A method for calculating ocean surface gusts using GPM satellite data
-
摘要: 海面阵风对海洋资源利用、海洋研究以及海上运输与工程安全具有重要意义。但目前观测手段有限,海面阵风数据缺失严重。本文利用全球降水观测计划(GPM)搭载的双频降雨雷达(DPR)和微波成像仪(GMI),使用微波成像仪的亮度温度信息对Ku波段后向散射系数进行修正,并结合ERA5海面风速反演海面阵风,以此来提高海面阵风观测效率。结果表明:反演得到的阵风风速与ERA5阵风风速的相关系数(R)为0.93,均方根误差(RMSE)为1.81 m/s;与NDBC浮标数据相比,R为0.78,RMSE为1.88 m/s。国内HY-2B卫星基于该方法得到的阵风结果与NDBC浮标数据相比,R为0.90,RMSE为1.84 m/s。将二者的海面风速均使用NDBC浮标海面风速作为参考,二者的反演结果均得到明显提高,表明精确的海面风速对阵风反演结果具有较好影响。同时,由于GPM卫星主被动观测频段相比于HY-2B卫星更接近,得到的阵风精度也优于HY-2B卫星反演的阵风精度。
-
关键词:
- 全球降水测量计划(GPM) /
- 海面阵风 /
- 反演算法 /
- 后向散射系数 /
- 亮度温度
Abstract: Ocean surface gusts are of great significance for marine resource utilization, ocean research, and the safety of maritime transportation and engineering. However, observational methods are limited, and surface gust data remain scarce. In this study, we employ the Dual-frequency Precipitation Radar (DPR) and the GPM Microwave Imager (GMI) onboard the Global Precipitation Measurement (GPM) satellite. Brightness temperatures from GMI are used to correct Ku-band backscattering coefficients, which are then combined with ERA5 surface wind speeds to retrieve sea surface gusts, thereby enhancing gust retrieval capability. The results show that the retrieved gusts achieve a correlation coefficient (R) of 0.93 and a root mean square error (RMSE) of 1.81 m/s compared with ERA5 gusts, and R = 0.78 with RMSE = 1.88 m/s against NDBC buoy data. Retrievals from the HY-2B satellite using the same method yield R = 0.90 and RMSE = 1.84 m/s against buoy observations. Replacing ERA5 wind speeds with buoy measurements as reference further improves the retrieval accuracy of both GPM and HY-2B, highlighting the importance of accurate surface wind input. Moreover, due to its active–passive observation frequencies being more consistent with buoy observations, the GPM satellite achieves higher gust retrieval accuracy than HY-2B. -
表 1 ERA5与NDBC浮标匹配分析结果和浮标位置信息
Tab. 1 Matching analysis results of ERA5 and NDBC buoy data and specific information of buoys
NDBC 浮标站点 纬度 经度 距离/km 匹配对数量 ERA5与浮标海面风速 ERA5与浮标阵风风速 R RMSE/(m·s−1) R RMSE/(m·s−1) 41040 14.54°N 53.14°W 12.92 2186 0.89 1.01 0.85 1.33 41049 27.51°N 62.27°W 2.15 2154 0.83 1.13 0.75 1.59 44008 40.50°N 69.25°W 0.34 2190 0.83 1.49 0.80 2.28 46001 56.30°N 148.03°W 5.39 2133 0.95 1.04 0.94 1.32 46013 38.24°N 123.32°W 6.10 2145 0.88 2.58 0.90 2.54 46066 52.76°N 155.02°W 3.01 2069 0.95 0.88 0.94 1.41 51000 23.53°N 153.79°W 5.29 2173 0.85 0.99 0.82 1.36 51004 17.54°N 152.23°W 4.71 2149 0.83 0.86 0.80 1.21 表 2 2020年7月至12月阵风风速反演结果
Tab. 2 Gust wind speed retrieval results from July to December 2020
时间 匹配对数量 R RMSE/(m·s−1) Bias/(m·s−1) Std/(m·s−1) 2020年7月 6075650 0.93 1.85 0.51 1.78 2020年8月 4410339 0.92 1.93 0.56 1.85 2020年9月 5915724 0.92 1.90 0.32 1.87 2020年10月 3476700 0.94 1.77 0.47 1.70 2020年11月 3406292 0.92 1.72 0.47 1.65 2020年12月 3499744 0.95 1.63 0.57 1.52 2020年7月至12月合计 26789989 0.93 1.81 0.37 1.78 表 3 ERA5数据和阵风反演数据与NDBC浮标数据对比
Tab. 3 Comparison of ERA5 data and gust retrieval data with NDBC buoy observations
R RMSE/(m·s−1) Bias/(m·s−1) Std/(m·s−1) 浮标海面风速与ERA5海面风速 0.71 2.02 −1.23 1.60 浮标阵风与ERA5阵风 0.64 2.25 0.32 2.22 浮标阵风与GPM反演阵风 0.78 1.88 −0.93 1.63 浮标阵风与替换海面风速的GPM反演阵风 0.97 0.68 0.30 0.61 -
[1] 大气科学名词审定委员会. 大气科学名词[M]. 3版. 北京: 科学出版社, 2009.Approval Committee of Atmospheric Science Terms. Chinese Terms in Atmospheric Science[M]. 3rd ed. Beijing: Science Press, 2009. [2] Sheridan P. Current gust forecasting techniques, developments and challenges[J]. Advances in Science and Research, 2018, 15: 159−172. doi: 10.5194/asr-15-159-2018 [3] Wang Ke, Lyu Xinyu, Huang Jing, et al. Influence of topography and the underlying surface of the Bohai Sea on wind and gust forecasts[J]. Earth and Space Science, 2023, 10(1): e2022EA002705. doi: 10.1029/2022EA002705 [4] Brasseur O. Development and application of a physical approach to estimating wind gusts[J]. Monthly Weather Review, 2001, 129(1): 5−25. doi: 10.1175/1520-0493(2001)129<0005:DAAOAP>2.0.CO;2 [5] Blaes J L, Glenn D, Hawkins D, et al. Developing a dataset of wind gust factors to improve forecasts of wind gusts in tropical cyclones[C]//39th National Weather Association Annual Meeting. Salt Lake City: National Weather Association, 2014: 43. [6] 周福, 蒋璐璐, 涂小萍, 等. 浙江省几种灾害性大风近地面阵风系数特征[J]. 应用气象学报, 2017, 28(1): 119−128. doi: 10.11898/1001-7313.20170111Zhou Fu, Jiang Lulu, Tu Xiaoping, et al. Near-surface gust factor characteristics in several disastrous winds over Zhejiang Province[J]. Journal of Applied Meteorological Science, 2017, 28(1): 119−128. doi: 10.11898/1001-7313.20170111 [7] 胡波. 浙江沿海台风阵风系数的影响因子分析[J]. 热带气象学报, 2017, 33(6): 841−849, doi: 10.16032/j.issn.1004-4965.2017.06.005Hu Bo. Analysis of gust factor associated with typhoons on Zhejiang coast[J]. Journal of Tropical Meteorology, 2017, 33(6): 841−849, doi: 10.16032/j.issn.1004-4965.2017.06.005 [8] Jung C, Schindler D. Modelling monthly near-surface maximum daily gust speed distributions in Southwest Germany[J]. International Journal of Climatology, 2016, 36(12): 4058−4070. doi: 10.1002/joc.4617 [9] 陈戈. 卫星高度计反演海面风速——模式函数与应用实例[J]. 遥感学报, 1999, 3(4): 305−311.Chen Ge. On retrieving sea surface wind speed from satellite altimeters: model functions and an application case[J]. Journal of Remote Sensing, 1999, 3(4): 305−311. [10] Gourrion J, Vandemark D, Bailey S, et al. Satellite altimeter models for surface wind speed developed using ocean satellite crossovers[R]. French Research Institute for Exploitation of the Sea, 2000. [11] 张有广, 蒋城飞, 贾永君, 等. HY-2B卫星载荷联合观测海面阵风的一种反演方法[J]. 海洋学报, 2022, 44(11): 133−143.Zhang Youguang, Jiang Chengfei, Jia Yongjun, et al. An inversion method for joint observation of wind gusts by HY-2B satellite remote sensors[J]. Haiyang Xuebao, 2022, 44(11): 133−143. [12] 林静, 张有广. 基于双频段雷达高度计数据的海面阵风反演研究[J]. 海洋学报, 2024, 46(4): 133−142. doi: 10.12284/hyxb2024039Lin Jing, Zhang Youguang. Research of sea surface gust inversion by dual band radar altimeter data[J]. Haiyang Xuebao, 2024, 46(4): 133−142. doi: 10.12284/hyxb2024039 [13] 张有广, 贾永君, 林明森, 等. 基于HY-2卫星数据的热带气旋风速和气压反演[J]. 遥感学报, 2024, 28(6): 1588−1601.Zhang Youguang, Jia Yongjun, Lin Mingsen, et al. A retrieval method of tropical cyclone wind speed and sea level pressure based on HY-2 satellite data[J]. National Remote Sensing Bulletin, 2024, 28(6): 1588−1601. [14] Skofronick-Jackson G, Petersen W A, Berg W, et al. The Global Precipitation Measurement (GPM) mission for science and society[J]. Bulletin of the American Meteorological Society, 2017, 98(8): 1679−1695. doi: 10.1175/BAMS-D-15-00306.1 [15] 余占猷. 利用DPR和GMI探测结果对东亚降水云的个例分析研究[D]. 合肥: 中国科学技术大学, 2016.Yu Zhanyou. Case study of precipitation clouds over the East Asia based on DPR and GMI measurements[D]. Hefei: University of Science and Technology of China, 2016. [16] 尹红刚, 吴琼, 谷松岩, 等. 风云三号(0)批降水测量卫星探测能力及应用[J]. 气象科技进展, 2016, 6(3): 55−61. doi: 10.3969/j.issn.2095-1973.2016.03.007Yin Honggang, Wu Qiong, Gu Songyan, et al. Analysis of rainfall measurement power in the FY-3(03) rain measurement satellite[J]. Advances in Meteorological Science and Technology, 2016, 6(3): 55−61. doi: 10.3969/j.issn.2095-1973.2016.03.007 [17] Panfilova M, Karaev V. Wind speed retrieval algorithm using Ku-band radar onboard GPM satellite[J]. Remote Sensing, 2021, 13(22): 4565. doi: 10.3390/rs13224565 [18] 王振占, 张德海, 赵谨, 等. HY-2A卫星大气校正微波辐射计在轨数据定标和检验研究[J]. 中国工程科学, 2013, 15(7): 44−52,61. doi: 10.3969/j.issn.1009-1742.2013.07.007Wang Zhenzhan, Zhang Dehai, Zhao Jin, et al. In-orbit calibration and validation of atmospheric correction microwave radiometer on HY-2A satellite[J]. Strategic Study of CAE, 2013, 15(7): 44−52,61. doi: 10.3969/j.issn.1009-1742.2013.07.007 [19] Hersbach H, Bell B, Berrisford P, et al. The ERA5 global reanalysis[J]. Quarterly Journal of the Royal Meteorological Society, 2020, 146(730): 1999−2049. doi: 10.1002/qj.3803 [20] 张淑静, 吕聪俐, 马敏. 国内外海上多功能浮标发展探讨[J]. 中国海事, 2019(9): 47−51, doi: 10.16831/j.cnki.issn1673-2278.2019.09.017Zhang Shujing, Lü Congli, Ma Min. Discussion on the development of domestic and overseas marine multifunction buoy[J]. China Maritime Safety, 2019(9): 47−51, doi: 10.16831/j.cnki.issn1673-2278.2019.09.017 [21] Hsu S A, Meindl E A, Gilhousen D B. Determining the power-law wind-profile exponent under near-neutral stability conditions at sea[J]. Journal of Applied Meteorology and Climatology, 1994, 33(6): 757−765. doi: 10.1175/1520-0450(1994)033<0757:DTPLWP>2.0.CO;2 [22] Campos R M, Gramcianinov C B, De Camargo R, et al. Assessment and calibration of ERA5 severe winds in the Atlantic Ocean using satellite data[J]. Remote Sensing, 2022, 14(19): 4918, doi: 10.3390/rs14194918 [23] Zabolotskikh E V, Mitnik L M, Chapron B. New approach for severe marine weather study using satellite passive microwave sensing[J]. Geophysical Research Letters, 2013, 40(13): 3347−3350. doi: 10.1002/grl.50664 [24] Hwang C, Kao E C, Parsons B. Global derivation of marine gravity anomalies from Seasat, Geosat, ERS-1 and TOPEX/POSEIDON altimeter data[J]. Geophysical Journal International, 1998, 134(2): 449−459. doi: 10.1111/j.1365-246X.1998.tb07139.x [25] Gower J F R. Intercalibration of wave and wind data from TOPEX/POSEIDON and moored buoys off the west coast of Canada[J]. Journal of Geophysical Research: Oceans, 1996, 101(C2): 3817−3829. doi: 10.1029/95JC03281 [26] 黎鹏. 星载波谱仪海面风场反演研究[D]. 武汉: 华中科技大学, 2019.Li Peng. The study on spaceborne spectrometer for sea surface wind field retrieval[D]. Wuhan: Huazhong University of Science & Technology, 2019. -
下载: