An improved NASA Scatterometer geophysical model function for tropical cyclones

Jones, W. Linwood; Park, Jun-Dong; Donnelly, William J.; Carswell, J.; McIntosh, R.E.; Zec, Josko; Yueh, Simon

doi:10.1109/igarss.1998.703719

Cited by 12 publications

(5 citation statements)

References 6 publications

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“…The GMF involved in wind retrieval is a highly nonlinear model. Researchers have attempted to improve the GMF through machine learning for scatterometers of different wave lengths and platforms [33,34]. Direct inversion of the sea surface wind field using a neural network without using GMF is also an important application in wind retrieval.…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Wind Measurements in Rainy Conditions Using the HY-2A Scatterometer

Wang,

Xie,

Deng

et al. 2023

Remote Sensing

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Wind measurement using spaceborne scatterometers has been used for various scientific and operational purposes. However, the major problem of such measurements is contamination by rain. To improve the wind measurement using the HY-2A scatterometer under rainy conditions, a neural network-based model was established in this study. The model is almost autonomous in that it only needs the backscatter coefficient measurement data and the observation geometry information from the HY-2A scatterometer itself. The model can distinguish between rain-contaminated wind pixels and rain-free wind pixels and significantly improve the accuracy of wind speed measurements using HY-2A scatterometer alone. TAO data and linearly calibrated ECMWF data were used in the study to validate the neural network-inverted wind speed. Under no rain conditions, the RMS of the neural network-inverted wind speed and TAO wind speed was 1.06 m/s, with a deviation of −0.21 m/s, which is a small difference from the standard method inverted wind speed. Under rain conditions, the RMS and deviation were 1.94 m/s and 0.66 m/s, respectively, which were better than the statistical results of the conventional maximum likelihood estimation method. The validated results using linearly calibrated data also indicate that the neural network-inverted wind speed is closer to the validation data under rain conditions.

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Section: Introductionmentioning

confidence: 99%

Neural Network-Based Wind Measurements in Rainy Conditions Using the HY-2A Scatterometer

Wang,

Xie,

Deng

et al. 2023

Remote Sensing

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“…Obviously, the development of a method for their early identification would allow more time for endangered communities to take suitable precautions. In recent years, there has been considerable research into forecasting techniques that make use of devices such as scatterometers [9], microwave radiometers [16], and QuikSCAT [24]. To date, the information generated by these devices has required skilled human interpretation.…”

Section: Introductionmentioning

confidence: 99%

Tropical Cyclone Forecast using Angle Features and Time Warping

Liu

Bao²,

Wang³

et al. 2006

The 2006 IEEE International Joint Conference on Neural Network Proceedings

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The most popular approach to comparing two given Tropical Cyclones (TCs) is to measure the distance between various contour points of the TC extracted from a satellite image. However, this measure has a very high computational cost as it involves many point-to-point calculations. Moreover, this measure does not reflect the most distinctive features of a tropical cyclone, their spiral shape. In this paper, we propose the use of angle features and time warping for TC forecast. The Gradient Vector Flow (GVF) snake model is applied to extract the contour points of a dominant tropical cyclone from the satellite image. Dvorak templates are used as references to predict the intensity of the tropical cyclone. Given two sets of contour points, one for each tropical cyclone, we retrieve the similarity of two shapes using angle features found among the successive contour points. We adopt a time warping approach to produce a fast and accurate result. Experimental results have shown that our approach is better than other conventional comparison approaches such as Hausdorff distance measure.

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“…GMF is a transfer function, providing the relationship between the radar observable σ 0 and the surface wind vector (speed and direction). It is dependent on measurement geometry (incidence angle and antenna beam viewing direction relative to upwind) and radar parameters (polarization and wavelength) [15][16][17]. To achieve the required GMF accuracy, a large amount of calibration data is required.…”

Section: Ancillary Data Sourcesmentioning

confidence: 99%

RapidScat Cross-Calibration Using the Double Difference Technique

et al. 2017

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RapidScat is a National Aeronautics and Space Administration (NASA) Ku-Band scatterometer that was operated onboard the International Space Station between September 2014 and August 2016 when the mission effectively ended after an irrecoverable instrument failure. A unique non-Sun-synchronous orbit facilitated global contiguous geographical sampling between the ±56 • latitude. For the first time, such an orbit enabled an overlap with other scatterometers flying in Sun-synchronous orbits. The double-difference technique was developed and successfully used for microwave radiometer calibration at the Remote Sensing Laboratory at the University of Central Florida, USA. This paper presents the extension of the double difference methodology to scatterometry. The methodology has been adopted for the cross-instrument calibration between RapidScat and QuikScat scatterometers simultaneously orbiting the Earth on-board two independent satellite platforms. The double-difference technique was deployed to compare measurements from these two scatterometers, as a more accurate alternative to the classic single difference approach. The work summarized in this paper addressed a cross-calibration algorithm developed and applied to RapidScat and QuikScat data in the period from January 2015 to March 2016. The initial results of the statistical analysis and biases between the two scatterometers are presented. Calculated biases may be used for measurement correction and reprocessing.

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An improved NASA Scatterometer geophysical model function for tropical cyclones

Cited by 12 publications

References 6 publications

Neural Network-Based Wind Measurements in Rainy Conditions Using the HY-2A Scatterometer

Neural Network-Based Wind Measurements in Rainy Conditions Using the HY-2A Scatterometer

Tropical Cyclone Forecast using Angle Features and Time Warping

RapidScat Cross-Calibration Using the Double Difference Technique

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