Examination of Surface Wind Asymmetry in Tropical Cyclones over the Northwest Pacific Ocean Using SMAP Observations

Sun, Zhian; Zhang, Biao; Zhang, Jun A.; Perrie, William

doi:10.3390/rs11222604

Cited by 19 publications

(13 citation statements)

References 28 publications

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“…TC intensity and storm structure are not unrelated}strong storms are more symmetric than weaker ones Jiang 2016, 2017;Sun et al 2019). While those aforementioned variables are originally derived for TC intensity prediction (Lee et al 2015(Lee et al , 2016Yang et al 2020), they are suitable for storm structure prediction as well.…”

Section: A Tropical Cyclones and Their Environmentmentioning

confidence: 99%

Machine Learning–Based Hurricane Wind Reconstruction

Yang

Lee

Tippett

et al. 2022

Weather and Forecasting

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Here we present a machine learning based wind reconstruction model. The model reconstructs hurricane surface winds with XGBoost which is a decision tree based ensemble predictive algorithm. The model treats the symmetric and asymmetric wind fields separately. The symmetric wind field is approximated by a parametric wind profile model and two Bessel function series. The asymmetric field, accounting for asymmetries induced by the storm and its ambient environment, is represented using a small number of Laplacian eigenfunctions. The coefficients associated with Bessel functions and eigenfunctions are predicted by XGBoost-based on storm and environmental features taken from NHC best-track and ERA-Interim data, respectively. We use HWIND for the observed wind fields. Three parametric wind profile models are tested in the symmetric wind model. The wind reconstruction model’s performance is insensitive to the choice of the profile model because the Bessel function series correct biases of the parametric profiles. The mean square error of the reconstructed surface winds is smaller than the climatological variance, indicating skill-ful reconstruction. Storm center location, eyewall size, and translation speed play important roles in controlling the magnitude of the leading asymmetries, while the phase of the asymmetries is mainly affected by storm translation direction. Vertical wind shear impacts the asymmetry phase to a lesser degree. Intended applications of this model include assessing hurricane risk using synthetic storm event sets generated by statistical-dynamical downscaling hurricane models.

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Section: A Tropical Cyclones and Their Environmentmentioning

confidence: 99%

Machine Learning–Based Hurricane Wind Reconstruction

Yang

Lee

Tippett

et al. 2022

Weather and Forecasting

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“…They built up a new parametric model for surface wind speed of TC. Recently, Sun et al (2019) found that the surface wind asymmetry degree decreases with increasing TC intensity but increases with increasing TC translation speed. This relationship is found for tropical storms and super typhoons, while typhoons and severe typhoons do not.…”

Section: Introductionmentioning

confidence: 99%

An ERA5 global climatology of tropical cyclone size asymmetry

Zhang

Chan

2022

Intl Journal of Climatology

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The size of tropical cyclone (TC), defined as the radius of gale‐force (34‐kt) 10‐m winds (R34) from a TC centre, is often asymmetric but received limited attention. Understanding the asymmetry of TC size is particularly essential to enhance the applicability, predictability, and preparedness for the parties in the coastal regions. In this study, the ERA5 reanalysis data are first validated by the QuikSCAT satellite data. Results demonstrate that the radius of ERA5 32‐kt 10‐m winds estimates best R34 in the QuikSCAT satellite data with a promising linear relationship (r = .75). A 41‐year (1979–2019) homogeneous ERA5 global climatology of TC size asymmetry is hence established based on this linear regression model. Both the temporal and spatial characteristics of the TC size asymmetry are investigated and discussed. This study lays the important groundwork for future sophisticated research on TC size.

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“…Statistical comparisons between SMAP-derived wind speeds and SFMR measurements, from eight TCs during 2015, have shown that the standard deviation of wind speed was 3.11 m/s for wind speeds up to 70 m/s [19]. Moreover, as an application, SMAP wind data acquired in the Northwest Pacific Ocean have been used to examine TC surface wind asymmetry [22].…”

Section: Introductionmentioning

confidence: 99%

Estimating Tropical Cyclone Wind Structure and Intensity From Spaceborne Radiometer and Synthetic Aperture Radar

Zhang

Zhu

Perrie

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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We present a relatively simple method to estimate tropical cyclone (TC) surface wind structure (34-, 50-and 64-kt wind radii) and intensity (maximum wind speed, MWS) from wind fields acquired from the L-band SMAP radiometer and C-band Sentinel-1A/B and RADARSAT-2 synthetic aperture radar (SAR) between 2015 and 2020. The radiometer and SAR-derived wind radii and MWS are systematically compared with the best-track estimates. The root-mean-square errors (RMSEs) of R34, R50 and R64 are 31.2, 21.8 and 17.0 n mi (1 n mi =1.852 km) for radiometer, and 21.7, 16.5 and 16.3 n mi for SAR, respectively. These error values are smaller than the averaged best-track uncertainty estimates for the three wind radii. Compared to best-track reports, the bias and RMSE for the MWS estimates are-0.2 m/s and 5.8 m/s for radiometer, and 4.4 m/s and 9.1 m/s for SAR, respectively. These results are for the wind speeds in the range of 17-80 m/s. For the two typical TCs (Lionrock and Noru) in the Northwest Pacific Ocean, our results show that a combination of the radiometer and SAR wind data acquired within a very short time interval has the potential to simultaneously obtain reasonable measurements of the wind radii and intensity parameters. Moreover, for a TC with long lifecycle, such as Typhoon Noru, we demonstrate that high-resolution and multi-temporal synergistic observations from SAR and radiometer are valuable for studying fine-scale features of the wind field and characteristics of wind asymmetry associated with intensity change, as well as the evolution of TC surface wind structure and intensity.

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Examination of Surface Wind Asymmetry in Tropical Cyclones over the Northwest Pacific Ocean Using SMAP Observations

Cited by 19 publications

References 28 publications

Machine Learning–Based Hurricane Wind Reconstruction

Machine Learning–Based Hurricane Wind Reconstruction

An ERA5 global climatology of tropical cyclone size asymmetry

Estimating Tropical Cyclone Wind Structure and Intensity From Spaceborne Radiometer and Synthetic Aperture Radar

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