Comparison of Winter Precipitation Measurements by Six Tretyakov Gauges at the Valdai Experimental Site
Bibliographic record
Abstract
Analyses of long-term (1991Analyses of long-term ( -2010) ) intercomparison data quantify the consistency of winter precipitation observations by six identical Tretyakov gauges at the Valdai research station in Russia.Relative to the standard Tretyakov gauge, the mean catch ratios are 97 to 106% for dry snow, 94 to 104% for wet snow, 87 to 109% for blowing snow, 96 to 103% for mixed precipitation, and 98 to 101% for winter rain.The differences between the highest and lowest mean catches are about 10 to 11% for snow, 7% for mixed precipitation, and 3% for rain.On average, this difference is about 0.2 mm over the 12-hour observation period.The catch difference for blowing snow is much higher, up to 22%, or an average of 0.6 mm per observation.Comparisons of 12-hour observations show better consistency in gauge performance for low snowfall events and a large variation in gauge catch for high snowfall events.The differences in 12-hour snow catches are mostly less than 2 mm among the six gauges.The differences in the 12-hour observations are less than 1% for rain and 4% for mixed precipitation.Close linear relationships exist between the 12-hour gauge observations for all precipitation types.The maximum differences in gauge snow catches increase very weakly with wind speed, and higher differences are associated with warmer temperatures, from -5C to 0C.There is, however, no significant relationship between the maximum catch difference and the mean wind speed or temperature over the 12-hour period.RSUM [Traduit par la rdaction] Les analyses des donnes comparatives long terme (1991-2010) permettent de quantifier la cohrence des observations de prcipitations hivernales faites au moyen de six nivomtres Tretyakov identiques la station de recherche de Valdai, en Russie.En ce qui concerne le nivomtre Tretyakov standard, les rapports de capture moyens vont de 97 106% pour la neige sche, de 94 104% pour la neige mouille, de 87 109% pour la chasse-neige leve, de 96 103% pour les prcipitations mixtes et de 98 101% pour la pluie hivernale.Les diffrences entre les captures les plus fortes et les plus faibles sont d'environ 10 11% pour la neige, 7% pour les prcipitations mixtes et 3% pour la pluie.En moyenne, cette diffrence est d'environ 0,2 mm pour la priode d'observation de 12 heures.La diffrence de capture pour la chasse-neige leve est beaucoup plus grande, jusqu' 22%, soit une moyenne de 0,6 mm par observation.Les comparaisons des observations sur 12 heures affichent une meilleure cohrence dans les mesures des nivomtres pour les faibles chutes de neige et une grande variation dans les captures pour les fortes chutes de neige.Les diffrences dans les captures de neige sur 12 heures sont dans l'ensemble infrieures 2 mm parmi les six nivomtres.Les diffrences dans les observations sur 12 heures sont infrieures 1% pour la pluie et 4% pour les prcipitations mixtes.Il existe des relations linaires troites entre les observations nivomtriques sur 12 heures pour tous les types de prcipitations.Les diffrences maximales entre les captures de neige par les nivomtres augmentent trs faiblement avec la vitesse du vent et des diffrences plus grandes s'observent quand la temprature est plus leve, de -5 C 0 C.Il n'y a cependant aucun lien significatif entre la diffrence de capture maximale et la vitesse de vent ou la temprature moyennes durant la priode de 12 heures.
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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 | 0.000 | 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) | 0.014 | 0.001 |
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; both teacher heads agree on what is shown here.
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".