Abstract

<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>

Highlights

  • Ocean surface wind fields from satellites and numerical weather prediction (NWP) models both have strong properties

  • The large systematic biases are associated with slowly evolving ocean conditions

  • persistent biases exist between scatterometer observations

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Summary

Improved ocean wind forcing products

1. Royal Netherlands Meteorological Institute, De Bilt, Netherlands 2. Institut de Ciències del Mar, CSIC, Barcelona, Spain. Ocean surface wind fields from satellites (scatterometer) and numerical weather prediction (NWP) models both have strong properties. How to best combine scatterometer observations and NWP model fields into global ocean wind forcing products with high temporal and spatial resolution?. First explore the differences between scatterometer (MetOp-A ASCAT) and NWP model (ECMWF ERA5). Systematic large-scale biases in NWP model winds, in the tropics and the mid-latitudes

Meridional wind speed variability bias
Conclusions

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