To address the lack of the assimilation capability for Fengyun satellite microwave imager observations in the CMA-RA v1.5 development system and to enhance the application value of domestic satellite observations, this study processes L1 observations from the Fengyun-3D (FY-3D) MicroWave Radiation Imager (MWRI), constructs an assimilation module based on the Gridpoint Statistical Interpolation (GSI) system, and performs a one-month batch experiment and evaluation using the Hybrid four-dimensional ensemble-variational (Hybrid-4DEnVar) assimilation method. The results show that the adopted quality control and bias correction schemes are reasonable and reliable, which can effectively screen high-quality clear-sky over-ocean observations and correct systematic biases. Assimilation of MWRI observations improves the quality of specific humidity analysis, i.e., the Root Mean Square Error (RMSE) of 800 hPa specific humidity analysis is reduced by up to 1.05% (passing the significance test), and the dry bias near 800 hPa in the tropics and the wet bias below 700 hPa in the southern Hemisphere are corrected. The 0—72 h specific humidity forecasts show significant improvements in the lower and middle troposphere (950—600 hPa), with particularly notable improvements occurring near 800 hPa during the first 24 h of forecast (the maximum error reduction reaches 8.74 mg/kg). The RMSEs of 3 h, 6 h, and 9 h forecasts are reduced by up to 1.4%, and the anomaly correlation coefficients of 700 hPa specific humidity forecasts are increased by 0.001—0.01. The forecasts of wind and temperature fields exhibit a neutral-to-positive effect, and the geopotential height forecast is improved at certain time steps of medium-range forecasts. The core contribution of FY-3D MWRI data assimilation focuses on the improvement of humidity analysis and forecasts, while its overall impact on other meteorological elements is relatively neutral.

Since the launch of China’s Fengyun-3 (FY-3) satellite series in 2008, the on-board Microwave Temperature Sounders (MWTS) have provided critical atmospheric sounding data for numerical weather prediction and extreme weather monitoring. However, their application in climate research remains limited. To establish long-term Fundamental Climate Data Records (FCDRs) for FY-3 satellites—the core foundation of climate research—a comprehensive assessment of data consistency between China’s FY-3 satellites and the U.S. NOAA satellites is essential. This study systematically evaluates the inter-satellite consistency between FY-3 MWTS observations and three datasets of NOAA FCDRs, focusing on comparison of brightness temperatures and their anomalies over the extended period of 2009–2024. In accordance with the Global Climate Observing System (GCOS) Essential Climate Variables (ECVs) requirements (GCOS-245), the accuracy and stability of both operational and recalibrated FY-3 brightness temperatures are quantified on a global grid scale, using multiple statistical metrics including root mean square error (RMSE), standard deviation (SD), bias, and linear trend. The key findings are as follows. (1) FY-3 MWTS observations can effectively capture the characteristic seasonal cycles and vertical atmospheric structures of brightness temperatures, confirming their basic reliability in climate-related analyses. (2) Significant discontinuities in brightness temperatures are observed in the upper troposphere and lower stratosphere for earlier FY-3 satellites (FY-3A/3B/3C), indicating limitations in their long-term climate consistency, while newer satellites (FY-3D/3E/3F) show substantially improved consistency with NOAA FCDRs. (3) The recalibrated data of FY-3D exhibit a marked quality improvement, with RMSE reduced by 60% compared to FY-3D operational observations, and this recalibrated FY-3D dataset is recommended as the preferred choice for climate applications. (4) Even the operational (non-recalibrated) brightness temperature data of FY-3D achieves remarkable consistency with global benchmarks, boasting a global mean accuracy of 0.270 ± 0.039 K, which fully meets the accuracy thresholds specified by GCOS for ECVs. (5) For specific MWTS channels, the global mean brightness temperatures of Channel 4 (over oceans), Channel 6, and Channel 9 generally meet the stability requirements for climate monitoring, further supporting their utility in long-term climate studies. (6) Larger RMSEs of FY-3D operational data are identified in two key regions: the stratosphere over high latitudes and the mid troposphere over topographically complex areas (e.g., the Qinghai–Xizang Plateau, the Andes Mountains, and parts of Africa). These discrepancies are attributed to two technical factors: non-linearity in the MWTS calibration process and orbital drift of the FY-3 satellites. This study validates the climate monitoring capabilities of FY-3 MWTS observations, clarifies key directions for data quality improvement, and lays an important foundation for the transformation of this data from product generation to climate applications.