Testing and Development of Transfer Functions for Weighing Precipitation Gauges in WMO-SPICE
Bibliographic record
Abstract
Abstract. Adjustments for the undercatch of solid precipitation caused by wind were developed for different weighing gauge/wind shield combinations tested in WMO-SPICE. These include several different manufacturer-provided unshielded and single-Alter shielded weighing gauges, a MRW500 precipitation gauge within a small, manufacturer-provided shield, and host-provided precipitation gauges within double-Alter, Belfort double-Alter, and small Double-Fence Intercomparison Reference (SDFIR) shields. Previously-derived adjustments were also tested on measurements from each weighing gauge/wind shield combination. The transfer functions developed specifically for each of the different types of unshielded and single-Alter shielded weighing gauges did not perform significantly better than the more generic and universal transfer functions developed previously using measurements from eight different WMO-SPICE sites. This indicates that wind shield type (or lack thereof) is more important in determining the magnitude of wind-induced undercatch than the type of weighing precipitation gauge. It also demonstrates the potential for widespread use of the previously-developed, multi-site single-Alter shielded and unshielded transfer functions. In addition, corrections for the lower-porosity Belfort double-Alter shield and a standard double-Alter shield were developed and tested using measurements from two separate sites for the first time. Among all of the manufacturer-provided shields tested, with an average undercatch of about 5 %, the Belfort double Alter shield required the least amount of correction, and caught ~ 80 % of the reference amount of precipitation even in snowy conditions with wind speeds greater than 5 m s−1. The SDFIR-shielded gauge accumulated 98 % of the Double-Fence Automated Reference (DFAR) precipitation amount on average, accumulated 90 % of the DFAR accumulation in high winds, and was almost indistinguishable from the full-sized DFAR used as a reference. In general, the more effective wind shields, that were associated with smaller unadjusted errors, also produced more accurate measurements after adjustment.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".