Comparison of Wind Speeds from Spaceborne Microwave Radiometers with In Situ Observations and ECMWF Data over the Global Ocean

Zhang, Lei; Shi, Hanqing; Wang, Zhenzhan; Yu, Huapeng; Yin, Xiaobin; Liao, Qixiang

doi:10.3390/rs10030425

Cited by 17 publications

(23 citation statements)

References 40 publications

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“…The along-track variation of u M u −1 c has apparent similarities with the bias maps that Stopa and Cheung (2014), Zhang et al (2018), and Belmonte Rivas and Stoffelen (2019) provide from comparisons of ERA-Interim and ERA-5 versus satellite based wind speeds. The majority of the ACE data was collected between 60 • S and 40 • S; thus, it is likely that a potential regional bias for this latitude band affects the estimated correction coefficients.…”

Section: Discussionsupporting

confidence: 62%

“…Several studies (e.g. Stopa and Cheung, 2014;Zhang et al, 2018) suggest that during the Southern summer ERA-Interim wind speeds are biased high by ≈ 5% in the latitude band 40 • S to 60 • S. In order to investigate, if this affects the derived correction, the bin averaged ratios S m S −1 M for the port sensor are plotted in Fig. 4 (upper panel) for ship positions within and outside of the latitude band 40 • S to 60 • S separately.…”

Section: Average Deflection and Acceleration Estimates Based On Era-5mentioning

confidence: 99%

“…Remote sensing systems (altimeter, scatterometer, and microwave radiometer) offer global observations of surface wind speed related quantities from space. The observed signals are calibrated and validated against surface wind speed observations from buoy networks (Young et al, 2017;Stopa and Cheung, 2014;Jones et al, 2016;Schmidt et al, 2017;Zhang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have investigated the performance of numerical weather models via comparison to in situ observations from ships, buoys, and wave-gliders (e.g. Li et al, 2013;Stopa and Cheung, 2014;Jones et al, 2016;Schmidt et al, 2017;Zhang et al, 2018;Belmonte Rivas and Stoffelen, 2019). They document a significant improvement in the precision and accuracy of these models over the last decades, but also variable model bias that can depend on region and season.…”

Section: Introductionmentioning

confidence: 99%

“…The performance of ERA-5 and its predecessor ERA-Interim has been evaluated in several studies: Stopa and Cheung (2014) and (Zhang et al, 2018) used wind speeds from buoys to validate wind speed (and wave height) from remote sensing products and ERA-Interim reanalysis data. Both studies found that the bias in the ERA-Interim wind speeds varied between regions and for different seasons.…”

Section: Introductionmentioning

confidence: 99%

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

Landwehr¹,

Thurnherr²,

Cassar³

et al. 2019

Preprint

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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.

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Section: Discussionsupporting

confidence: 62%

Section: Average Deflection and Acceleration Estimates Based On Era-5mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Landwehr¹,

Thurnherr²,

Cassar³

et al. 2019

Preprint

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Observation and Monitoring of Tropical Cyclones from Space

ROUX

2023

Satellites for Atmospheric Sciences 2

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Trade Wind Boundary Layer Turbulence and Shallow Convection: New Insights Combining SAR Images, Satellite Brightness Temperature and Airborne In Situ Measurements

Brilouet

Bouniol

Couvreux

et al. 2022

Preprint

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The imprint of marine atmospheric boundary layer (MABL) dynamical structures on sea surface roughness, as seen from Sentinel-1 Synthetic Aperture Radar (SAR) acquisitions, is investigated. We focus on February 13th, 2020, a case study of the EUREC4A (Elucidating the role of clouds-circulation coupling in climate) field campaign. For suppressed conditions, convective rolls imprint on sea surface roughness is confirmed through the intercomparison with MABL turbulent organization deduced from airborne measurements. A discretization of the SAR wide swath into 25 x 25 km$ˆ2$ tiles then allows us to capture the spatial variability of the turbulence organization varying from rolls to cells. Secondly, we objectively detect cold pools within the SAR image and combine them with geostationary brightness temperature. The geometrical or physically-based metrics of cold pools are correlated to cloud properties. This provides a promising methodology to analyze the dynamics of convective systems as seen from below and above.

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Comparison of Wind Speeds from Spaceborne Microwave Radiometers with In Situ Observations and ECMWF Data over the Global Ocean

Abstract: In comparison with the ECMWF data, some obviously positive differences exist at high southern latitudes in January and at high northern latitudes in July.

Cited by 17 publications

References 40 publications

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

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

Observation and Monitoring of Tropical Cyclones from Space

Trade Wind Boundary Layer Turbulence and Shallow Convection: New Insights Combining SAR Images, Satellite Brightness Temperature and Airborne In Situ Measurements

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