Preprints
https://doi.org/10.5194/acp-2021-1050
https://doi.org/10.5194/acp-2021-1050
 
12 Jan 2022
12 Jan 2022
Status: this preprint is currently under review for the journal ACP.

The representation of winds in the lower troposphere in ECMWF forecasts and reanalyses during the EUREC4A field campaign

Alessandro Carlo Maria Savazzi1, Louise Nuijens1, Irina Sandu2, Geet George3, and Peter Bechtold1 Alessandro Carlo Maria Savazzi et al.
  • 1Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, The Netherlands
  • 2European Centre for Medium Range Weather Forecasts (ECMWF), Reading, UK
  • 3Max-Planck-Institut für Meteorologie, Hamburg, Germany

Abstract. The characterization of systematic forecast errors in lower-tropospheric winds over the ocean is a primary need for reforming models. Winds are among the drivers of convection, thus an accurate representation of winds is essential for better convective parameterizations. We focus on the temporal variability and vertical distribution of lower-tropospheric wind biases in operational medium-range weather forecasts and ERA5 reanalyses produced with the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). Thanks to several sensitivity experiments and an unprecedented wealth of measurements from the 2020 EUREC4A field campaign, we show that the wind bias varies greatly from day to day, resulting in RSME's up to 2.5 m s−1, with a mean wind speed bias up to −1 m s−1 near and above the trade-inversion in the forecasts and up to −0.5 m s−1 in reanalyses. The modeled zonal and meridional wind exhibit a too strong diurnal cycle, leading to a weak wind speed bias everywhere up to 5 km during daytime, turning into a too strong wind speed bias below 2 km at nighttime. The biases are fairly insensitive to the assimilation of sondes and likely related to remote convection and large scale pressure gradients. Convective momentum transport acts to distribute biases throughout the lowest 1.5 km, whereas at higher levels, other unresolved or dynamical tendencies play a role in setting the bias. Below 1 km, modelled friction due to unresolved physical processes appears too strong, but is (partially) compensated by dynamical tendencies, making this a challenging coupled problem.

Alessandro Carlo Maria Savazzi et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-1050', Anonymous Referee #1, 24 Feb 2022
  • RC2: 'Comment on acp-2021-1050', Anonymous Referee #2, 21 May 2022

Alessandro Carlo Maria Savazzi et al.

Alessandro Carlo Maria Savazzi et al.

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Short summary
Winds are of great importance for the transport of energy and moisture in the atmosphere. In this study we use measurements from the EUREC4A field campaign and several model experiments to understand the wind bias in the forecasts produced by the European Centre for Medium-Range Weather Forecasts. We are able to link the model errors to heights above 2 km and to the representation of the diurnal cycle of winds: the model makes the winds too slow in the morning and too strong in the evening.
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