## 1 Introduction  
On 3 August 2023, FY-3F was successfully launched as a polar-orbiting satellite. It will replace the FY-3C satellite with its local time crossing the equator at 10:20 in its descending node. FY-3F is now constellated with other FY-3 series satellites, as well as NOAA and MetOp satellites, is forming a powerful global observing system, providing global data coverage with a 4 h refresh rate for numerical weather prediction (NWP), and therefore significantly enhances both the accuracy of global NWP [1]. At the same time, they also improve the timeliness of tropical cyclone (TC) monitoring. In order to apply these satellites to TC monitoring, the prerequisite is to evaluate the quality of the observational data and describe the associated errors. For cross-track scanners, biases distributed with scan angle and striping noise are important evaluation factors. Previous studies have found that the MWHS carried by FY-3A and FY-3B exhibit scan-dependent biases [2, 3]. Additionally, channel 3 of the MWTS on FY-3B displays latitude-dependent biases, while channel 4 exhibits an asymmetric distribution with scanning [4]. Both the Microwave Humidity Sounder (MHS) on MetOp-B and the FY-3C MWHS have noise equivalent delta temperature (NEDT) present [5]. Satellite microwave remote sensing, due to its ability to penetrate clouds and rain, has also become an important means of observing TC. The temperature of a TC center is typically higher than its surrounding temperature, earning it the designation of \"warm core\". As one of the most notable characteristics of TCs, it can reflect the intensity of TCs [6, 7, 8]. The warm core structures have been retrieved from a series of satellite microwave sounding instruments using regression algorithms [7; 9] and physical retrieval [10; 11]. For example, due to the sensitivity of some channels to oxygen and water vapor in Advanced Microwave Sounding Unit-A (AMSU-A) and Advanced Technology Microwave Sounder (ATMS), they have been widely used to retrieve information about the atmosphere, including temperature distribution in the atmosphere, which can obtain TCs' warm core structure [9; 12]. The warm core structure generated by ATMS exhibits a close correlation with the maximum wind speed or minimum sea level pressure [7; 13]. Furthermore, [PERSON] and [PERSON] [14] improved upon the traditional AMSU-A temperature retrieval algorithm to obtain the warm core structure from ATMS. [PERSON] et al. [15] employed a multiple linear regression algorithm to obtain the TCS' warm core structure from MWTS onboard FY-3C. Similarly, [PERSON] et al. [16; 17] also utilized a multiple linear regression algorithm to obtain the warm core structure from MWTS onboard FY-3D and FY-3E. However, the retrieval based on regression algorithms is suitable for temperature retrieval and also derives a noticeable cold anomaly at lower levels due to the scattering effects from precipitating clouds. In addition, One-Dimension Variational (1 DVAR) also is a method that can be used to retrieve atmospheric temperature profiles. The National Oceanic and Atmospheric Administration (NOAA) developed the Microwave Integrated Retrieval System (MiRS) based on the 1 DVAR algorithm, which has been applied to AMSU/MHS since 2007 [18]. However, the accuracy of retrieving the warm core structure using MiRS has not been fully evaluated. [PERSON] and [PERSON] [10] found that the retrieval of the warm core using the 1 DVAR algorithm from channels 4-15 of ATMS is more realistic, while the high-frequency channels are affected by scattering and used for detecting precipitation. Recently, researchers at NOAA have released a version based on MiRS called MiRS-TC, especially for TC conditions [19]. Compared to MiRS, MiRS-TC has significantly improved the precision of retrieving temperature and humidity profiles in TC scenarios. Similarly, [PERSON] et al.