Using global reanalysis data to quantify and correct airflow distortion bias in shipborne wind speed measurements
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 parameterise...
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ftinfoscience:oai:infoscience.epfl.ch:278321 2023-05-15T13:45:36+02:00 Using global reanalysis data to quantify and correct airflow distortion bias in shipborne wind speed measurements Landwehr, Sebastian Thurnherr, Iris Cassar, Nicolas Gysel-Beer, Martin Schmale, Julia 2020-06-30T11:28:23Z https://doi.org/10.5194/amt-13-3487-2020 https://infoscience.epfl.ch/record/278321/files/amt-13-3487-2020.pdf http://infoscience.epfl.ch/record/278321 unknown doi:10.5194/amt-13-3487-2020 https://infoscience.epfl.ch/record/278321/files/amt-13-3487-2020.pdf http://infoscience.epfl.ch/record/278321 http://infoscience.epfl.ch/record/278321 Text 2020 ftinfoscience https://doi.org/10.5194/amt-13-3487-2020 2023-02-13T23:00:34Z 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 parameterised with wind speed. Thus, shipborne wind speed measurements provide highly relevant observations. They can, however, be compromised by flow distortion due to the ship's structure and objects near 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, lose their validity due to changes in the structures near the anemometer and, thus, require frequent re-evaluation, which is costly in either computational power or ship time. Here, we evaluate if global atmospheric reanalysis 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 onboard the R/V Akademik Tryoshnikov, which are compared to 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 +22 % and -17∘ to +11∘ respectively. The resulting error in the true wind speed is +11.5 % on average but ranges from −4 % to +41 % (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 biases in the correction factors. We show ... Text Antarc* Antarctic EPFL Infoscience (Ecole Polytechnique Fédérale Lausanne) Antarctic The Antarctic Atmospheric Measurement Techniques 13 6 3487 3506 |
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Open Polar |
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EPFL Infoscience (Ecole Polytechnique Fédérale Lausanne) |
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ftinfoscience |
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unknown |
description |
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 parameterised with wind speed. Thus, shipborne wind speed measurements provide highly relevant observations. They can, however, be compromised by flow distortion due to the ship's structure and objects near 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, lose their validity due to changes in the structures near the anemometer and, thus, require frequent re-evaluation, which is costly in either computational power or ship time. Here, we evaluate if global atmospheric reanalysis 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 onboard the R/V Akademik Tryoshnikov, which are compared to 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 +22 % and -17∘ to +11∘ respectively. The resulting error in the true wind speed is +11.5 % on average but ranges from −4 % to +41 % (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 biases in the correction factors. We show ... |
format |
Text |
author |
Landwehr, Sebastian Thurnherr, Iris Cassar, Nicolas Gysel-Beer, Martin Schmale, Julia |
spellingShingle |
Landwehr, Sebastian Thurnherr, Iris Cassar, Nicolas Gysel-Beer, Martin Schmale, Julia Using global reanalysis data to quantify and correct airflow distortion bias in shipborne wind speed measurements |
author_facet |
Landwehr, Sebastian Thurnherr, Iris Cassar, Nicolas Gysel-Beer, Martin Schmale, Julia |
author_sort |
Landwehr, Sebastian |
title |
Using global reanalysis data to quantify and correct airflow distortion bias in shipborne wind speed measurements |
title_short |
Using global reanalysis data to quantify and correct airflow distortion bias in shipborne wind speed measurements |
title_full |
Using global reanalysis data to quantify and correct airflow distortion bias in shipborne wind speed measurements |
title_fullStr |
Using global reanalysis data to quantify and correct airflow distortion bias in shipborne wind speed measurements |
title_full_unstemmed |
Using global reanalysis data to quantify and correct airflow distortion bias in shipborne wind speed measurements |
title_sort |
using global reanalysis data to quantify and correct airflow distortion bias in shipborne wind speed measurements |
publishDate |
2020 |
url |
https://doi.org/10.5194/amt-13-3487-2020 https://infoscience.epfl.ch/record/278321/files/amt-13-3487-2020.pdf http://infoscience.epfl.ch/record/278321 |
geographic |
Antarctic The Antarctic |
geographic_facet |
Antarctic The Antarctic |
genre |
Antarc* Antarctic |
genre_facet |
Antarc* Antarctic |
op_source |
http://infoscience.epfl.ch/record/278321 |
op_relation |
doi:10.5194/amt-13-3487-2020 https://infoscience.epfl.ch/record/278321/files/amt-13-3487-2020.pdf http://infoscience.epfl.ch/record/278321 |
op_doi |
https://doi.org/10.5194/amt-13-3487-2020 |
container_title |
Atmospheric Measurement Techniques |
container_volume |
13 |
container_issue |
6 |
container_start_page |
3487 |
op_container_end_page |
3506 |
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1766228459774803968 |