Evaluation of Chinese Scatterometer Ocean Surface Wind Data: Preliminary Analysis

Ke, Zhao; Zhao, Chunjiang; Chen, Ge

doi:10.1029/2020ea001482

Cited by 12 publications

(4 citation statements)

References 25 publications

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“…This is expected to provide more robust results from the RFM method. Currently operating missions are HY-2B and HY-2C (Haiyang satellites) with the HSCAT scatterometer instrument onboard launched by the Chinese National Satellite Ocean Application Service (NSOAS) (Zhao et al (2021)). The China-France Oceanography Satellite CFOSAT satellite with a scatterometer launched by Centre National d'Etudes Spatiales (CNES)…”

Section: Data Sampling Characteristicsmentioning

confidence: 99%

Vertical extrapolation of ASCAT ocean surface winds using machine learning techniques

Hatfield

Hasager²,

Karagali³

2022

Preprint

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Abstract. The increasing demand for wind energy offshore requires more hub-height relevant wind information while larger wind turbine sizes require measurements at greater heights. In situ measurements are harder to acquire at higher atmospheric levels; meanwhile the emergence of machine-learning applications has led to several studies demonstrating the improvement in accuracy for vertical wind extrapolation over conventional power-law and logarithmic profile methods. Satellite wind retrievals supply multiple daily wind observations offshore, however only at 10 m height. The goal of this study is to develop and validate novel machine-learning methods using satellite wind observations and near-surface atmospheric measurements to extrapolate wind speeds to higher heights. A machine-learning model is trained on 12 years of collocated offshore wind measurements from a meteorological mast (FINO3) and space-bourne wind observations from the Advanced Scatterometer (ASCAT). The model is extended vertically to predict the FINO3 vertical wind profile. Horizontally, it is validated against the NORA3 meso-scale model reanalysis data. In both cases the model slightly over-predicts the wind speed with differences of 0.25 and 0.40 m s-1 respectively. An important feature in the model training process is the air-sea temperature difference, thus satellite sea surface temperature observations were included in the horizontal extension of the model, resulting in 0.20 m s-1 differences with NORA3. A limiting factor when training machine-learning models with satellite observations is the small finite number of daily samples at discrete times; this can skew the training process to higher/lower wind speed predictions depending on the average wind speed at the satellite observational times. Nonetheless, results shown in this study demonstrate the applicability of using machine learning techniques to extrapolate long-term satellite wind observations when enough samples are available.

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Section: Data Sampling Characteristicsmentioning

confidence: 99%

Vertical extrapolation of ASCAT ocean surface winds using machine learning techniques

Hatfield

Hasager²,

Karagali³

2022

Preprint

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“…At the same time, rainfall causes backscattering attenuation, which affects data accuracy [6][7][8]. Nevertheless, HY-2B data have demonstrated superior quality compared to that of other products in its class [9][10][11] and have been utilized in various applications. In some studies on data applications, HSCAT-B data have shown good application value.…”

Section: Introductionmentioning

confidence: 99%

Study of Quality Control Methods Utilizing IRMCD for HY-2B Data Assimilation Application

Hu,

Zhang,

et al. 2024

Atmosphere

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Quality control (QC) of HaiYang-2B (HY-2B) satellite data is mainly based on the observation process, which remains uncertain for data assimilation (DA). The data in operation have not been widely used in numerical weather prediction. To ensure HY-2B data meet the theoretical assumptions for DA applications, the iterated reweighted minimum covariance determinant (IRMCD) QC method was studied in HY-2B data based on the typhoon “Chanba”. The statistical results showed that most of the outliers were eliminated, and the observation increment distribution of the HY-2B data after QC (QCed) was closer to a Gaussian distribution than the raw data. The kurtosis and skewness of the QCed data were much closer to zero. The QCed track demonstrated the lowest accumulated error and the best intensity in typhoon assimilation, and the QCed intensity was closest to the observation during the nearshore enhancement, exhibiting the strongest intensity among the experiment. Further analysis revealed that the improvement was accompanied by a significant reduction in vertical wind shear during the nearshore enhancement of the typhoon. The QCed moisture flux divergence and vertical velocity in the upper layer increased significantly, which promoted the upward transport of momentum in the lower layers and contributed to the maintenance of the typhoon’s barotropic structure. Compared with the assimilation of raw data, the effective removal of outliers using the IRMCD algorithm significantly improved the simulation results for typhoons.

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“…After the launch of satellite-borne satellite sensors, verification of data products is an essential procedure (Bailey & Werdell, 2006;Ye et al, 2015). Multiple data sources including satellite or buoy data are employed to validate CFOSAT SCAT wind field measurements, revealing a strong agreement between the inversion results of SCAT winds and satellite or buoy observations (Dong et al, 2020;Ye, Li, et al, 2021;Zhao et al, 2021). The root mean square error (RMSE) for wind speed measurement was below 1.5 m/s, while the measurement error for wind direction was less than 15°, indicating that the overall quality of the wind field has attained an advanced level.…”

Section: Introductionmentioning

confidence: 99%

Calibration of CFOSAT Off‐Nadir SWIM SWH Product Based on CNN‐LSTM Model

Zhang,

Qi,

Yan

et al. 2024

Earth and Space Science

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High‐precision observation of significant wave height (SWH) is crucial for marine research. The Surface Waves Investigation and Monitoring (SWIM) aboard the China France Oceanography Satellite (CFOSAT) provides the ocean wave spectrum that allows for the calculation of the off‐nadir SWH parameters, but there exists a certain bias with the in‐situ SWH values. To improve the accuracy of the SWH calculation bias from the off‐nadir 6°, 8°, 10° wave spectra and the whole combined spectrum, this paper establishes a spatio‐temporal hybrid model that combines convolutional neural network (CNN) and long short‐term memory network (LSTM). Additionally, to further correct bias exhibited under high sea state, we introduce a bias correction module based on deep neural network (DNN) to adjust the SWIM off‐nadir SWH greater than 4 m. The experimental results demonstrate a significant enhancement in the accuracy of corrected SWIM off‐nadir SWH, and the best calibration result is 10° with 0.267 m root mean square error (RMSE), and 0.979 correlation coefficient (R) compared with the ERA5 value. We conducted a comprehensive study and analysis on the performance of the proposed model under different wave heights, extreme sea states, and wind and swell regions. Meanwhile, the buoy and altimeters are leveraged to render further evaluation the RMSE of the corrected SWH is less than 0.5 m.

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Evaluation of Chinese Scatterometer Ocean Surface Wind Data: Preliminary Analysis

Abstract: The qualities of wind products under different atmospheric stratification conditions are also evaluated. The results reveal that the order of retrieved wind quality from high to low can be ranked as HY-2B/SCAT, CFOSCAT and HY-2A/SCAT.

Cited by 12 publications

References 25 publications

Vertical extrapolation of ASCAT ocean surface winds using machine learning techniques

Vertical extrapolation of ASCAT ocean surface winds using machine learning techniques

Study of Quality Control Methods Utilizing IRMCD for HY-2B Data Assimilation Application

Calibration of CFOSAT Off‐Nadir SWIM SWH Product Based on CNN‐LSTM Model

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