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High-resolution sea surface wind speeds of Super Typhoon Lekima (2019) retrieved by Gaofen-3 SAR

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Abstract

Gaofen-3 (GF-3) is the first Chinese space-borne multi-polarization synthetic aperture radar (SAR) instrument at C-band (5.43 GHz). In this paper, we use data collected from GF-3 to observe Super Typhoon Lekima (2019) in the East China Sea. Using a VH-polarized wide ScanSAR (WSC) image, ocean surface wind speeds at 100m horizontal resolution are obtained at 21:56:59 UTC on 8 August 2019, with the maximum wind speed, 38.9 m·s−1. Validating the SAR-retrieved winds with buoy-measured wind speeds, we find that the root mean square error (RMSE) is 1.86 m·s−1, and correlation coefficient, 0.92. This suggests that wind speeds retrieved from GF-3 SAR are reliable. Both the European Centre for Medium-Range Weather Forecasts (ECMWF) fine grid operational forecast products with spatial resolution, and China Global/Regional Assimilation and Prediction Enhance System (GRAPES) have good performances on surface wind prediction under weak wind speed condition (< 24 m·s−1), but underestimate the maximum wind speed when the storm is intensified as a severe tropical storm (> 24 m·s−1). With respect to SAR-retrieved wind speeds, the RMSEs are 5.24 m·s−1 for ECMWF and 5.17 m·s−1 for GRAPES, with biases of 4.16 m·s−1 for ECMWF and 3.84 m·s−1 for GRAPES during Super Typhoon Lekima (2019).

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References

  • Alley R B, Emanuel K A, Zhang F (2019). Advances in weather prediction. Science, 363(6425): 342–344

    Article  Google Scholar 

  • Buchanan S, Misra V, Bhardwaj A (2018). Integrated kinetic energy of Atlantic tropical cyclones in a global ocean surface wind analysis. Int J Climatol, 38(6): 2651–2661

    Article  Google Scholar 

  • Cangialosi J P, Kimberlain T B, Beven J L II, Demaria M (2015). The validity of dvorak intensity change constraints for tropical cyclones. Weather Forecast, 30(4): 1010–1015

    Article  Google Scholar 

  • Chou K H, Wu C C, Lin S Z (2013). Assessment of the ASCAT wind error characteristics by global dropwindsonde observations. J Geophys Res Atmos, 118(16): 9011–9021

    Article  Google Scholar 

  • Deng M, Zhang G, Zhao R, Li S, Li J (2017). Improvement of gaofen-3 absolute positioning accuracy based on cross-calibration. Sensors (Basel), 17(12): 2903

    Article  Google Scholar 

  • Fang H, Xie T, Perrie W, Zhang G, Yang J, He Y (2018). Comparison of C-band quad-polarization synthetic aperture radar wind retrieval models. Remote Sens, 10(9): 1448

    Article  Google Scholar 

  • Gao Y, Guan C, Sun J, Xie L (2019). A wind speed retrieval model for Sentinel-1A EW mode cross-polarization images. Remote Sens, 11 (2): 153

    Article  Google Scholar 

  • Hwang P A, Stoffelen A, van Zadelhoff G J, Perrie W, Zhang B, Li H, Shen H (2015). Cross-polarization geophysical model function for C-band radar backscattering from the ocean surface and wind speed retrieval. J Geophys Res Oceans, 120(2): 893–909

    Article  Google Scholar 

  • Huang X, Peng X, Fei J, Cheng X, Ding J, Yu D (2021). Evaluation and error analysis of official tropical cyclone intensity forecasts during 2005–2018 for the western North Pacific. J Meteorol Soc Jpn, 99

  • Jin Q, Fan X, Liu J, Xue Z, Jian H (2020). Estimating tropical cyclone intensity in the South China Sea using the XGBoost Model and FengYun Satellite images. Atmosphere, 11(4): 423

    Article  Google Scholar 

  • Komarov A S, Zabeline V, Barber D G (2014). Ocean surface wind speed retrieval from C-band SAR images without wind direction input. IEEE Trans Geosci Remote Sens, 52(2): 980–990

    Article  Google Scholar 

  • Leite G C, Ushizima D M, Medeiros F N S, de Lima G G (2010). Wavelet analysis for wind fields estimation. Sensors (Basel), 10(6): 5994–6016

    Article  Google Scholar 

  • Lin M, Ye X, Yuan X (2017). The first quantitative joint observation of typhoon by Chinese GF-3 SAR and HY-2A microwave scatterometer. Acta Oceanol Sin, 36(11): 1–3

    Article  Google Scholar 

  • Magnusson L, Bidlot J R, Bonavita M, Brown A R, Browne P A, De Chiara G, Dahoui M, Lang S T K, McNally T, Mogensen K S, Pappenberger F, Prates F, Rabier F, Richardson D S, Vitart F, Malardel S (2019). ECMWF activities for improved hurricane forecasts. Bull Am Meteorol Soc, 100(3): 445–458

    Article  Google Scholar 

  • Montgomery M T, Smith R K (2017). Recent developments in the fluid dynamics of tropical cyclones. Annual Review of Fluid Mechanics, 49: 541–574

    Article  Google Scholar 

  • Mueller K J, DeMaria M, Knaff J, Kossin J P, Vonder Haar T H (2006). Objective estimation of tropical cyclone wind structure from infrared satellite data. Weather Forecast, 21(6): 990–1005

    Article  Google Scholar 

  • Ren L, Yang J, Mouche A A, Wang H, Zheng G, Wang J, Zhang H, Lou X, Chen P (2019). Assessments of ocean wind retrieval schemes used for Chinese gaofen-3 synthetic aperture radar co-polarized data. IEEE Trans Geosci Remote Sens, 57(9): 7075–7085

    Article  Google Scholar 

  • Sangster S J, Landsea C W (2020). Constraints in dvorak wind speed estimates: how quickly can hurricanes intensify? Weather Forecast, 35(4): 1235–1241

    Article  Google Scholar 

  • Shao W, Ding Y, Li J, Gou S, Nunziata F, Yuan X, Zhao L (2019). Wave retrieval under typhoon conditions using a machine learning method applied to Gaofen-3 SAR imagery. Can J Rem Sens, 45(6): 723–732

    Article  Google Scholar 

  • Shao W, Yuan X, Sheng Y, Sun J, Zhou W, Zhang Q (2018). Development of wind speed retrieval from cross-polarization Chinese gaofen-3 synthetic aperture radar in typhoons. Sensors (Basel), 18(2): 412–427

    Article  Google Scholar 

  • Shen H, Perrie W, He Y (2016). Evaluation of hurricane wind speed retrieval from cross-dual-pol SAR. Int J Remote Sens, 37(3): 599–614

    Article  Google Scholar 

  • Shen H, Perrie W, He Y, Liu G (2014). Wind speed retrieval from VH dual-polarization RADARSAT-2 SAR Images. IEEE Trans Geosci Remote Sens, 52(9): 5820–5826

    Article  Google Scholar 

  • Stoffelen A, Verspeek J A, Vogelzang J, Verhoef A (2017). The CMOD7 geophysical model function for ASCAT and ERS wind retrievals. IEEE J Sel Top Appl Earth Obs Remote Sens, 10(5): 2123–2134

    Article  Google Scholar 

  • Uhlhorn E W, Klotz B W, Vukicevic T, Reasor P D, Rogers R F (2014). Observed hurricane wind speed asymmetries and relationships to motion and environmental shear. Mon Weather Rev, 142(3): 1290–1311

    Article  Google Scholar 

  • Wang H, Yang J, Mouche A, Shao W, Zhu J, Ren L, Xie C (2017). GF-3 SAR oceanwind retrieval: the first view and preliminary assessment. Remote Sens, 9(7): 694–706

    Article  Google Scholar 

  • van Zadelhoff G J, Stoffelen A, Vachon P W, Wolfe J, Horstmann J, Belmonte Rivas M (2014). Retrieving hurricane wind speeds using cross-polarization C-band measurements. Atmos Meas Tech, 7(2): 437–449

    Article  Google Scholar 

  • Zhang B, Perrie W, Zhang J A, Uhlhorn E W, He Y (2014). Highresolution hurricane vector winds from C-band dual-polarization SAR observations. J Atmos Ocean Technol, 31(2): 272–286

    Article  Google Scholar 

  • Zhang G, Li X, Perrie W, Hwang P A, Zhang B, Yang X (2017a). A hurricane wind speed retrieval model for C-band RADARSAT-2 cross-polarization ScanSAR images. IEEE Trans Geosci Remote Sens, 55(8): 4766–4774

    Article  Google Scholar 

  • Zhang G, Perrie W, Li X, Zhang J A (2017b). A hurricane morphology and sea surface wind vector estimation model based on C-band cross-polarization SAR imagery. IEEE Trans Geosci Remote Sens, 55(3): 1743–1751

    Article  Google Scholar 

  • Zhang T, Li X M, Feng Q, Ren Y, Shi Y (2019). Retrieval of sea surface wind speeds from Gaofen-3 full polarimetric data. Remote Sens, 11 (7): 813

    Article  Google Scholar 

  • Zhong R, Xu S, Huang F, Wu X (2020). Reasons for the weakening of tropical depressions in the South China Sea. Mon Weather Rev, 148 (8): 3453–3469

    Article  Google Scholar 

  • Zhu X S, Yu H, Mao Z C, Xu M, Tan J G (2016). Satellite-based analysis on the concentric eyewall replacement cycles of super Typhoon Muifa (1109). J Trop Meteorol, 22: 330–340

    Google Scholar 

  • Zhu X S, Yu H (2019). Environmental influences on the intensity and configuration of tropical cyclone concentric eyewalls in the western North Pacific. J Meteor Soc Japan, 97: 153–173

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of Zhejiang Province (Nos. LQ21D060001, LQ21D050001), Basic Public Welfare Research Program of Zhejiang Province(Nos. LGF18D050001), Climate Change Special Program of China Meteorological Administration(Nos. CCSF202036), National Key R&D Program of China (Nos. 2016YFC1401003, 2017YFE0107700), National Natural Science Foundation of China (Nos.41705096, 41775065), Research Program from Science and Technology Committee of Shanghai (Nos.19dz1200101), Key Research and Development Program of Zhejiang Province(Nos. 2021C02036), the open fund of State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, MNR (Nos. QNHX2012), and the Canadian Space Agency Government Research Initiative Program.

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Authors and Affiliations

  1. Zhejiang Climate Centre, Hangzhou, 310017, China

    He Fang, Gaofeng Fan, Zhengquan Li, Juzhen Cai & Yue He

  2. State Key Laboratory of Satellite Ocean Environment Dynamics (Ministry of Natural Resources), Second Institute of Oceanography, Hangzhou, 310012, China

    He Fang & Jingsong Yang

  3. Shanghai Typhoon Institute, and Key Laboratory of Numerical Modeling for Tropical Cyclone of China Meteorological Administration, Shanghai, 200030, China

    Xuesong Zhu

  4. Fisheries & Oceans Canada, Bedford Institute of Oceanography, Dartmouth, B2Y4A2, Canada

    William Perrie

  5. School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Tao Xie

Authors
  1. He Fang
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  2. William Perrie
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  3. Gaofeng Fan
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  4. Zhengquan Li
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  5. Juzhen Cai
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  6. Yue He
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  7. Jingsong Yang
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  8. Tao Xie
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  9. Xuesong Zhu
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Correspondence to Xuesong Zhu.

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Fang, H., Perrie, W., Fan, G. et al. High-resolution sea surface wind speeds of Super Typhoon Lekima (2019) retrieved by Gaofen-3 SAR. Front. Earth Sci. 16, 90–98 (2022). https://doi.org/10.1007/s11707-021-0887-8

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  • DOI: https://doi.org/10.1007/s11707-021-0887-8

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