Caribou Creek respectively. The correlation at Fortress Mountain was 0.94 (RMSE of 48 mm w.e.) with no systematic overestimation. The SSG1000 snow scale, having a different measurement principle, agreed quite closely with the manual measurements at Sodankylӓ throughout the intercomparison periods (r2 as high as 0.99 and RMSE from 8 to 24 mm w.e.). When the SSG1000 is compared to the CS725, the agreement is linear until the start of seasonal melt when the positive bias in the CS725 increases substantially relative to the SSG1000. Since both Caribou Creek and Sodankylӓ have sandy soil, water from the snowpack readily infiltrates into the soil during melt but the CS725 does not differentiate this water from the un-melted snow. This issue can be identified, at least during the spring melt, with soil moisture and temperature observations like those measured at Caribou Creek. With a less permeable soil and surface runoff, the increase in the instrument bias during melt is not as significant, as shown by the Fortress Mountain intercomparison.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".