Inverse Weighting Method for Sea Surface Wind Direction Retrieval From Tiangong-2 Interferometric Imaging Radar Altimeter Imagery

In this study, we performed preliminary comparative evaluation and correction of two-dimensional sea surface height (SSH) data from the Chinese Tiangong-2 Interferometric Imaging Radar Altimeter (InIRA) with the goal of advancing its retrieval. Data from the InIRA were compared with one-dimensional SSH data from the traditional altimeters Jason-2, Saral/AltiKa, and Jason-3. Because the sea state bias (SSB) of distributed InIRA data has not yet been considered, consistency was maintained by neglecting the SSB for the traditional altimeters. The results of the comparisons show that the InIRA captures the same SSH trends as those obtained by traditional altimeters. However, there is a significant deviation between InIRA and traditional altimeter SSHs; consequently, systematic and parametric biases were analyzed. The parametric bias was found to be related to the incidence angles and a significant wave height. Upon correcting the two biases, the standard deviation significantly reduced to 8.1 cm. This value is slightly higher than those of traditional altimeters, which typically have a bias of ~7.0 cm. The results indicate that the InIRA is promising in providing a wide swath of SSH measurements. Moreover, we recommend that the InIRA retrieval algorithm should consider the two biases to improve SSH accuracy.

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Sea Surface Wind Correction Using HF Ocean Radar and Its Impact on Coastal Wave Prediction

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-16-0249.1 ◽

2017 ◽

Vol 34 (9) ◽

pp. 2001-2020 ◽

Cited By ~ 3

Author(s):

Yukiharu Hisaki

Keyword(s):

Wind Direction ◽

Surface Wind ◽

Hf Radar ◽

Sea Surface ◽

Wave Parameter ◽

Simple Method ◽

Wind Speeds ◽

Wave Prediction ◽

Sea Surface Wind ◽

The Impact

AbstractBoth wind speeds and wind directions are important for predicting wave heights near complex coastal areas, such as small islands, because the fetch is sensitive to the wind direction. High-frequency (HF) radar can be used to estimate sea surface wind directions from first-order scattering. A simple method is proposed to correct sea surface wind vectors from reanalysis data using the wind directions estimated from HF radar. The constraints for wind speed corrections are that the corrections are small and that the corrections of horizontal divergences are small. A simple algorithm for solving the solution that minimizes the weighted sum of the constraints is developed. Another simple method is proposed to correct sea surface wind vectors. The constraints of the method are that corrections of wind vectors and horizontal divergences from the reanalysis wind vectors are small and that the projection of the corrected wind vectors to the direction orthogonal to the HF radar–estimated wind direction is small. The impact of wind correction on wave parameter prediction is large in the area in which the fetch is sensitive to wind direction. The accuracy of the wave prediction is improved by correcting the wind in that area, where correction of wind direction is more important than correction of wind speeds for the improvement. This method could be used for near-real-time wave monitoring by correcting forecast winds using HF radar data.

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Extraction of sea surface wind direction from bistatic high-frequency radar Doppler spectra

OCEANS'11 MTS/IEEE KONA ◽

10.23919/oceans.2011.6107006 ◽

2011 ◽

Author(s):

Weimin Huang ◽

Eric Gill

Keyword(s):

Wind Direction ◽

High Frequency ◽

Surface Wind ◽

Sea Surface ◽

High Frequency Radar ◽

Sea Surface Wind

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Wind Direction Retrieval Using Support Vector Machine from CYGNSS Sea Surface Data

Remote Sensing ◽

10.3390/rs13214451 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4451

Author(s):

Yun Zhang ◽

Xu Chen ◽

Wanting Meng ◽

Jiwei Yin ◽

Yanling Han ◽

...

Keyword(s):

Support Vector Machine ◽

Wind Direction ◽

Surface Wind ◽

Sea Surface ◽

Support Vector ◽

Navigation Satellite ◽

Feature Parameters ◽

Sea Surface Wind ◽

Global Navigation ◽

Global Navigation Satellite

In view of the difficulty of wind direction retrieval in the case of the large space and time span of the global sea surface, a method of sea surface wind direction retrieval using a support vector machine (SVM) is proposed. This paper uses the space-borne global navigation satellite systems reflected signal (GNSS-R) as the remote sensing signal source. Using the Cyclone Global Navigation Satellite System (CYGNSS) satellite data, this paper selects a variety of feature parameters according to the correlation between the features of the sea surface reflection signal and the wind direction, including the Delay Doppler Map (DDM), corresponding to the CYGNSS satellite parameters and geometric feature parameters. The Radial Basis Function (RBF) is selected, and parameter optimization is performed through cross-validation based on the grid search method. Finally, the SVM model of sea surface wind direction retrieval is established. The result shows that this method has a high retrieval classification accuracy using the dataset with wind speed greater than 10 m/s, and the root mean square error (RMSE) of the retrieval result is 26.70°.

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Development of a Gray-Level Co-Occurrence Matrix-Based Texture Orientation Estimation Method and Its Application in Sea Surface Wind Direction Retrieval From SAR Imagery

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2018.2812778 ◽

2018 ◽

Vol 56 (9) ◽

pp. 5244-5260 ◽

Cited By ~ 11

Author(s):

Gang Zheng ◽

Xiaofeng Li ◽

Lizhang Zhou ◽

Jingsong Yang ◽

Lin Ren ◽

...

Keyword(s):

Wind Direction ◽

Surface Wind ◽

Estimation Method ◽

Sea Surface ◽

Gray Level ◽

Orientation Estimation ◽

Sea Surface Wind ◽

Texture Orientation ◽

Sar Imagery ◽

Occurrence Matrix

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Research on Internal Solitary Wave Detection and Analysis Based on Interferometric Imaging Radar Altimeter Onboard the Tiangong-2 Space Laboratory

Remote Sensing ◽

10.3390/rs14010174 ◽

2021 ◽

Vol 14 (1) ◽

pp. 174

Author(s):

Hao Zhang ◽

Chenqing Fan ◽

Junmin Meng ◽

Shibao Li ◽

Lina Sun

Keyword(s):

Sea Surface ◽

Height Anomaly ◽

Small Scale ◽

Interferometric Imaging ◽

Radar Altimeter ◽

Imaging Radar ◽

Flight Direction ◽

Sea Surface Height Anomaly ◽

Geometric Relationship ◽

Detection Effect

The Tiangong-2 space laboratory was launched by China on 15 September 2016, carrying the Interferometric Imaging Radar Altimeter (InIRA), the first of the latest generation of imaging altimeters that can perform imaging and acquire elevation information simultaneously. This paper analyzes the feasibility of using InIRA images to obtain two-dimensional characteristics of oceanic internal solitary waves (ISWs) and information about vertical sea surface fluctuations caused by the propagation of ISWs. The results show that InIRA demonstrates a relatively reliable ability to observe ISWs with high resolution and can identify the fine-scale features of ISWs of different forms. Furthermore, InIRA can observe centimeter-level changes in the Sea Surface Height Anomaly (SSHA) caused by ISWs. The geometric relationship between the sensor’s flight direction and the propagation direction of ISWs does not affect its detection effect. However, the swath width of InIRA is too narrow to fully capture ISW information, and the height accuracy of InIRA height product images is not insufficient to detect the height information of small-scale ISWs. These shortcomings need to be considered in the future development of imaging altimeters to increase their potential for detecting mesoscale phenomena in the ocean.

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