The representation of the trade winds in ECMWF forecasts and reanalyses during EUREC4A

• Published in: Atmospheric Chemistry and Physics
• Main Authors: Savazzi, A.C.M. (author), Nuijens, Louise (author), Sandu, Irina (author), Georg, Geet (author)
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2022
• Subjects: North Atlantic
• Online Access: http://resolver.tudelft.nl/uuid:88d41b4f-a9fa-424c-a296-ccfac83f2b3b; https://doi.org/10.5194/acp-22-13049-2022

The characterization of systematic forecast errors in lower-tropospheric winds is an essential component of model improvement. This paper is motivated by a global, long-standing surface bias in the operational medium-range weather forecasts produced with the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). Over the tropical oceans, excessive easterly flow is found. A similar bias is found in the western North Atlantic trades, where the EUREC4A field campaign provides an unprecedented wealth of measurements. We analyze the wind bias in the IFS and ERA5 reanalysis throughout the entire lower troposphere during EUREC4A. The wind bias varies greatly from day to day, resulting in root mean square errors (RMSEs) 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. These biases are insensitive to the assimilation of sondes. The modeled zonal and meridional winds exhibit a diurnal cycle that is too strong, leading to a weak wind speed bias everywhere up to 5 km during daytime but a wind speed bias below 2 km at nighttime that is too strong. Removing momentum transport by shallow convection reduces the wind bias near the surface but leads to stronger easterly near cloud base. The update in moist physics in the newest IFS cycle (cycle 47r3) reduces the meridional wind bias, especially during daytime. Below 1 km, modeled friction due to unresolved physical processes appears to be too strong but is (partially) compensated for by the dynamics, making this a challenging coupled problem. Atmospheric Remote Sensing