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

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.

Keywords

• radiosonde data
• temperature and humidity profiles
• verification and evaluation