Ocean Surface Wind Speed Dependence and Retrieval From Off‐Nadir CFOSAT SWIM Data

Ren, Lin; Yang, Jianguo; Xu, Ying; Zhang, Y.; Zheng, Gang; Wang, J.; Dai, Jindong; Jiang, Cuiling

doi:10.1029/2020ea001505

Cited by 15 publications

(10 citation statements)

References 35 publications

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“…However, correlation for NRCS at incidence angles of 8-10 • with wind speed is very low, as shown in [13], and the highest correlation is observed at nadir. The analysis of wind speed retrieval performance in [21] proves this fact.…”

Section: Introductionmentioning

confidence: 58%

“…Similar comparison was performed for SWIM data in [21], where wind speed was obtained using the GMF approach. For incidence angles 0-6 • , both methods show good performance, but for θ ≥ 8 • , the GMF approach error sufficiently exceeds the error of the present method, and for θ = 10 • , the GMF method can not be applied, as was predicted in [13].…”

Section: Comparison With Buoymentioning

confidence: 99%

“…The approach in this works implies the building of geophysical model functions for each incidence angle. The same approach was applied to the data of SWIM (Surface Waves Investigation and Monitoring) radar onboard CFOSAT (Chinese-French Oceanography Satellite) [21]. SWIM also operates at low incidence angles ranging from 0 • to 10 • .…”

Section: Introductionmentioning

confidence: 99%

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Wind Speed Retrieval Algorithm Using Ku-Band Radar Onboard GPM Satellite

Panfilova

Karaev

2021

Remote Sensing

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The algorithm to retrieve wind speed in a wide swath from the normalized radar cross section (NRCS) was developed for the data of Dual Frequency Precipitation Radar (DPR) operating in scanning mode installed onboard a Global Precipitation Measurement (GPM) satellite. The data for Ku-band radar were used. Equivalent NRCS values at nadir were estimated in a wide swath under the geometrical optics approximation from off-nadir observations. Using these equivalent NRCS nadir values and the sea buoys data, the new parameterization of dependence between NRCS at nadir and the wind speed was obtained. The algorithm was validated using ASCAT (Advanced Scatterometer) data and revealed good accuracy. DPR data are promising for determining wind speed in coastal areas.

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Section: Introductionmentioning

confidence: 58%

Section: Comparison With Buoymentioning

confidence: 99%

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Wind Speed Retrieval Algorithm Using Ku-Band Radar Onboard GPM Satellite

Panfilova

Karaev

2021

Remote Sensing

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“…Nadir sounding of the sea surface with 0.2 s time intervals (5 Hz) ensures obtaining high‐quality significant wave height (H s ) and normalized radar cross section (

{\sigma }_{0}

). Near‐surface wind speed is calculated as a function of H s and

{\sigma }_{0\hspace*{.5em}}

using a parametric approach (Hauser et al., 2020; Gourrion et al., 2002; Ren et al., 2021). The accuracy of H s measurements is better than 10% (or 0.4 m).…”

Section: Datamentioning

confidence: 99%

Global Validation of SWIM/CFOSAT Wind Waves Against Voluntary Observing Ship Data

Grigorieva

Badulin

Gulev

2022

Earth and Space Science

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The article presents a joint analysis of wind and wave characteristics derived from Voluntary Observing Ship (VOS) data and measurements of the innovative Ku‐band radar SWIM (Surface Waves Investigation and Monitoring) carried by Chinese‐French Ocean SATellite (CFOSAT). Global distributions of significant wave height and wind speed in both data sets demonstrate good qualitative and quantitative agreement, especially in regions with a high spatio‐temporal density of visual observations. A particular focus is made on discriminating wave systems by joint analysis of separately observed wind sea and swell characteristics in VOS and the partitions of wave spectra measured by SWIM. It is shown that three wave partitions from SWIM cannot be clearly attributed to wind sea, first, and secondary swell systems. The first partition aggregates both wind sea and swell in the operational wavelength range of the SWIM radar, while the second and third partitions fit neither wind sea, nor swell. A comparison of VOS and SWIM data within a 50 km radius and a 30 min time lag shows a very close match for most parameters in terms of mean values, yet with relatively high dispersion of individual measurements.

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“…Sea ice plays an important role in global climate change, shipping, navigation and the extraction of natural resources (Komarov and Buehner, 2021), and influences the detection of other ocean phenomena; for example, sea wave retrieval requires the removal of sea ice 'pollution' (Ren et al, 2021;Wang J.K., et al, 2021). Thus, sea ice recognition has become a main task performed to meet the scientific and operational requirements of national and commercial ice services, which provides sea ice edge, extent, and concentration products (Cheng et al, 2020;de Geĺis et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Sea ice recognition for CFOSAT SWIM at multiple small incidence angles in the Arctic

et al. 2022

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Sea ice recognition is one of the main tasks for sea ice monitoring in the Arctic and is also applied for the detection of other ocean phenomena. The Surface Wave Investigation and Monitoring (SWIM) instrument, as an innovative remote sensor that operates at multiple small incidence angles, is different from existing sensors with moderate and normal incidence modes for sea ice monitoring. Sea ice recognition at small incidence angles has rarely been studied. Moreover, SWIM uses a discrimination flag of sea ice and sea water to remove sea ice from sea wave products. Therefore, this research focuses on sea ice recognition in the Arctic based on SWIM data from October 2020 to April 2021. Eleven features are first extracted, and applied for the analysis of the waveform characteristics using the cumulative probability distribution (CPD) and mutual information measurement (MIM). Then, random forest (RF), k-nearest neighbor (KNN) and support vector machine (SVM) classifiers are built, and their abilities of sea ice recognition are assessed. The optimal classifier is the KNN method with Euclidean distance and k equal to 11. Feature combinations are also used to separate sea ice and sea water based on the KNN method to select the optimal combination. Thus, the optimal classifier-feature assembly at each small incidence angle is established, and the highest overall accuracy reaches 97.1%. Moreover, the application of the optimal classifier–feature assemblies is studied, and its performance is fairly good. These assemblies yield high accuracies in the short- and long-term periods of sea ice recognition, and the overall accuracies are greater than 93.1%. So, the proposed method satisfies the SWIM requirement of removing the sea ice effect. Moreover, sea ice extents and edges can be extracted from SWIM sea ice recognition results at a high level of precision greater than 94.8%. As a result, the optimal classifier–feature assemblies based on SWIM data express the effectiveness of the SWIM approach in sea ice recognition. Our work not only highlights the new sea ice monitoring technology of remote sensing at small incidence angles, but also studies the application of SWIM data in sea ice services.

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Ocean Surface Wind Speed Dependence and Retrieval From Off‐Nadir CFOSAT SWIM Data

Cited by 15 publications

References 35 publications

Wind Speed Retrieval Algorithm Using Ku-Band Radar Onboard GPM Satellite

Wind Speed Retrieval Algorithm Using Ku-Band Radar Onboard GPM Satellite

Global Validation of SWIM/CFOSAT Wind Waves Against Voluntary Observing Ship Data

Sea ice recognition for CFOSAT SWIM at multiple small incidence angles in the Arctic

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