Vol.35, NO.2, 2024
Turn off MathJax
Article Contents
Article Navigation >  Journal of Applied Meteorological Science  > 2024  >  35(2): 225-236
Zhang Yunkai, Xu Na, Zhai Xiaochun, et al. A method to estimate sea surface wind vectors using geostationary satellites. J Appl Meteor Sci, 2024, 35(2): 225-236. DOI:  10.11898/1001-7313.20240208.
Citation: Zhang Yunkai, Xu Na, Zhai Xiaochun, et al. A method to estimate sea surface wind vectors using geostationary satellites. J Appl Meteor Sci, 2024, 35(2): 225-236. DOI:  10.11898/1001-7313.20240208.
shu

A Method to Estimate Sea Surface Wind Vectors Using Geostationary Satellites

DOI: 10.11898/1001-7313.20240208
  • 1.

    Chinese Academy of Meteorological Sciences, Beijing 100081

  • 2.

    Innovation Center for Fengyun Meteorological Satellite (FYSIC), Beijing 100081

  • 3.

    Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), CMA, Beijing 100081

  • Received Date: 2023-11-14
  • Rev Recd Date: 2024-01-18
  • Publish Date: 2024-03-27
    Abstract
  • Sea surface wind (SSW) is an essential physical parameter in the ocean and atmosphere, playing an irreplaceable role in hydrological and energy cycles, as well as in global and local climate systems. Polar-orbiting satellite instruments can gather a large amount of SSW information by observing surface roughness or wave height. Although observations from polar-orbiting satellites can cover the globe, there is a significant temporal gap for observing a fixed region. However, a geostationary satellite enables a relatively high observation frequency to accomplish this mission. Due to limitations in resolution, power consumption, and other factors, it is difficult for geostationary satellites to directly retrieve SSW. Nevertheless, it can obtain wind vectors at different altitudes by tracking the movement of clouds or clear-sky water vapor gradients in continuous satellite imagery, which is called atmospheric motion vector (AMV). There is a strong correlation between low-level AMV and SSW, and SSW could be estimated from low-level AMV. Previous studies estimated SSW based on low-level AMV mainly by empirical methods, which is unable to take the variation of AMV with height and latitude into consideration. Therefore, a new method based on a fully-connected neural network (FCNN) is proposed to address this issue. The theory of atmospheric dynamics, which explains how wind varies with altitude and latitude, is referenced, and FCNN is constructed by selecting physical parameters with strong causal relationships from geostationary satellite AMV information. The experiment is performed using GOES-16 advanced baseline imager (ABI) visible band AMV with a resolution of 0.5 km. After completing the wind estimation and comparing it with data from 93 National Data Buoy Center buoys nearshore or offshore North America from 1 January to 31 December in 2021, results show that root mean square error (RMSE) of the estimated wind speed from FCNN is less than 1.5 m·s-1. This represents a reduction of up to 0.24 m·s-1 compared to the empirical model. Additionally, the estimated wind direction shows slight improvement compared to AMV. After applying the model to the vicinity of a hurricane, and comparing it with reanalysis information for a total of 13 hours for 3 North Atlantic hurricanes and 3 Eastern Pacific hurricanes in 2022, results show that RMSE of wind speed estimated from FCNN is less than 1.1 m·s-1, reduced up to 0.04 m·s-1 compared to the result of the traditional empirical model. Additionally, there is no systematic bias in the low wind speed range.
    • FullText(HTML)
    References(37)

  • Fig. 1  Comparison of AMV and its products with ERA5 in wind speed

    (the shaded denotes the number of points in the range of 0.5 m·s-1)

    DownLoad: Full-Size Img PowerPoint

    Fig. 2  Comparison of WA with ERA5 in wind speed

    (the shaded denotes the number of points in the range of 0.5 m·s-1)

    DownLoad: Full-Size Img PowerPoint

    Fig. 3  Comparison of AMV and WA with ERA5 in wind direction

    (the shaded denotes the number of points in the range of 12°)

    DownLoad: Full-Size Img PowerPoint

    Fig. 4  Comparison of AMV and its products with NDBC buoy in wind speed

    (the shaded denotes the number of points in the range of 0.5 m·s-1)

    DownLoad: Full-Size Img PowerPoint

    Fig. 5  Comparison of AMV and WA with NDBC buoy in wind direction

    (the shaded denotes the number of points in the range of 12°)

    DownLoad: Full-Size Img PowerPoint

    Fig. 6  Comparison of WA with GOSE-16 AMV and ERA5 in the vicinity of Hurricane Fiona at 1800 UTC 22 Sep 2022   (a)GOES-16 VIS AMV barbs and GOES-16 VIS image, (b)WA barbs and GOES-16 VIS image, (c)ERA5 wind barbs and GOES-16 VIS image

    DownLoad: Full-Size Img PowerPoint

    Fig. 7  Comparison of AMV and WA with ERA5 in wind speed in the vicinity of hurricane

    (the shaded denotes the number of points in the range of 0.5 m·s-1)

    DownLoad: Full-Size Img PowerPoint

    Fig. 8  Comparison of AMV and WA with ERA5 wind direction in the vicinity of hurricane

    (the shaded denotes the number of points in the range of 12°)

    DownLoad: Full-Size Img PowerPoint

    Table  1  Distribution of GOSE-16 VIS AMV and wind direction and speed error of VIS AMV against ERA5 10 m wind in 2021

    高度层/hPa 样本量 风速/(m·s-1) 风向/(°)
    均方根误差 偏差 均方根误差 偏差
    (700, 750] 711556 3.48 2.90 42.88 1.91
    (750, 800] 2183024 2.92 2.39 37.44 0.58
    (800, 850] 6094145 2.30 1.65 27.90 1.39
    (850, 900] 18751431 1.76 1.25 19.11 -0.32
    (900, 950] 24815855 1.58 1.28 13.12 -0.34
    (950, 1000] 19127869 1.42 1.36 9.98 0.52
    DownLoad: Download CSV

    Table  2  Hurricane information by moment

    飓风名称 取样时刻 取样范围 所属海域
    凯伊 2022-09-06T18:00:00 8°~28°N，101°~121°W 东太平洋
    凯伊 2022-09-07T18:00:00 11°~31°N，103°~123°W 东太平洋
    玛德琳 2022-09-19T18:00:00 12°~32°N，100°~120°W 东太平洋
    菲奥娜 2022-09-20T18:00:00 12°~32°N，62°~82°W 北大西洋
    菲奥娜 2022-09-21T18:00:00 15°~35°N，62°~82°W 北大西洋
    菲奥娜 2022-09-22T18:00:00 20°~40°N，60°~80°W 北大西洋
    菲奥娜 2022-09-23T18:00:00 28°~48°N，52°~72°W 北大西洋
    伊恩 2022-09-26T18:00:00 10°~30°N，75°~95°W 北大西洋
    伊恩 2022-09-27T18:00:00 14°~34°N，74°~94°W 北大西洋
    伊恩 2022-09-28T18:00:00 16°~36°N，72°~92°W 北大西洋
    罗斯林 2022-10-21T18:00:00 7°~27°N，95°~115°W 东太平洋
    罗斯林 2022-10-22T18:00:00 8°~28°N，98°~118°W 东太平洋
    马丁 2022-11-02T18:00:00 26°~46°N，38°~58°W 北大西洋
    DownLoad: Download CSV
  • [1]
    Wang J, Zhang J, Wang J.Sea surface wind speed retrieval under rain with the HY-2 microwave radiometer.Acta Oceanol Sinica, 2017, 36(7):32-38. doi:  10.1007/s13131-017-1080-5
    [2]
    Zhang P, Hu X Q, Lu Q F, et al. FY-3E: The first operational meteorological satellite mission in an early morning orbit. Adv Atmos Sci, 2022, 39(1): 1-8. doi:  10.1007/s00376-021-1304-7
    [3]
    An D W, Gu S Y, Yang Z D, et al. Method for remove blocky ambiguity of non-gas cyclone field on sea surface of scatterometer. J Appl Meteor Sci, 2012, 23(4): 485-492. doi:  10.3969/j.issn.1001-7313.2012.04.012
    [4]
    Shao W Z, Nunziata F, Zhang Y G, et al. Wind speed retrieval from the Gaofen-3 synthetic aperture radar for VV- and HH-polarization using a re-tuned algorithm. Eur J Remote Sens, 2021, 54(1): 318-337. doi:  10.1080/22797254.2021.1924082
    [5]
    Chen K R, Zhou Y, Li S S, et al. Exploiting frequency-domain information of GNSS reflectometry for sea surface wind speed retrieval. IEEE Trans Geosci Remote Sens, 2023, 61. DOI:  10.1109/TGRS.2023.3284849.
    [6]
    Zhao D L, Toba Y. A spectral approach for determining altimeter wind speed model functions. J Oceanogr, 2003, 59(2): 235-244. doi:  10.1023/A:1025599423405
    [7]
    Amani M, Moghimi A, Mirmazloumi S M, et al. Ocean remote sensing techniques and applications: A review(part Ⅰ). Water, 2022, 14(21): 3400. doi:  10.3390/w14213400
    [8]
    Xu J M, Zhang Q S. Status review on atmospheric motion vectors-derivation and application. J Appl Meteor Sci, 2006, 17(5): 574-582. doi:  10.3969/j.issn.1001-7313.2006.05.007
    [9]
    Kishtawal C M, Deb S K, Pal P K, et al. Estimation of atmospheric motion vectors from Kalpana-1 imagers. J Appl Meteor Climatol, 2009, 48(11): 2410-2421. doi:  10.1175/2009JAMC2159.1
    [10]
    Lazzara M A, Dworak R, Santek D A, et al. High-latitude atmospheric motion vectors from composite satellite data. J Appl Meteor Climatol, 2014, 53(2): 534-547. doi:  10.1175/JAMC-D-13-0160.1
    [11]
    Bormann N, Salonen K, Peubey C, et al. An Overview of the Status of the Operational Assimilation of AMVs at ECMWF//Proceedings of the 11th International Winds Workshop, Auckland, New Zealand. 2012: 20-24.
    [12]
    Li H H, Wang M, Xue J S, et al. Application of FY-2C cloud trail wind data in mesoscale numerical model. Acta Meteor Sinica, 2008, 66(1): 50-58. doi:  10.3321/j.issn:0577-6619.2008.01.005
    [13]
    Li H R, Ding W Y, Xue J S, et al. A study on the application of FY-2E cloud drift wind height reassignment in numerical forecast of Typhoon Chanthu(1003) track. J Trop Meteor, 2015, 21(1): 34-42.
    [14]
    Xue C B, Gong J D, He C F, et al. Quality control of cloud derived wind vectors from geostationary meteorological satellites with its application to data assimilation system. J Appl Meteor Sci, 2013, 24(3): 356-364. doi:  10.3969/j.issn.1001-7313.2013.03.012
    [15]
    Bedka K M, Mecikalski J R. Application of satellite-derived atmospheric motion vectors for estimating mesoscale flows. J Appl Meteor, 2005, 44(11): 1761-1772. doi:  10.1175/JAM2264.1
    [16]
    Dunion J P, Velden C S. Application of surface-adjusted GOES low-level cloud-drift winds in the environment of Atlantic tropical cyclones. Part Ⅰ: Methodology and validation. Mon Wea Rev, 2002, 130(5): 1333-1346. doi:  10.1175/1520-0493(2002)130<1333:AOSAGL>2.0.CO;2
    [17]
    Nonaka K, Shimoji K, Kato K. Estimation of the Sea Surface Wind in the Vicinity of Typhoon Using Himawari-8 Low-level AMVs. 13th International Winds Workshop, 2016.
    [18]
    Zhou M Z, Xu J. Covariation relationship between tropical cyclone intensity and size change over the Northwest Pacific. J Appl Meteor Sci, 2023, 34(4): 463-474. doi:  10.11898/1001-7313.20230407
    [19]
    Kong L S, Zhang X Z. Sensitive experiments on reconstruction model of historical typhoon wind field in the Northwest Pacific Ocean. J Appl Meteor Sci, 2022, 33(1): 56-68. doi:  10.11898/1001-7313.20220105
    [20]
    Franklin J L, Black M L, Valde K. GPS dropwindsonde wind profiles in hurricanes and their operational implications. Wea Forecasting, 2003, 18(1): 32-44. doi:  10.1175/1520-0434(2003)018<0032:GDWPIH>2.0.CO;2
    [21]
    Bessho K, Date K J, Hayashi M, et al. An introduction to Himawari-8/9-Japan's new-generation geostationary meteorological satellites. J Meteor Soc Jpn, 2016, 94(2): 151-183. doi:  10.2151/jmsj.2016-009
    [22]
    Song J, Xu H Y, Gao S H, et al. Lightweight fully convolutional neural network based remote sensing image building extraction model. Remote Sens Technol Appl, 2023, 38(1): 190-199. https://www.cnki.com.cn/Article/CJFDTOTAL-YGJS202301018.htm
    [23]
    Wang G J, Hu Y F, Zhang S, et al. Water identification from the GF-1 satellite image based on the deep convolutional neural networks. Nat Remote Sens Bull, 2022, 26(11): 2304-2316. https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB202211014.htm
    [24]
    Zhang L, Wu L, Li F, et al. Indentification of weather radar abnormal data based on deep learning. J Appl Meteor Sci, 2023, 34(6): 694-705. doi:  10.11898/1001-7313.20230605
    [25]
    Yang J Y, Guo J H, Yue H J, et al. CDnet: CNN-based cloud detection for remote sensing imagery. IEEE Trans Geosci Remote Sens, 2019, 57(8): 6195-6211. doi:  10.1109/TGRS.2019.2904868
    [26]
    Mi Q C, Gao X N, Li Y, et al. Application of deep learning method to drought prediction. J Appl Meteor Sci, 2022, 33(1): 104-114. doi:  10.11898/1001-7313.20220109
    [27]
    Hu Y Y, Pang L, Wang Q G. Application of deep learning bias correction method to temperature grid forecast of 7-15 days. J Appl Meteor Sci, 2023, 34(4): 426-437. doi:  10.11898/1001-7313.20230404
    [28]
    Pradhan R, Aygun R S, Maskey M, et al. Tropical cyclone intensity estimation using a deep convolutional neural network. IEEE Trans Image Process, 2018, 27(2): 692-702. doi:  10.1109/TIP.2017.2766358
    [29]
    Du Y, Xu Q, Cheng Y C, et al. Estimating Sea Surface Currents Based on Himawari-8 Sea Surface Temperature Data//2021 Photonics & Electromagnetics Research Symposium(PIERS). IEEE, 2021: 2034-2038.
    [30]
    Shi X J, Duan B H, Ren K J. F2F-NN: A field-to-field wind speed retrieval method of microwave radiometer data based on deep learning. Remote Sens, 2022, 14(15). DOI:  10.3390/rs14153517.
    [31]
    Lean K, Bormann N. Moving to GOES-16: A New Generation of GOES AMVs. EUMETSAT/ECMWF Fellowship Programme Research Report, 2019.
    [32]
    Daniels J, Bresky W, Bailey A, et al. Enterprise Algorithm Theoretical Basis Document for Derived Motion Winds. NOAA Nesidis Center for Satellite Applications and Research, 2022.
    [33]
    Wang H, Zhu J H, Lin M S, et al. Evaluating Chinese HY-2B HSCAT ocean wind products using buoys and other scatterometers. IEEE Geosci Remote Sens Lett, 2020, 17(6): 923-927.
    [34]
    Li G P. New Dynamic Meteorology(2nd ed). Beijing: China Meteorological Press, 2014.
    [35]
    Rumelhart D E, Hinton G E, Williams R J. Learning representations by back-propagating errors. Nature, 1986, 323(6088): 533-536.
    [36]
    Mears C, Lee T, Ricciardulli L, et al. Improving the accuracy of the cross-calibrated multi-platform(CCMP) ocean vector winds. Remote Sens, 2022, 14(17). DOI:  10.3390/rs14174230.
    [37]
    Guo X X, Qu J H, Ye L M, et al. FY-4A/AGRI cloud detection method based on naive Bayesian algorithm. J Appl Meteor Sci, 2023, 34(3): 282-294. doi:  10.11898/1001-7313.20230303
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中
  • -->

Catalog

    Figures(8)  / Tables(2)

    Get Citation
    PDF
    XML
    Article views (463) PDF downloads(207) Cited by()
    • Received : 2023-11-14
    • Accepted : 2024-01-18
    • Published : 2024-03-27
    Journal Online
    Contact

    © 2020 Journal of Applied Meteorological Science   京ICP备15006967号-1

    Supported by Beijing Renhe Information Technology Co. Ltd

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return