# Performance Evaluation and Noise Mitigation of the FY-3E Microwave Humidity Sounder
## 1 Introduction  
Fengyun-3E (FY-3E), which is China's first early-morning orbit satellite, was successfully launched from Jiuquan Satellite Launch Center in Gansu Province on 5 July 2021. This satellite is the fifth satellite in the Fengyun-3 series, which was designed for meteorological operations, marine monitoring, climate monitoring, atmospheric chemistry and space weather. FY-3E crosses the equator (descending node) between 5:30 and 5:50 local solar time, which fills in the gap of satellite observations within the 6-h assimilation window and provides atmospheric state profiles every 4 h to the global data assimilation system. FY-3E adopts a three-axis stabilized system with an orbital altitude of 836 km and an inclination of 98.75\({}^{\circ}\). The orbital period and the quasi-repeat time are 101.5 min and 5.5 days, respectively [1]. FY-3E is equipped with 11 devices, including the Microwave Humidity Sounder 2 (MWHS-2), which is able to detect the atmosphere under all weather conditions (except for precipitation) and provide global coverage of atmospheric temperature and humidity profiles. Therefore, MWHS-2 will play a key role in numerical weather predictions (NWPs), data assimilation and weather/climate monitoring [2; 3; 4; 5]. Satellite data assimilation is a crucial step that improves NWPs. The data assimilation method uses the observations provided by satellites to adjust the background field, with the assumption that both the observation data and the background field have only unbiased Gaussian errors [6]. To obtain the initial field of the NWP model that is closest to the truth, the differences between the observed and simulated brightness temperatures (\"O\(-\)B\") andthe unbiased errors of each are integrated to adjust the background field [7]). The error characteristics of the observation data are important references for data assimilation, before which the error estimation and bias correction must be performed on the observations. Moreover, data assimilation is largely influenced by the error and bias characteristics of satellite observations [8; 9]. Observation error (mainly including instrument observation error and data representative error) is generally used to determine the weights of the observation data in assimilation [10] and is an important factor in determining whether the observations can be effectively used and whether the analysis field can provide optimal estimation of the atmospheric state [11]. Many studies have focused on the characteristics of observation errors in specific situations [12; 13]. [PERSON] and [PERSON] [14] improved microwave data assimilation in plateau regions by adjusting the observation errors of different vegetation types. [PERSON] [15] also found that Landsat data utilization can be significantly improved by clarifying the error characteristics of each channel. Additionally, an effective bias correction scheme is very important to ensure unbiased observation error [16]. [PERSON] et al. [17] improved the soil moisture simulation in the Community Land Model Version 4 by assimilating the brightness temperature observed by the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and emphasized the effect of bias correction on the AMSR-E brightness temperature. [PERSON] et al. [18] proposed a bias correction scheme for the Cross-Track Infrared Sounder assimilation by considering the bias dependence on the latitude and scanning angles, which proved that this method has better performance than the traditional air mass bias correction scheme in the regional NWP model. In addition, many studies have focused on adaptive bias correction methods [6; 19]. [PERSON] et al. [20] developed a constrained variational bias correction scheme by adding a new constraint term to the variational cost function. This new method successfully constrains the drift during bias correction and provides a more stable assimilation system for long-term reanalysis data construction.