Assessment of CYGNSS Wind Speed Retrievals in Tropical Cyclones

Ricciardulli, Lucrezia; Mears, C. A.; Manaster, Andrew; Meißner, Thomas

doi:10.3390/rs13245110

Cited by 13 publications

(6 citation statements)

References 49 publications

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“…In this section, we compare the reconstructed wind speeds with wind measurements from the SMAP L-band radiometer in tropical cyclones, which are produced by using algorithms developed for high wind conditions [59,60]. The accuracy of SMAP under tropical cyclones conditions is confirmed and compared with HWRF, with the standard deviation below 4 m/s for high wind between 10 and 60 m/s [61].…”

Section: Validation Of Wind Fields Between Smap and Reconstructionmentioning

confidence: 86%

Tropical Cyclone Wind Field Reconstruction and Validation Using Measurements from SFMR and SMAP Radiometer

Yang

Ren

et al. 2022

Remote Sensing

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Accurate information on tropical cyclone position, intensity, and structure is critical for storm surge prediction. Atmospheric reanalysis datasets can provide gridded, full coverage, long-term and multi-parameter atmospheric fields for the research on the impact of tropical cyclones on the upper ocean, which effectively makes up for the uneven temporal and spatial distribution of satellite remote sensing and in situ data. However, the reanalysis data cannot accurately describe characteristic parameters of tropical cyclones, especially in high wind conditions. In this paper, the performance of the tropical cyclone representation in ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis 5th Generation) is investigated and analyzed with respect to IBTrACS (International Best Track Archive for Climate Stewardship) during the period 2018–2020. Comparisons demonstrate that ERA5 winds significantly underestimate the maximum wind speed during the tropical cyclones (>30 m/s) compared to those provided by IBTrACS. An effective wind reconstruction method is examined to enhance tropical cyclone intensity representation in reanalysis data in 94 cases of 31 tropical cyclones 2018–2020. The reconstructed wind speeds are in good agreement with the SFMR (Stepped Frequency Microwave Radiometer) measured data and SMAP (Soil Moisture Active Passive) L-band radiometer remotely sensed measurements. The proposed wind reconstruction method can effectively improve the accuracy of the tropical cyclone representation in ERA5, and will benefit from the establishment of remote sensing satellite retrieval model and the forcing fields of the ocean model.

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Section: Validation Of Wind Fields Between Smap and Reconstructionmentioning

confidence: 86%

Tropical Cyclone Wind Field Reconstruction and Validation Using Measurements from SFMR and SMAP Radiometer

Yang

Ren

et al. 2022

Remote Sensing

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“…With a constellation of eight micro-satellites, CYGNSS provides relatively large spatial coverage and high temporal resolution. However, due to the low sensitivity of the reflected GNSS signal to high winds, the high winds from GNSS-R measurements are usually of large retrieval errors [7]. The low frequency radiometers, especially the L-band instruments onboard the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP) missions, are sensitive to high and extreme sea surface wind speeds, while little sensitive to rain.…”

Section: Introductionmentioning

confidence: 99%

“…However, due to signal saturation in the (co-polarized) radar backscatter measurements, both C-and Ku-band scatterometer extreme wind speeds show large errors. Moreover, the rain contamination on the Ku-band backscatter leads to an underestimation of the TC intensity estimates [7,19]. As such, it is difficult to characterize the TC intensity directly using the maximum wind speed from scatterometer-derived wind fields.…”

Section: Introductionmentioning

confidence: 99%

“…Particularly, it is difficult to distinguish true high winds from the rain-contaminated Ku-band retrievals [20], making the estimation of TC intensity with the Ku-band scatterometer wind speed unreliable. In addition, scatterometers are generally calibrated against in situ buoy measurements [7], leading to a large underestimation of high and extreme winds when using dropsonde wind data as a reference (i.e., the gold standard for the extreme winds community). A recent study by Polverari et al [6] shows, though, that the ASCAT high and extreme winds can be made consistent with the extreme winds from the airborne Stepped-Frequency Microwave Radiometer (SFMR), which is calibrated with dropsonde data, by applying an appropriate high-wind rescaling, which may be useful in estimating the TC intensity following the tropical cyclone community standards.…”

Section: Introductionmentioning

confidence: 99%

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Characterization of Tropical Cyclone Intensity Using the HY-2B Scatterometer Wind Data

et al. 2022

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The estimation of tropical cyclone (TC) intensity using Ku-band scatterometer data is challenging due to rain perturbation and signal saturation in the radar backscatter measurements. In this paper, an alternative approach to directly taking the maximum scatterometer-derived wind speed is proposed to assess the TC intensity. First, the TC center location is identified based on the unique characteristics of wind stress divergence/curl near the TC core. Then the radial extent of 17-m/s winds (i.e., R17) is calculated using the wind field data from the Haiyang-2B (HY-2B) scatterometer (HSCAT). The feasibility of HSCAT wind radii in determining TC intensity is evaluated using the maximum sustained wind speed (MSW) in the China Meteorological Administration best-track database. It shows that the HSCAT R17 value generally better correlates with the best-track MSW than the HSCAT maximum wind speed, therefore indicating the potential of using the HSCAT data to improve the TC nowcasting capabilities.

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“…Recent applications of conventional GNSS-R, including the ongoing NASA CYGNSS mission, focus on both ocean and land remote sensing, see e.g., [11][12][13][14]. Additionally, in the Remote Sens.…”

Section: Introductionmentioning

confidence: 99%

Link Budget Analysis for GNSS-R Sea Surface Return in Arbitrary Acquisition Geometries Using BA-PTSM

Martino¹,

Simone²,

Iodice³

et al. 2022

Remote Sensing

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In this article, we present a link budget analysis for Global Navigation Satellite System (GNSS) signals scattered off the sea surface in arbitrary acquisition geometries. The aim of our study is to investigate the reliability of the Geometrical Optics (GO) scattering model, which accurately describes sea surface scattering at and near the specular reflection direction, in properly modeling the sea surface return in far-from-specular acquisition geometries, which are of interest for maritime surveillance purposes and where GO is expected to fail. To this end, we adopted the recent Bistatic Anisotropic Polarimetric Two-Scale Model (BA-PTSM), which revealed good agreement with advanced scattering models, such as the second-order Small Slope Approximation (SSA2), regardless of the acquisition geometry, with the advantage of a reduced computational complexity. Numerical results have been derived for both circular polarization channels and for both spaceborne and airborne GNSS-Reflectometry (GNSS-R). It has been shown that, as long as conventional GNSS-R processing is assumed, GO can be safely adopted for simulation and analysis of spaceborne GNSS-R data regardless of the acquisition geometry and sea state, whereas more accurate scattering models, e.g., BA-PTSM, should be used for airborne receivers in far-from-specular configurations.

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Assessment of CYGNSS Wind Speed Retrievals in Tropical Cyclones

Cited by 13 publications

References 49 publications

Tropical Cyclone Wind Field Reconstruction and Validation Using Measurements from SFMR and SMAP Radiometer

Tropical Cyclone Wind Field Reconstruction and Validation Using Measurements from SFMR and SMAP Radiometer

Characterization of Tropical Cyclone Intensity Using the HY-2B Scatterometer Wind Data

Link Budget Analysis for GNSS-R Sea Surface Return in Arbitrary Acquisition Geometries Using BA-PTSM

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