FN Archimer Export Format
PT J
TI Developing a new wind dataset by blending satellite data and WRF model wind predictions
BT 
AF Salvação, Nadia
   Bentamy, Abderrahim
   Guedes Soares, C.
AS 1:1;2:2;3:1;
FF 1:;2:PDG-ODE-LOPS-SIAM;3:;
C1 Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001, Lisboa, Portugal
   Laboratoire Spatial et Interfaces Air-Mer (IFREMER), Centre Bretagne - ZI de la Pointe du Diable, CS 10070-29280, Plouzané, France
C2 UNIV LISBOA, PORTUGAL
   IFREMER, FRANCE
SI BREST
SE PDG-ODE-LOPS-SIAM
UM LOPS
IN WOS Ifremer UMR
   copubli-europe
IF 8.7
TC 5
UR https://archimer.ifremer.fr/doc/00786/89847/95312.pdf
   https://archimer.ifremer.fr/doc/00786/89847/95707.pdf
LA English
DT Article
DE ;WRF;Satellite wind;Blended data;Wind energy
AB This paper presents an approach to improve wind datasets developed using the regional atmospheric model Weather Research Forecasting by combining its predictions with remotely sensed wind observations in enhanced wind speed analyses that leads to blended winds. In this study, satellite data derived from scatterometers, radiometers, and synthetic aperture radar are used. The spatial and temporal features of each wind product are thoroughly analysed. For the probabilistic evaluation of their skill, comprehensive comparisons with available buoy data are carried out. The statistical analysis shows that the combined use of satellite and numerical weather prediction model data improves the agreement with buoy measurements, demonstrating the added value of using the blended product. As an application of the method, new improved satellite wind speeds are presented in the form of a wind energy assessment along the Iberian coastal area. From inspection of the provided wind power maps, northern and central regions emerge as the most promising areas for wind harnessing offshore despite some seasonal variations. Finally, potential wind farm sites are provided, along with insights into multi-year wind speed distribution. The results show how the new dataset can be used for the selection of promising areas for wind 
PY 2022
PD OCT
SO Renewable Energy
SN 0960-1481
PU Elsevier BV
VL 198
UT 000852299600002
BP 283
EP 295
DI 10.1016/j.renene.2022.07.049
ID 89847
ER

EF