Calibration of scatterometer wind speed under hurricane conditions

Ribal, Agustinus; Tamizi, Ali; Young, Ian R.

doi:10.1175/jtech-d-21-0055.1

Cited by 8 publications

(10 citation statements)

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“…These datasets have been calibrated and quality controlled as described by Ribal and Young [2020a] and Ribal and Young [2020b]. At wind speeds above 30m/s scatterometers tend to saturate (Ribal, Tamizi, and Young 2021). However, as shown by Ribal and Young [2020a], Metop-A and Metop-B yield unbiased data for wind speeds up to a minimum of 25m/s.…”

Section: Motivating Applicationmentioning

confidence: 99%

Basin-wide spatial conditional extremes for severe ocean storms

et al. 2020

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Physical considerations and previous studies suggest that extremal dependence between ocean storm severity at two locations exhibits near asymptotic dependence at short inter-location distances, leading to asymptotic independence and perfect independence with increasing distance. We present a spatial conditional extremes (SCE) model for storm severity, characterising extremal spatial dependence of severe storms by distance and direction. The model is an extension of Shooter et al. 2019 (Environmetrics 30, e2562, 2019) and Wadsworth and Tawn (2019), incorporating piecewise linear representations for SCE model parameters with distance and direction; model variants including parametric representations of some SCE model parameters are also considered. The SCE residual process is assumed to follow the delta-Laplace form marginally, with distance-dependent parameter. Residual dependence of remote locations given conditioning location is characterised by a conditional Gaussian covariance dependent on the distances between remote locations, and distances of remote locations to the conditioning location. We apply the model using Bayesian inference to estimates extremal spatial dependence of storm peak significant wave height on a neighbourhood of 150 locations covering over 200,000 km2 in the North Sea.

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Section: Motivating Applicationmentioning

confidence: 99%

Basin-wide spatial conditional extremes for severe ocean storms

et al. 2020

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“…These satellites each carry the ASCAT (Advanced SCATterometer) C‐band radar which measures, assuming a neutral boundary layer Ribal et al. (2021), the wind speed and direction through the electromagnetic backscatter of the ocean surface roughened by the wind. The ASCAT measures both in day and night conditions and is not affected by cloud cover.…”

Section: Data Setsmentioning

confidence: 99%

“…As the ETC wind speed measurements exceed 20 m/s, the non‐linear calibration method, as proposed by Ribal et al. (2021), is utilized. Care has to be taken when analyzing the wind speed measurements over 20 m/s, as only a small number of observations are available at the extremes for the calibration procedure.…”

Section: April 2021 100‐year Storm Analysismentioning

confidence: 99%

“…Here, the scatterometer observations are calibrated against NDBC buoy observations in different regions of the oceans, and then cross-validated between remote sensing measurements (Ribal et al, 2021). As the ETC wind speed measurements exceed 20 m/s, the non-linear calibration method, as proposed by Ribal et al (2021), is utilized. Care has to be taken when analyzing the wind speed measurements over 20 m/s, as only a small number of observations are available at the extremes for the calibration procedure.…”

Section: Era5 Reanalysismentioning

confidence: 99%

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Evaluation of Spectral Wave Models Physics as Applied to a 100‐Year Southern Hemisphere Extra‐Tropical Cyclone Sea State

Meucci,

Young,

Pepler

et al. 2023

JGR Oceans

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Extreme significant wave height estimates, and their probability of exceedance, are fundamental offshore and coastal engineering design parameters. These estimates are characterised by uncertainty due to an incomplete understanding of the atmosphere‐ocean energy and momentum exchanges during intense storms. This particularly affects extreme wave statistics of ocean regions exposed to large and frequent synoptic disturbances such as Extra‐Tropical Cyclones (ETCs). In this work, we assessed the performance of global phase‐averaged spectral wave models in representing the 1 in 100‐year sea state generated by a Southern Ocean ETC in April 2021. We collected in‐situ and remote sensing observations, from the storm generation region to its decaying phase and the impact on South‐East Australian coastlines. We compared the observations with a suite of reanalysis and hindcast global wave model datasets. While comparing well for wind speed up to 20 m/s, the models presented differences in solving the air‐sea momentum exchange between the atmosphere and the ocean for wind speed velocities between 20 and 35 m/s, which are a distinctive characteristic of ETCs. Despite marked differences in the storm generation region, the models converged to a similar representation of the swell systems impacting the South‐East Australian coastlines, as demonstrated by a comparison with deep‐water buoy observations close to the coastlines. Furthermore, we found that the energy of the ERA5 reanalysis, which assimilates satellite wave height measurements is quickly dispersed and, as such, of little advantage in representing the 9.9 m significant wave heights that impacted the South‐East Australian coastlines on April 10th 2021.

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“…For example, atmospheric motion vectors derived from infrared images of geostationary satellites, while spatio‐temporally dense, tend to cover the upper atmospheric layers when nearby deep convective clouds are present (Hidehiko et al ., 2015; Kim and Kim, 2018). On the other hand, ocean surface winds derived from scatterometers on board polar‐orbiting satellites are constrained by the number of available overpasses as well as their relatively low horizontal resolution (Figa‐Saldana et al ., 2002; Wang et al ., 2007a; Ribal et al ., 2021). For the South China Sea basin, the lack of in situ TC observations, particularly near‐surface wind distributions, has remained a constant challenge to operational forecasting.…”

Section: Introductionmentioning

confidence: 99%