Notice bibliographique
Résumé
The recent upgrade of the National Weather Service WSR-88D radar network to polarimetric capabilities provides the abundance of information about the precipitation microphysics. Even with the plethora of polarimetric measurements at our disposal, use of this essential information regarding the microphysical processes is underutilized. For example, there is no polarimetric relation for snow estimation. The focus of this study is to improve the existing state of polarimetric data usage for discrimination between the ice pellets and freezing rain if their occurrence is away from the radar (patchy, no radar-centric structure), and more importantly the development of the polarimetric relations for snow quantification. \nObservations and analysis of an ice-liquid phase precipitation event, collected with an S-band polarimetric KOUN radar and a two-dimensional video disdrometer in central Oklahoma are presented. Using the disdrometer measurements, precipitation is classified either as ice pellets or rain/freezing rain. The ice pellets were challenging to detect by looking at conventional polarimetric radar data due to the localized and patchy nature of the ice phase and occurrence close to the ground. In this study, a new, unconventional way of looking at polarimetric radar data is introduced: Slanted Vertical Profiles SVPs at low (0° - 1°) radar elevations. From the analysis of the localized and patchy structures using SVPs, the polarimetric refreezing signature, reflected in local enhancement in ZDR and reduction in ZH and ρhv, became much more evident. Model simulations of sequential drop freezing using Marshal-Palmer DSDs along with the disdrometer observations suggest that preferential freezing of small drops may be responsible for the refreezing polarimetric signature. \nAccurate measurements of snow amounts by radar are very difficult to achieve. The inherent uncertainty in radar snow estimates based on the radar reflectivity factor Z is caused by the variability of snow particle size distributions and snow particle density as well as large diversity of snow growth habits. In this study, a novel methodology for snow quantification based on the joint use of radar reflectivity Z and specific differential phase KDP is introduced. An extensive dataset of 2D video disdrometer measurements of aggregated snow in central Oklahoma is used to derive polarimetric relations for liquid-equivalent snowfall rate S and ice water content IWC in the forms of bivariate power-law relations. The physical basis of these relations is explained. Their multipliers are sensitive to variations in the width of the canting angle distribution, and to lesser extent particles’ aspect ratios and densities, whereas the exponents are practically invariant. This novel approach is tested against the S(Z) relation using snow disdrometer measurements in three geographical regions (Oklahoma, Colorado, and Canada). Similarly, the new approach is tested on polarimetric radar data at three localities, Oklahoma, Virginia, and Colorado. Polarimetric relations for snow quantification demonstrated significant improvement in snow estimates compared to the traditional Z-based methods.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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) | 0,002 | 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 tête enseignante, 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 ».