Wind assessment in a coastal environment using synthetic aperture radar satellite imagery and a numerical weather prediction model

Abstract

Wind 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.