EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Improved ocean wind forcing products

Rianne Giesen1, Ana Trindade2,3, Marcos Portabella2, and Ad Stoffelen1
Rianne Giesen et al.
  • 1Royal Netherlands Meteorological Institute, De Bilt, The Netherlands
  • 2Institut de Ciències del Mar, CSIC, Barcelona, Spain
  • 3Universitat Politecnica de Catalunya, Barcelona, Spain

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.

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.


Belmonte Rivas, M. and A. Stoffelen (2019): Characterizing ERA-Interim and ERA5 surface wind biases using ASCAT, Ocean Sci., 15, 831–852, doi: 10.5194/os-15-831-2019.

Trindade, A., M. Portabella, A. Stoffelen, W. Lin and A. Verhoef (2019), ERAstar: A High-Resolution Ocean Forcing Product, IEEE Trans. Geosci. Remote Sens., 1-11, doi: 10.1109/TGRS.2019.2946019.

How to cite: Giesen, R., Trindade, A., Portabella, M., and Stoffelen, A.: Improved ocean wind forcing products, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15559,, 2020


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