An Overview of Surface-Based Precipitation Observations at Environment and Climate Change Canada
Why this work is in the frame
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Bibliographic record
Abstract
The objective of this paper is to provide an overview of the present status and procedures related to surface precipitation observations at Environment and Climate Change Canada (ECCC). This work was done to support the ongoing renewal of observation systems and networks at the Meteorological Service of Canada. The paper focusses on selected parameters, namely, accumulated precipitation, precipitation intensity, precipitation type, rainfall, snowfall, and radar reflectivity. Application-specific user needs and requirements are defined and captured by World Meteorological Organization (WMO) Expert Teams at the international level by Observing Systems Capability Analysis and Review (OSCAR) and WMO Integrated Global Observing System (WIGOS), and by ECCC user engagement initiatives within the Canadian context. The precipitation-related networks of ECCC are separated into those containing automatic instruments, those with human (manual) observers, and the radar network. The unique characteristics and data flow for each of these networks, the instrument and installation characteristics, processing steps, and limitations from observation to data distribution and storage are provided. A summary of precipitation instrument-dependent algorithms that are used in ECCC's Data Management System is provided. One outcome of the analysis is the identification of gaps in spatial coverage and data quality that are required to meet user needs. Increased availability of data, including from long-serving manual sites, and an increase in the availability of precipitation type and snowfall amount are identified as improvements that would benefit many users. Other recognized improvements for in situ networks include standardized network procedures, instrument performance adjustments, and improved and sustained access to data and metadata from internal and external networks. Specific to radar, a number of items are recognized that can improve quantitative precipitation estimates. Increased coverage for the radar network and improved methods for assessing and portraying radar data quality would benefit precipitation users.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it