Wind assessment in a coastal environment using synthetic aperture radar satellite imagery and a numerical weather prediction model
Bibliographic record
Abstract
AbstractWind assessment in a coastal environment remains a complex issue for both synthetic aperture radar (SAR) satellite imagery and numerical weather prediction (NWP) models. This study compares the accuracy of each technique to improve the overall mapping precision of both methods. On the one hand, 14 RADARSAT-1 scenes of the St. Lawrence River (Canada) are converted into wind speeds using a hybrid model function that consists of the CMOD-IFR2 geophysical model function and a C-band polarization ratio. A priori information on wind directions is gathered from QuikSCAT scatterometer and in situ wind data. On the other hand, co-located wind maps are generated with the Environment Canada mesoscale compressible community (MC2) model. Comparisons between these two methods are then presented according to three approaches: a systematic SAR and MC2 comparison at 4 km grid-point spacing, a validation with observations (buoy and QuikSCAT scatterometer), and a local analysis of SAR and MC2 winds along a transect perpendicular to the coastline. The main features of the offshore wind fields are well resolved by both methods. The comparison study shows that SAR and MC2 winds have good agreement, with a root mean square difference for wind speeds of 2.07 m/s and a bias of 0.13 m/s.Les vents en milieu côtier demeurent complexes à évaluer autant pour l'imagerie satellite radar à synthèse d'ouverture (RSO) que pour les modèles numériques de prévision météorologique. Cette étude vise à comparer chaque technique afin d'améliorer la précision globale liée à la cartographie des vents de surface. D'une part, quatorze scènes RADARSAT-1 du fleuve St-Laurent (Canada) sont transformées en image de vent à l'aide d'une fonction de modèle hybride composée du modèle géophysique CMOD-IFR2 ainsi que d'un rapport de polarisation en bande C. L'information a priori sur la direction du vent provient du diffusiomètre QuikSCAT et d'observations in situ. D'autre part, des images de vent co-localisées sont générées par le modèle Mésoéchelle Compressible Communautaire (MC2) d'Environnement Canada. Les comparaisons entre ces deux méthodes sont présentées selon trois approches: une comparaison systématique des vents évalués par RSO et MC2 à 4 km de maille de grille, une validation avec les observations (bouée et diffusiomètre QuikSCAT) et une analyse locale des vents RSO et MC2 le long d'un transect perpendiculaire à la côte. Dans l'ensemble, les caractéristiques propres aux champs de vent au-dessus de l'eau en zone côtière sont bien résolues par chacune des deux méthodes. Notre étude de comparaison montre que les vents RSO et MC2 sont bien corrélés avec une erreur quadratique moyenne pour les vitesses de vent de 2,07 m/s et un biais de 0,13 m/s. [Traduit par la Rédaction]
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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".