Strong winds in a coupled wave–atmosphere model during a North Atlantic storm event: evaluation against observations

Abstract

Strong winds may be biased in atmospheric models. Here the European Centre for Medium-range Weather Forecasts (ECMWF) coupled wave-atmosphere model is used (i) to evaluate strong winds against observations, and (ii) to test how alternative wind stress parametrizations could lead to a more accurate model. For the period of storms Kaat and Lilli (23-27 January 2014), we compared simulated winds with in situ -moored buoys and platforms -and satellite observations available from the North Atlantic. Five wind stress parametrizations were evaluated. The first result is that moderate simulated winds (5-20 m s −1 ) match with all observations. However, for strong winds (above 20 m s −1 ), mean differences appear, as much as −7 m s −1 at 30 m s −1 . Significant differences also exist between observations, with buoys and Advanced Scatterometer ASCAT-KNMI generally showing lower wind speeds than the platforms and other remote-sensing data used in this study (AMSR2, ASCAT-RSS, WindSat, SMOS and JASON-2). Buoy and ASCAT-KNMI winds are likely to underestimate the real wind speed. It is difficult to conclude which dataset should be used as a reference. The second result is that common wave-age dependent parametrizations produce unrealistic drags and are not appropriate for coupling, whereas a newly empirically adjusted Charnock parametrization leads to higher winds compared to the default ECMWF parametrization. This proposed new parametrization may lead to more accurate results in an operational context.

Improving Parametric Cyclonic Wind Fields Using Recent Satellite Remote Sensing Data

“…Taking advantage of an extremely active 2017 hurricane season in the tropical Atlantic Ocean and the Eastern Pacific soon after the launch of the CYGNSS mission, we revisited some of the main parametric wind models used for storm surge hazard assessment or prediction of cyclonic waves, and investigated the potential of recent remote sensing data to estimate surface wind speeds under extreme conditions. Using an innovative approach, we first confirmed the findings of a number of previous studies: the inability of ASCAT data to reproduce strong winds for instance [51], or systematic bias in several parametric models, as stated by Willoughby et al [39], Lin and Chavas [17] or Chavas et al [47]. All these results are displayed in a "user-friendly" figure (Figure 2) that should be helpful for the readers to identify the model most suited for their case study.…”

Characterization of the air-sea exchanges mechanisms during a Mediterranean heavy precipitation event using realistic sea state modelling

“…with T p the peak period of the waves and g the acceleration of gravity. 20 Keeping the coefficients A and B constant with wind speed results in drag coefficient and wind stress too strong in strong wind conditions (wind speed above 20 m.s −1 ) as shown by Pineau-Guillou et al (2018). In order to tackle this, and to reproduce the saturation or the decrease of the drag coefficient observed in strong to cyclonic winds (e.g.…”

“…Using the former formulation of Oost et al (2002) in high-resolution numerical experiments of HPE, Thévenot et al (2016) and Bouin et al (2017) showed an impact on the location of precipitation when the sea state forcing is taken into account in the sea surface turbulent fluxes parametrization. Nevertheless, these formulas are known to produce too 20 strong fluxes when strong winds (>20 m.s −1 ) are encountered (Pineau-Guillou et al, 2018).…”

Coastal Sea Level and Related Fields from Existing Observing Systems

“…Among these contributors, wind-waves play an important role on sea level at the coast (for a full discussion see Dodet et al, this issue), either directly, or indirectly through their influence on the wind stress and storm surge (e.g. Mastenbroek et al 1993, Pineau-Guillou et al 2018, and their role on the morphodynamic evolution of the nearshore (Coco et al, 2014;Masselink et al, 2016). Over the ocean shelves and along the coasts, ocean mass variations, reflected in ocean bottom pressure changes, are one of the dominant components of sea-level variability.…”