基于双频降雨雷达数据的海面阵风计算方法研究

池淏; 张有广; 林静

doi:10.12284/hyxb2025067

基于双频降雨雷达数据的海面阵风计算方法研究

doi: 10.12284/hyxb2025067

池淏^1,,
张有广^2, ,,
林静¹

1.
卫星海洋环境监测预警全国重点实验室，国家海洋环境预报中心，北京 100081
2.
卫星海洋环境监测预警全国重点实验室，国家卫星海洋应用中心，北京 100081

基金项目: 国家重点研发计划新型海洋观测资料同化技术支撑项目(2021YFC3101502)。

详细信息

作者简介:
池淏（2000—），男，甘肃省天水市人，主要从事海面风场研究。E-mail：2313400008@hhu.edu.cn

通讯作者:
张有广(1971—)，男，研究员，主要从事海洋微波遥感研究。E-mail: zhangyouguang@mail.nsoas.org.cn

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出版历程
- 收稿日期: 2025-01-22
- 修回日期: 2025-05-09
- 网络出版日期: 2025-06-03

Research of sea surface gust calculation method based on dual-frequency precipitation radar data

Chi Hao^1
,,
Zhang Youguang^{2
, ,},
Lin Jing¹

1.
State Key Laboratory of Satellite Ocean Environment Prediction, National Marine Environmental Forecasting Center, Beijing 100081, China
2.
State Key Laboratory of Satellite Ocean Environment Prediction, National Satellite Ocean Application Service, Beijing 100081, China

摘要

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。林静等基于HY-2B雷达高度计C、Ku波段后向散射系数的差值对海面风速进行修正，计算得到星下点的阵风风速，但观测范围较小。本文在此基础上，选用观测原理与雷达高度计相似的全球降雨观测计划（GPM）搭载的双频降雨雷达（DPR），利用其Ku、Ka波段后向散射系数的差值，并以ERA5海面风速作为观测值，对海面风速进行修正计算，得到阵风风速，以扩展观测范围并提高观测效率。结果表明：计算得到的阵风风速与ERA5阵风风速对比，相关系数（R）为0.96，均方根误差（RMSE）为1.79 m/s，平均偏差（Bias）为0.73 m/s，标准差（Std）为1.64 m/s；与同时期的NDBC浮标数据对比，R为0.91，RMSE为1.50 m/s，Bias为−0.15 m/s，Std为1.50 m/s，说明降雨雷达反演的阵风可靠性较好。进一步使用NDBC浮标海面风速代替ERA5海面风速后，R提高至0.95，RMSE降低至1.10 m/s，Bias为−0.07 m/s，Std为1.50 m/s，反演结果得到提高，说明精确的海面风速对阵风计算结果有较好的影响。
- 双频降雨雷达 /
- 海面阵风 /
- 反演算法 /
- 后向散射系数
Abstract: Sea surface gusts play a critical role in the utilization of ocean resources, marine research, and the safety of maritime transportation and offshore construction. However, current observation methods are limited, resulting in significant data gaps in surface gust measurements. Lin Jing et al. corrected sea surface wind speeds based on the difference in backscattering coefficients between the C and Ku bands observed by the HY-2B radar altimeter, thereby deriving gust wind speeds at nadir points, although the spatial coverage remained relatively limited. Building upon this approach, the present study employs the Dual-frequency Precipitation Radar (DPR) aboard the Global Precipitation Measurement (GPM) mission, whose observational principles are similar to those of radar altimeters. By utilizing the difference in Ku- and Ka-band backscattering coefficients and using ERA5 sea surface wind speed as a reference, surface wind speeds are corrected to retrieve gust speeds, aiming to expand observation coverage and improve observational efficiency. Validation against ERA5 gust data yields a correlation coefficient (r) of 0.96, a root mean square error (RMSE) of 1.79 m/s, a mean bias (Bias) of 0.73 m/s, and a standard deviation (Std) of 1.64 m/s. Comparison with simultaneous NDBC buoy observations shows an r of 0.91, an RMSE of 1.50 m/s, a Bias of −0.15 m/s, and a Std of 1.50 m/s, indicating that gust wind speeds retrieved from DPR data demonstrate good reliability. Furthermore, by replacing ERA5 sea surface wind speeds with NDBC buoy measurements, the r increases to 0.95, the RMSE decreases to 1.10 m/s, the Bias is −0.07 m/s, and the Std remains at 1.50 m/s, further improving the retrieval results. These findings highlight that accurate sea surface wind speeds have a significant positive impact on the accuracy of gust wind retrievals.
- dual-frequency precipitation radar /
- sea surface gusts /
- inversion method /
- backscattering coefficient

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图 1 Ku波段后向散射系数示意图

Fig. 1 Schematic diagram of Ku band backscattering coefficient

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图 2 Ka波段后向散射系数示意图

Fig. 2 Schematic diagram of Ka band backscattering coefficient

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图 3 NDBC浮标位置示意图

Fig. 3 Schematic diagram of the position of the NDBC buoy

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图 4 2020年6月1日海面风速示意图

Fig. 4 Schematic diagram of sea surface wind speed on June 1, 2020

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图 5 2020年6月1日海面阵风示意图

Fig. 5 Schematic diagram of sea gusts speed on June 1, 2020

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图 6 反演阵风结果与NDBC浮标阵风数据散点分析

Fig. 6 Scatter analysis of retrieved gust results and NDBC buoy gust data

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图 7 替换海面风速后的阵风反演结果与NDBC浮标阵风数据散点分析

Fig. 7 Scatter analysis of gust retrieval results after replacing sea surface wind speed with NDBC buoy data

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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 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
NDBC 浮标站点	纬度	经度	距离/ km	匹配对数量	R	RMSE/ (m·s⁻¹)	R	RMSE/ (m·s⁻¹)
41002	31.76°N	74.94°W	6.15	2155	0.81	1.94	0.81	2.19
41040	14.54°N	53.14°W	12.92	2197	0.82	1.34	0.81	1.51
41044	21.58°N	58.63°W	15.39	2146	0.73	1.74	0.76	1.82
41048	31.83°N	69.57°W	11.33	2134	0.90	1.07	0.87	1.46
42001	25.93°N	89.66°W	12.04	2052	0.91	1.30	0.90	1.59
44011	41.09°N	66.56°W	11.57	1200	0.69	2.28	0.73	2.49
46005	46.14°N	131.09°W	13.77	2150	0.75	2.15	0.78	2.26
46066	52.77°N	155.01°W	1.78	2060	0.82	1.96	0.85	2.16
46080	57.91°N	150.13°W	12.32	1979	0.85	1.84	0.86	2.10
51001	24.48°N	162.03°W	5.05	2153	0.74	1.66	0.78	1.68

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表 2 2020年6−8月阵风风速反演结果表

Tab. 2 Gust wind speed retrieval results from June to August 2020

时间	匹配对数量	R	RMSE/ (m·s⁻¹)	Bias/ (m·s⁻¹)	Std/ (m·s⁻¹)
2020年6月	1933334	0.96	1.79	0.73	1.64
2020年7月	2071620	0.96	1.77	0.70	1.63
2020年8月	1529099	0.96	1.81	0.76	1.64
2020年6月至2020年8月	5534053	0.96	1.79	0.73	1.64

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