Using global reanalysis data to quantify and correct airflow distortion bias in shipborne wind speed measurements

Abstract

Abstract. At sea, wind forcing is responsible for the formation and development of surface waves and represents an important source of near surface turbulence. Therefore, processes related to near surface turbulence and wave breaking, such as sea spray emission and air-sea gas exchange are often parametrised with wind speed. Shipborne wind speed measurements thus provide highly relevant observations. They can, however, be compromised by flow distortion due to the ship's structure and objects nearby the anemometer that modify the airflow, leading to a deflection of the apparent wind direction and positive or negative acceleration of the apparent wind speed. The resulting errors in the estimated true wind speed can be greatly magnified at low wind speeds. For some research ships, correction factors have been derived from computational fluid dynamic models or through direct comparison with wind speed measurements from buoys. These correction factors can, however, loose their validity due to changes of the structures nearby the anemometer and thus require frequent re-evaluation, which is costly in either computational power or ship time. Here we evaluate if global weather forecast model data can be used to quantify the flow distortion bias in shipborne wind speed measurements. The method is tested on data from the Antarctic Circumnavigation Expedition (ACE) on board the R/V Akademik Tryoshnikov, which are compared with ERA-5 reanalysis wind speeds. We find that, depending on the relative wind direction, the relative wind speed and direction measurements are biased by −37 % to +20 % and −13° to +15°, respectively. The resulting error in the true wind speed is +11 % on average but ranges from −5 % to +40 % (5th and 95th percentile). After applying the bias correction, the uncertainty in the true wind speed is reduced to 5 % and depends mainly on the average accuracy of the ERA-5 data over the period of the experiment. The obvious drawback of this approach is the potential intrusion of model bias in the correction factors. We show that this problem can be somewhat mediated when the error propagation in the true wind correction is accounted for and used to weight the observations. We discuss the potential caveats and limitations of this approach and conclude that it can be used to quantify flow distortion bias for ships that operate on a global scale. The method can also be valuable to verify Computational Fluid Dynamic studies of airflow distortion on research vessels.

Wind, waves, and surface currents in the Southern Ocean: Observations from the Antarctic Circumnavigation Expedition

“…7 shows scatter diagrams of matching averages at collocated clusters (panels a and b) and geographical distributions of biases (difference between WaMoS-II and satellite observations, panels c and d). Overall, in-situ measurements of wind speeds during ACE are consistent with concurrent satellite observations, with data laying along the 1:1 correlation line (wind observations are also in agreement with reanalysis data from the ERA-5 archive; Landwehr et al, 2020). Nevertheless, there is a notable RM SE ≈ 3.2 m/s, with R ≈ 0.70 and SI ≈ 0.360.…”

Wind, waves, and surface currents in the Southern Ocean: observations from the Antarctic Circumnavigation Expedition

“…3b). Further, water depth from the echo sounder, ship's positions, speed, and course from a Global Positioning System (GPS) receiver and true wind velocity and direction from two two-dimensional sonic anemometers operating as part of an automated weather station (AWS) and mounted at 31.5 m above mean sea level (see Schmale et al, 2019;Landwehr et al, 2020a;Thurnherr et al, 2020) are fed into the system. Wind measurements were acquired at a rate of 1 Hz, averaged over 175 s and converted from the measurement height to a neutral 10 m wind speed (U 10 ) by assuming a logarithmic profile (see Holthuijsen, 2007) before being passed on to the WaMoS-II.…”

Sources, Occurrence and Characteristics of Fluorescent Biological Aerosol Particles Measured Over the Pristine Southern Ocean

“…(2019) available at https://doi.org/10.5281/zenodo.3559982; (3) Landwehr et al. (2020) available at https://doi.org/10.5281/zenodo.3836439; (4) Landwehr, Thurnherr et al. (2020) available at https://doi.org/10.5194/amt-13-3487-2020; (5) Schmale et al.…”