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

Abstract

Gaofen-3 (GF-3) is the first Chinese spaceborne 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).

Graphical abstract

Keywords

synthetic aperture radar / wind speed / numerical weather predication (NWP) / typhoon

References

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

[2] 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

[3] 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

[4] 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

[5] 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

[6] 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

[7] 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

[8] 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

[9] 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

[10] 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

[11] 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

[12] 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

[13] 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

[14] 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

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

[16] 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

[17] 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

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

[19] 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

[20] 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

[21] 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

[22] 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

[23] 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

[24] 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

[25] 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

[26] 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

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

[28] 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

[29] 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

[30] 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

[31] 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

[32] 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

[33] 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

Acknowledgement

This work was supported in part by the Natural Science Foundation of Zhejiang Province (No. LQ21D060001), the Fengyun Application Pioneering Project (No. FY-APP-2021.0105), the Science and Technology Project of Zhejiang Meteorological Bureau (No. 2021YB07), the Innovation and Development Project of China Meteorological Administration (No. CXFZ2022J040), the National Key R&D Program of China (No. 2018YFC1506404), the Basic Public Welfare Research Program of Zhejiang Province (No. LGF18D050001), the Climate Change Special Program of China Meteorological Administration (No. CCSF202036), the Key Research and Development Program of Zhejiang Province (No. 2021C02036), the Research Program from Science and the Technology Committee of Shanghai (No.19dz1200101), the Shanghai Typhoon Institute (No. 2021JB05), and the open fund of State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, MNR (No. QNHX2012).

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2021 Higher Education Press