基于探空的FY-4B/GIIRS温湿廓线检验和订正

Validation and Correction of FY-4B/GIIRS Temperature and Humidity Profiles Based on Radiosonde Data

  • 摘要: 以探空资料为基准, 对2023年2月—2024年1月风云四号气象卫星B星(FY-4B)干涉式大气垂直探测仪(GIIRS)温湿廓线产品开展检验评估, 分析误差特征, 并利用概率密度匹配法(PDF方法)对云天温度廓线进行订正。结果表明: 晴天条件下温度平均偏差为-0.3~1 K, 均方根误差在2 K以内; 湿度平均偏差为0~1.3 g·kg-1, 均方根误差最大值位于近地层, 约为2.1 g·kg-1。云天条件下偏差增大, 平均偏差整体呈正值, 温度均方根误差为2.2~2.7 K, 湿度均方根误差最大值约为3 g·kg-1。12:00(世界时, 下同)近地层温度偏差较00:00有所增大; 晴天条件下, 12:00 400 hPa以下的湿度偏差大于00:00;云天条件下, 00:00 750~950 hPa的湿度偏差大于12:00。云天条件下温湿廓线系统性偏差明显, 与质控码为0的样本相比, 质控码为1的样本偏冷、偏干加剧, 且偏差分布更为离散, 温度偏差呈不对称的双峰分布。PDF方法可有效减小FY-4B/GIIRS温度廓线的系统性偏差, 订正后, 质控码为0和1的样本平均偏差分别由0.74 K和2.07 K下降至-0.03 K和0.01 K, 均方根误差分别由1.89 K和3.20 K减小至1.73 K和2.34 K。

     

    Abstract: In order to promote applications of FY-4B satellite data, temperature and humidity profile products of FY-4B geostationary interferometric infrared sounder (GIIRS) are verified and evaluated from February 2023 to January 2024 based on radiosonde data. Deviation characteristics are compared and analyzed under different conditions. In addition, the probability density function (PDF) matching method is employed to correct systematic errors in FY-4B/GIIRS temperature profile under cloudy condition. Results indicate that the quality of FY-4B/GIIRS temperature and humidity profiles is significantly influenced by cloud activity, leading to a notable reduction in the proportion of high-quality data when affected by the cloud. Under clear sky condition, the mean bias (MB) of temperature profiles ranges from -0.3 K to 1 K, the root mean square error (RMSE) is within 2 K, and the minimum error is approximately 1.1 K near 400 hPa height. The MB of humidity profiles ranges from 0 to 1.3 g·kg-1, and the maximum RMSE is about 2.1 g·kg-1 at the surface layer. Temperature and humidity profile errors increase under cloudy condition, while the bias of entire atmospheric layer is predominantly positive. The RMSE of temperature ranges from 2.2 K to 2.7 K, while the maximum RMSE for humidity is approximately 3 g·kg-1. The trend of errors is consistently similar at 0000 UTC and 1200 UTC. Compared with 0000 UTC, the deviation of temperature profiles at the surface layer at 1200 UTC is larger and slightly more distinct. The humidity error at 1200 UTC is greater than that at 0000 UTC at the layer below 400 hPa under clear sky condition, while the humidity error at 0000 UTC is greater than that at 1200 UTC at layer between 750 hPa and 950 hPa under cloudy condition. Significant systematic errors exist in temperature and humidity profiles under cloudy condition. Samples with quality control of 1 tend to be colder and drier compared to those with quality control of 0. The deviation distribution is more discrete, while the deviation of temperature follows an asymmetric bimodal distribution. After correction using the PDF method, systematic errors of FY-4B/GIIRS temperature profiles are effectively reduced. MBs of samples with quality control of 0 and 1 decrease from 0.74 K and 2.07 K to 0.03 K and 0.01 K, and RMSEs decrease from 1.89 K and 3.20 K to 1.73 K and 2.34 K, respectively. When the deviation is generally unbiased, the effectiveness of PDF methods is limited.

     

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