High-resolution passive polarimetric microwave mapping of ocean surface wind vector fields

2001 ◽  
Vol 39 (3) ◽  
pp. 606-622 ◽  
Author(s):  
J.R. Piepmeier ◽  
A.J. Gasiewski
Keyword(s):  
High Resolution ◽  
Vector Fields ◽  
Surface Wind ◽  
Ocean Surface ◽  
Wind Vector ◽  
Ocean Surface Wind

scholarly journals Improved ocean wind forcing products

2020 ◽  
Author(s):  
Rianne Giesen ◽  
Ana Trindade ◽  
Marcos Portabella ◽  
Ad Stoffelen
Keyword(s):  
High Resolution ◽  
Weather Prediction ◽  
Surface Wind ◽  
Ocean Surface ◽  
Ocean Model ◽  
Wind Forcing ◽  
Small Scale ◽  
Wind Fields ◽  
Ocean Surface Wind ◽  
Ocean Wind

<p>The ocean surface wind plays an essential role in the exchange of heat, gases and momentum at the atmosphere-ocean interface. It is therefore crucial to accurately represent this wind forcing in physical ocean model simulations. Scatterometers provide high-resolution ocean surface wind observations, but have limited spatial and temporal coverage. On the other hand, numerical weather prediction (NWP) model wind fields have better coverage in time and space, but do not resolve the small-scale variability in the air-sea fluxes. In addition, Belmonte Rivas and Stoffelen (2019) documented substantial systematic error in global NWP fields on both small and large scales, using scatterometer observations as a reference.</p><p>Trindade et al. (2019) combined the strong points of scatterometer observations and atmospheric model wind fields into ERA*, a new ocean wind forcing product. ERA* uses temporally-averaged differences between geolocated scatterometer wind data and European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis fields to correct for persistent local NWP wind vector biases. Verified against independent observations, ERA* reduced the variance of differences by 20% with respect to the uncorrected NWP fields. As ERA* has a high potential for improving ocean model forcing in the CMEMS Model Forecasting Centre (MFC) products, it is a candidate for a future CMEMS Level 4 (L4) wind product. We present the ongoing work to further improve the ERA* product and invite potential users to discuss their L4 product requirements.</p><p>References:</p><p>Belmonte Rivas, M. and A. Stoffelen (2019): <em>Characterizing ERA-Interim and ERA5 surface wind biases using ASCAT</em>, Ocean Sci., 15, 831–852, doi: 10.5194/os-15-831-2019.</p><p>Trindade, A., M. Portabella, A. Stoffelen, W. Lin and A. Verhoef (2019), <em>ERAstar: A High-Resolution Ocean Forcing Product</em>, IEEE Trans. Geosci. Remote Sens., 1-11, doi: 10.1109/TGRS.2019.2946019.</p>

Interpretation of scatterometer ocean surface wind vector EOFs over the Northwestern Pacific

2003 ◽  
Vol 84 (1) ◽  
pp. 53-68 ◽  
Author(s):  
Jiayi Pan ◽  
Xiao-Hai Yan ◽  
Quanan Zheng ◽  
W.Timothy Liu ◽  
Victor V Klemas
Keyword(s):  
Surface Wind ◽  
Ocean Surface ◽  
Northwestern Pacific ◽  
Wind Vector ◽  
Ocean Surface Wind
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