The third generation of MicroWave Temperature Sounder (MWTS-III) is a major payload of FengYun-3F (FY-3F) satellite, which was launched on August 3, 2023. It can provide valuable atmospheric temperature profiles in morning time for numerical weather prediction (NWP) applications. The prelaunch thermal vacuum (TVAC) calibration test was performed to examine the instrument quality. This paper briefly describes of the instrument design of MWTS-III and details the TVAC tests calibration methodology and radiometric performance, including sensitivity, striping, nonlinearity, and calibration accuracy. Channels 1 and 2, which are received by direct-detection receivers, have a noise equivalent delta temperature (NEDT) of less than 0.2K, weak correlations with other channels, striping indexes (SI) of 1.0 and 1.2, small peak nonlinearity and calibration residual errors within ±0.03K. The NEDT for Channels 3-17 is 0.16 K-1.38 K. The inter-channel correlation and striping phenomena are more significant for these 15 channels. Since the linear calibration deviations were well mitigated by the nonlinear calibration, the calibration residual errors for channels 3-11 are within ±0.05 K and for channels 12-17 are within ±0.17 K. The predicted on-orbit calibration accuracy for all channels are between 0.37 K and 1.48K. In summary, all channels of FY-3F MWTS-III meet the specifications, and the instrument has great application potential.

The second-generation Microwave Humidity Sounder (MWHS-2) onboard Fengyun (FY)-3C has a data quality comparable to that of counterpart microwave moisture sounders. However, channels 13 and 14 have large biases that are negatively correlated with the instrument temperature and prevent the operational assimilation of the data of these two channels. To better understand the biases of channels 13 and 14, the correlation of the observation minus simulation data (O–B bias) of different channels with the instrument temperature and other factors is investigated for FY-3C MWHS-2 in this article. A sensitivity analysis using a gradient boosting decision tree indicates that the instrument temperature and scan position are the two dominant factors, with total bias-contribution scores exceeding 0.8 for both channels 13 and 14. This conclusion is verified through further analysis of the bias distributions for the scan position, scene brightness temperature, and ascending/descending orbits. Using 12-week data recorded in 2016, the correlations of the O–B bias with the scan position and instrument temperature are specifically investigated and a correction algorithm is formulated, with which data for 2016 and 2017 are corrected. The corrected data in both channels have smaller, more stable biases, and are less affected by the instrument temperatures and scan position. The bias contributions associated with these two factors should thus be further studied.

The accurate inversion of low-altitude atmospheric temperature and humidity is crucial for weather forecasting and climate monitoring. This letter introduces the MR-TH method, a deep learning approach that uses convolutional neural networks (CNNs) and transformer architecture to invert low-altitude 3-D atmospheric temperature and humidity distribution from airborne microwave radiometer data. By capturing nonlinear relationships and spatial correlations, MR-TH improves the inversion accuracy of traditional methods. This network is trained and validated using onboard flight data, reanalysis products, and radiosonde measurements. The results indicate that the mean square error (mse) of temperature inversion for MR-TH is 0.31.5 K and the humidity mse is 0.22.0 g/kg, with an accuracy improvement of over 15% compared to the BP neural network (BP NN) method within the range of 15 km altitude. MR-TH also shows a high correlation (>90%) with radiosonde data. MR-TH provides a feasible solution for improving the accuracy of atmospheric parameter inversion from airborne microwave radiometer observation data.

The third generation Microwave Temperature Sounder (MWTS-III) onboard Fengyun-3E (FY-3E) can obtain the atmospheric temperature profile in the early morning time, which is valuable for numerical weather prediction (NWP), climate analysis and other environment studies. However, calibrated brightness temperature (BT) of the surface-insensitive sounding channels (i.e. channels 7-17) have a sea-land contrast bias. It is 0.2 K-0.3 K for channels 9-17 and -1.33 K and -2.86 K for channels 7 and 8, respectively. The bias characteristic of channels 9-17 is similar to that of the corresponding channels of FY-3C, which is caused by the interference from a window channel and can be corrected by empirical method. Moreover, the spectral response functions of channels 7 and 8 were examined over a wider frequency range by measuring the instrument of similar design and construction to the on-orbit MWTS-III. The measured data shows that channels 7 and 8 have out-of-band response located between 50 GHz to 52 GHz, which close to the response range of channel 4. A linear function developed in this letter is effective for the BT bias correction. The sea-land contrast bias of these two channels has been decreased to -0.35K and -0.16K. And the daily mean bias of channels 7 and 8 are improved from -1.8K and -2.3K to -0.5K and -0.4K, respectively. This correction function has been integrated into the FY-3E MWTS-III operational calibration program in December 7, 2022. Meanwhile, the subsequent MWTS-III has been further improved to avoid the sea-land contrast bias.