Tropical Cyclone Wind Speed Estimation From Satellite Altimeter‐Derived Ocean Parameters

Sharoni, Syarawi M. H.; Reba, Mohd Nadzri Md; Hossain, Mohammad Shawkat

doi:10.1029/2020jc016988

Cited by 8 publications

(6 citation statements)

References 55 publications

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“…Wind structure can be measured by satellites, for example, synthetic aperture radar (SAR) (Mouche et al., 2017, 2019; Yu et al., 2019) and imager (Cordoba et al., 2017; Tsujino et al., 2021; Tsukada & Horinouchi, 2020; Zheng et al., 2019), and estimated by satellite altimeter‐derived ocean parameters (Sharoni et al., 2021). However, all remote sensing observations have certain extent of bias due to, for example, the observational error of TC rain (Sharoni et al., 2021) or the limited number of satellites that are suitable for TC observations (Mouche et al., 2017; Yu et al., 2019). Ground‐based Doppler radar (Sanger et al., 2014; Shimada et al., 2018; Wadler et al., 2018), airborne dropsondes (Ahern et al., 2019; Rogers, 2021), multilevel towers (Luo et al., 2020; Tang et al., 2018), and moored buoys (Tamizi & Young, 2020; Zhang et al., 2016) can also provide TC wind structure observations.…”

Section: Introductionmentioning

confidence: 99%

Sea Surface Wind Structure in the Outer Region of Tropical Cyclones Observed by Wave Gliders

et al. 2023

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Understanding the sea surface wind structure during tropical cyclones (TCs) is the key for study of ocean response and parameterization of air‐sea surface in numerical simulation. However, field observations are scarce. In 2019, three wave gliders were deployed in the South China Sea and the adjacent Western Pacific region, which acquired sea surface wind structure of eight TCs. Analysis of the field data suggests that the wave glider‐observed surface winds are consistent with most analysis/reanalysis data (i.e., ERA5, Cross‐Calibrated Multi‐Platform, and National Centers for Environmental Prediction‐Global Data Assimilation System) and Soil Moisture Active Passive. Both wave glider observations and analysis/reanalysis data indicate that TC wind fields induce an obvious increase in speed toward the sea surface together with a sharp change in direction, showing an asymmetric wind structure which is sensitive to TC translation speed and intensity. Larger mean values of wind speed and inflow angle are located on the right side along TC tracks. The inflow angle shows a highly dynamic dependence on the radial distance from the TC center, the TC intensity, as well as the TC‐relative azimuth. Comparisons between field observations and theoretical models indicate that the most widely used, ideal TC wind profile models can largely represent the observed sea surface wind structure, but generally underestimate the wind speed due to lack of consideration of background wind. Moreover, simple ideal models (e.g., the modified Rankine vortex model) may outperform complex models when accurate information of TCs is limited. Wave glider observations have potential for better understanding of air‐sea exchanges and for improvements of the corresponding parameterization schemes.

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Section: Introductionmentioning

confidence: 99%

Sea Surface Wind Structure in the Outer Region of Tropical Cyclones Observed by Wave Gliders

et al. 2023

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“…In addition to this, Multi-source data joint [23] is introduced to consider more influencing factors of the high wind speed inversion. Besides of σ 0 and SWH measured by the satellite altimeter [24], the brightness temperature(T b ), water vapor(W V ) and liquid water content (W L ) measured by the microwave radiometer [25], [26] are incorporated into GMF in order to consider the rainfall effect and some other high wind conditions. In [25] the multi-parameter linear regress model with σ 0 Ku , σ 0 C , SWH Ku , SWH C , T b , W V , and W L can provide the accuracy with less than 2.0m/s, and artificial neural network model [25], [26] can provide the accuracy with less than 1.5m/s.…”

Section: Introductionmentioning

confidence: 99%

“…Besides of σ 0 and SWH measured by the satellite altimeter [24], the brightness temperature(T b ), water vapor(W V ) and liquid water content (W L ) measured by the microwave radiometer [25], [26] are incorporated into GMF in order to consider the rainfall effect and some other high wind conditions. In [25] the multi-parameter linear regress model with σ 0 Ku , σ 0 C , SWH Ku , SWH C , T b , W V , and W L can provide the accuracy with less than 2.0m/s, and artificial neural network model [25], [26] can provide the accuracy with less than 1.5m/s. Reference [25] reaffirms that the satellite altimeter enables estimates of U 10 up to 35 m/s in tropical cyclone with accuracy around 1 m/s.…”

Section: Introductionmentioning

confidence: 99%

“…In [25] the multi-parameter linear regress model with σ 0 Ku , σ 0 C , SWH Ku , SWH C , T b , W V , and W L can provide the accuracy with less than 2.0m/s, and artificial neural network model [25], [26] can provide the accuracy with less than 1.5m/s. Reference [25] reaffirms that the satellite altimeter enables estimates of U 10 up to 35 m/s in tropical cyclone with accuracy around 1 m/s. But the microwave radiometer often has one problem of channel saturation [27]- [29].…”

Section: Introductionmentioning

confidence: 99%

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A New Cyclic Iterative Correction Algorithm for Retrieving Sea Surface Wind Speed Based on a Multilayer-Medium Model

Tian

Shi

2022

IEEE Access

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At high sea conditions, e.g., typhoons and hurricanes, the sea surface waves break and produce foams, spray droplets, and bubbles, which are collectively known as whitecaps. Whitecaps covering the rough sea surface greatly absorb the electromagnetic pulses emitted by satellite remote sensors, such as altimeters, scatter-meters, and the Surface Waves Investigation and Monitoring (SWIM). Thus, the whitecaps reduce the sea surface backscattering, which results in the sea surface wind speed being overestimated, an error that is particularly prevalent at high wind speeds. Many algorithms of retrieving wind speeds can retrieve effectively sea surface wind speed when the wind speed is less than 15m/s, but the bias of retrieving wind speed is become bigger and bigger when wind speed is more than 20m/s. In order to eliminate the effect of whitecaps on retrieving the sea surface wind speed at high wind speeds(>15m/s), we propose a Cyclic Iterative Correction Algorithm(CICA). Firstly, a four-layer medium with including whitecaps is established. Secondly, the effect of whitecaps is calculated. Finally, the retrieving and correcting wind speed is continuously repeated. The ultimate goal is to remove the whitecaps' influence on the backscattering coefficient and re-correct the overestimated wind speed until it reaches a constant value. The wind speeds measured by SWIM were corrected by using CICA, and then were compared with buoy measurements. Those comparisons show that using CICA can improve the wind speed inversion accuracy to 1.5 m/s, an improvement that is particularly evident in high sea conditions.

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“…Typically, a scatterometer sensor is adopted on satellites to measure both sea wind speed and direction. Examples of space-based scatterometers are Seasat-A [23], Quick Scatterometer (QuikSCAT) [8,16,[18][19][20][21], OceanSat-2 [24], and Meteorological Operational satellite-A/B/C (MetOp-A/B/C) [2,[25][26][27]. Altimeters can also be used to measure wind speed at the ocean surface, such as Geodetic Satellite (GEOSAT) [28], European Remote-Sensing Satellite-1/2 (ERS-1/2) [29], Topography Experiment/Poseidon (TOPEX/Poseidon) [17,22], Jason-1/2/3 [2,[30][31][32], and Environmental Satellite (EnviSat) [25,33].…”

Section: Introductionmentioning

confidence: 99%

Improved Calibration of Wind Estimates from Advanced Scatterometer MetOp-B in Korean Seas Using Deep Neural Network

et al. 2021

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Satellite-based observations of sea wind are useful for forecasting marine weather and performing marine disaster management. Meteorological Operational Satellite-B (MetOp-B) is one of the satellites that provide wind products through a scatterometer named the Advanced Scatterometer (ASCAT). Since the linear regression method has been conventionally employed for calibrating remotely-sensed wind data, deviations and biases remain un-resolved to some degree. For coastal applications, these remotely-sensed wind data need to be calibrated again using local in-situ measurements in order to provide more accurate and realistic information. Thus, this study proposed a new method to calibrate ASCAT-based wind speed estimates using artificial neural networks. Herein, a deep neural network (DNN) model was applied. Wind databases collected during a period from 2012 to 2019 by the MetOp-B ASCAT and ten buoy stations in Korean seas were considered for deep learning-based calibration. ASCAT-based wind data and in-situ measurements were collocated in space and time. They were then separated into training and validation sets. A DNN model was designed and trained using multiple input variables such as observation location, sensing date and time, wind speed, and wind direction of the training set. The DNN-based best fit calibration model was evaluated using the validation set. The mean of biases between ASCAT-based and in-situ wind speeds was found to be decreased from 0.41 to 0.05 m/s on average for all buoy locations. The root mean squared error (RMSE) of wind speed was reduced from 1.38 m/s to 0.93 m/s. Moreover, the DNN-based calibration considerably improved the quality of wind speeds of less than 4 m/s, and of high wind speeds of 11–25 m/s. These results suggest that ASCAT-based observations can accurately represent real wind fields if a DNN-based calibration approach is applied.

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Tropical Cyclone Wind Speed Estimation From Satellite Altimeter‐Derived Ocean Parameters

Cited by 8 publications

References 55 publications

Sea Surface Wind Structure in the Outer Region of Tropical Cyclones Observed by Wave Gliders

Sea Surface Wind Structure in the Outer Region of Tropical Cyclones Observed by Wave Gliders

A New Cyclic Iterative Correction Algorithm for Retrieving Sea Surface Wind Speed Based on a Multilayer-Medium Model

Improved Calibration of Wind Estimates from Advanced Scatterometer MetOp-B in Korean Seas Using Deep Neural Network

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