设为首页
加入收藏
A Method to Estimate Sea Surface Wind Vectors Using Geostationary Satellites
-
摘要: 参考大气动力学理论中风随高度、纬度的分布特征, 提出一种基于静止卫星低层大气导风利用全连接神经网络估测海面风的新思路, 构建基于卫星遥感数据的全连接神经网络海面风矢量估测模型, 实现基于大气导风的海面风估测。基于GOES-16先进基线成像仪可见光通道0.5 km分辨率大气导风开展试验, 并与2021年1月1日—12月31日北美近海岸和海上93个美国国家数据浮标中心浮标数据比对, 结果表明:全连接神经网络估算得到基于大气导风的海面风风速均方根误差不大于1.5 m·s-1, 较传统模型降低0.24 m·s-1。将模型应用于飓风场景, 通过与2022年3个北大西洋飓风和3个东太平洋飓风共13个时次的再分析数据比对表明:基于大气导风的海面风风速均方根误差不大于1.1 m·s-1, 相较于传统经验模型降低0.04 m·s-1, 在低风速区无系统性偏差。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.
-
图 1 AMV及其产品与ERA5风速对比
(填色表示0.5 m·s-1范围内点的数量)
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)
图 2 单一物理量WA与ERA5风速对比
(填色表示0.5 m·s-1范围内点的数量)
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)
图 3 AMV及WA与ERA5风向对比
(填色表示12°范围内点的数量)
Fig. 3 Comparison of AMV and WA with ERA5 in wind direction
(the shaded denotes the number of points in the range of 12°)
图 4 AMV及其产品与NDBC浮标风速对比
(填色表示0.5 m·s-1范围内点的数量)
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)
图 5 AMV及WA与NDBC浮标风向对比
(填色表示12°范围内点的数量)
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°)
图 6 2022年9月22日18：00飓风菲奥娜附近的WA与GOSE-16可见光通道AMV和ERA5对比 (a)GOES-16可见光通道AMV风羽与GOSE-16可见光云图，(b)WA风羽与GOES-16可见光云图，(c)ERA5风羽与GOES-16可见光云图
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
图 7 飓风区AMV及其产品与ERA5风速对比
(填色表示0.5 m·s-1范围内点的数量)
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)
图 8 飓风区AMV及WA与ERA5风向对比
(填色表示12°范围内点的数量)
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°)
表 1 2021年GOSE-16可见光通道AMV高度分布及不同高度AMV与ERA5 10 m平均风向和风速误差
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 
下载: 导出CSV
表 2 各时刻飓风信息
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
下载: 导出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] 安大伟, 谷松岩, 杨忠东, 等. 散射计海面非气旋风场块状模糊去除方法. 应用气象学报, 2012, 23(4): 485-492. doi: 10.3969/j.issn.1001-7313.2012.04.012An 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] 许健民, 张其松. 卫星风推导和应用综述. 应用气象学报, 2006, 17(5): 574-582. doi: 10.3969/j.issn.1001-7313.2006.05.007Xu 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] 李华宏, 王曼, 薛纪善, 等. FY-2C云迹风资料在中尺度数值模式中的应用研究. 气象学报, 2008, 66(1): 50-58. doi: 10.3321/j.issn:0577-6619.2008.01.005Li 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] 薛谌彬, 龚建东, 何财福, 等. 静止卫星云导风的质量控制及在同化中的应用. 应用气象学报, 2013, 24(3): 356-364. doi: 10.3969/j.issn.1001-7313.2013.03.012Xue 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] 周明珠, 徐晶. 西北太平洋热带气旋强度和尺度协同变化特征. 应用气象学报, 2023, 34(4): 463-474. doi: 10.11898/1001-7313.20230407Zhou 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] 孔莉莎, 张秀芝. 西北太平洋历史台风风场重建模型参数试验. 应用气象学报, 2022, 33(1): 56-68. doi: 10.11898/1001-7313.20220105Kong 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] 宋佳, 徐慧窈, 高少华, 等. 轻量化卷积神经网络遥感影像建筑物提取模型. 遥感技术与应用, 2023, 38(1): 190-199. https://www.cnki.com.cn/Article/CJFDTOTAL-YGJS202301018.htmSong 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] 王国杰, 胡一凡, 张森, 等. 深度卷积神经网络的遥感影像水体识别. 遥感学报, 2022, 26(11): 2304-2316. https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB202211014.htmWang 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] 张林, 吴蕾, 李峰, 等. 基于深度学习的天气雷达异常数据识别技术. 应用气象学报, 2023, 34(6): 694-705. doi: 10.11898/1001-7313.20230605Zhang 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] 米前川, 高西宁, 李玥, 等. 深度学习方法在干旱预测中的应用. 应用气象学报, 2022, 33(1): 104-114. doi: 10.11898/1001-7313.20220109Mi 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] 胡莹莹, 庞林, 王启光. 基于深度学习的7~15 d温度格点预报偏差订正. 应用气象学报, 2023, 34(4): 426-437. doi: 10.11898/1001-7313.20230404Hu 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] 李国平. 新编动力气象学(第2版). 北京: 气象出版社, 2014.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] 郭雪星, 瞿建华, 叶凌梦, 等. 基于朴素贝叶斯的FY-4A/AGRI云检测方法. 应用气象学报, 2023, 34(3): 282-294. doi: 10.11898/1001-7313.20230303Guo 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 -
点击查看大图
计量
- 摘要浏览量: 245
- HTML全文浏览量: 91
- PDF下载量: 156
- 被引次数: 0
