Snow study at Centre for Atmospheric Research Experiments : variability of snow fall velocity, density and shape
Notice bibliographique
Résumé
In this work, snow data, collected at the Centre for Atmospheric Research Experiments (CARE) site during the winter of 2005/06 as part of the Canadian CALIPSO/CloudSat Validation Project (C3VP) were analyzed with various goals in mind: 1) investigate the effects of surface temperature and system depth on the snow fall velocity and particle size distribution, 2) find the variables that control the relationships between snow fall velocity and size (control variables), 3) retrieve the coefficient and the exponent in the power-law mass-size relations used in snow reflectivity, 4) estimate vertical air motion and 5) describe the shape of snowflakes that can be used in polarimetric studies of snow. It also includes considerable calibration work on the Hydrometeor Velocity and Shape Detector (HVSD); as well as sensitivity testing for radar calibration and Mie-scattering effect on snow density. Snow events were classified into several categories according to the radar echo vertical extent (H), surface and echo top temperatures (T s, Tt), to find their effects on snow fall velocity and particle size distribution. Several case studies, including situations of strong turbulence, were also examined. Simple and multiple correlation analyses between control variables and parameters of the power-law size-velocity relationship were carried out for 13 snow cases having a high R2 between their mean snowflakes fall velocity and the v-D fitted curve, in order to find the control variables of power-law v-D relations. Those cases were all characterized by single layered precipitation with different echo depth, surface and echo top temperatures. Results show that the exponent "b" in v-D power-law relationship has little effect on the variability of snow fall velocity; all control variables (T s, Tt, H) correlate much better to the coefficient "a" than to the exponent "b", leading to a snow fall velocity that can be simulated with a varying coefficient "a" and a fixed exponent "b" (v=aD0.15) with good accuracy. Coefficient "a" and exponent "b" for a generic snow v-D relationship were also examined. The results indicate that coefficient "a" of generic snow represents the most frequent coefficient "a" during the events, while the exponent "b" does not show any representative. Retrieval of the coefficient "a" and exponent "b" in a power-law mass-size relation, which eventually affects the snow reflectivity, was conducted by minimizing the root mean square (RMS) of differences in reflectivity between Vertically pointing McGill X-band Radar (VertiX) and HVSD. Minima of reflectivity differences lay on a diagonal direction of a diagram of the coefficient "a" (x-axis) versus exponent "b" (y-axis). It is shown that as the system deepens, the slope gets less steep. In addition, coefficient and exponent for this mass-size relation change with time, and snow density derived from several combined snow events does not explain the average snow density of the period. A method to retrieve vertical air motion with good accuracy using VertiX and HVSD is suggested. Several snow shape parameters and relations between the area ratio (Ar) and size of snowflakes (Ar-D relation) are investigated with snow dimensions defined in various ways. These Ar-D relations will be used as a guideline in snow density models.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».