全球志愿观测船数据的适用性分析-海洋预报

全球志愿观测船数据的适用性分析

单位：1. 中国气象局气象探测中心, 北京 100081;
2. 宁波市气象服务中心, 浙江宁波 315000

分类号：P71

出版年·卷·期（页码）：2025·42·第二期（76-88）

摘要：

基于2022年全球通讯系统（GTS）共享的志愿观测船观测数据，与欧洲中期天气预报中心的ERA5数据开展比较研究。采用定量统计指标（相关系数CC、相对偏差RB、均方根误差RMSE、平均误差ME、平均绝对误差MAE），重点分析海洋气象基本气候变量观测船舶（ECVs船舶）获得的气温、气压、相对湿度、风速等要素的有效观测数据与ERA5数据在不同时间、不同区域的一致性，并探究船舶观测的气象数据在全球的适用性。结果表明：ECVs船舶数据总体可反映各个要素的变化趋势，其中气温数据与ERA5数据的一致性最高，CC和RB分别为0.99和1.59%；其次为气压数据，CC下降至0.90，ME为-2.42 hPa；再次为相对湿度数据，RMSE和MAE分别为8.16%和5.89%；一致性最差的是风速数据，CC和RB分别为0.74和22.85%。ECVs船舶在不同季节和月份观测的数据质量存在差异，其中气温数据与ERA5数据的一致性最高且最稳定。针对不同海域的ECVs船舶数据的分析表明，西太平洋、印度洋、北大西洋海域船舶的数据质量各有优劣。气温和相对湿度的评价指标具有较明显的日变化特征，但气压和风速评价指标的日变化不明显。

In this paper, a comparative study between the ERA5 data and the Voluntary Observing Ships (VOS) data shared by the Global Telecommunication System (GTS) in 2022 is conducted. Using quantitative statistical metrics (correlation coefficient-CC, relative bias-RB, root mean squared error-RMSE, mean error-ME, mean absolute error-MAE), the data consistency and applicability of air temperature, pressure, relative humidity and wind speed observed by the VOS are assessed from aspects of periods and regions. The VOS that could observe simultaneously Essential Climate Variables (air temperature, water vapor, surface pressure, wind speed and direction) are defined as ECVs ships in the paper. The results show that the ECVs ships data have reasonable changing trends generally, among which the air temperature data has the best statistical metrics with CC and RB being 0.99 and 1.59%, respectively. The second best data is air pressure, with CC dropping to 0.90 and ME being -2.42 hPa. The third best data is relative humidity, with RMSE and MAE being 8.16% and 5.89%, respectively. The wind speed data is not good, with CC and RB being 0.74 and 22.85%, respectively. The quality of the ECVs ships data varies in different seasons and months, the consistency of air temperature between the ECVs ships and ERA5 data is the best and most stable. The analysis of the ECVs ships data in different ocean areas shows that the statistical metrics in Western Pacific, Indian Ocean and North Atlantic have their own advantages and disadvantages respectively. The statistical metrics of air temperature and relative humidity have significant diurnal variation, while those of air pressure and wind speed have not.

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