The first scatterometer onboard Chinese meteorological satellites is a dual-frequency and dual-polarization scatterometer named Wind Radar (WindRAD). It uses an advanced fan-beam conical scanning mechanism to acquire wind vector observation of global ocean surfaces and other geophysical parameters. Since the launch in 2021, external calibration using ground-based active radar calibrator (ARC) has been carried out to evaluated WindRAD in-orbit situation and observation accuracy. Satellite-ground integrated external calibration process is proposed, and optimal satellite-ground observation mode is specially designed for the WindRAD, greatly reducing the complexity of satellite-ground interaction and improving the efficiency of external calibration observation. This article proposes the algorithm of WindRAD external calibration, with comprehensive consideration of scientific nature and engineering realizability. WindRAD in-orbit observation data as well as ARC data were used to calculate the real antenna patterns and absolute calibration coefficients of each polarization for both C- and Ku-bands. The evaluation results revealed that the in-orbit antenna pattern hadn’t changed much compared with the prelaunch test result, which for the first time confirmed the correctness of the key parameters used in WindRAD calibration. Moreover, the calibration accuracy is better than 1 dB. For the first time, the stability of WindRAD in orbit is confirmed using external active reference target.

Sea surface wind speed (SSWS) is an important oceanic dynamical parameter, extensively utilized in numerical simulations of climate change and weather forecasts, as well as in storm intensity assessment. Microwave radiometers onboard sun-synchronous satellites can provide a large amount of SSWS data globally. Atmospheric attenuation caused by large raindrops and rainwater contamination can both lead to estimation errors, especially for the sensors without L-band and C-band channels such as the Microwave Radiation Imager (MWRI) sensor onboard the Fengyun-3D (FY3D) satellite. To investigate the potential of MWRI in SSWS detection under bad weather conditions, a lightweight deep neural network (LWDNN) is used based on the Global Change Observation Mission First-Water (GCOM-W1) Advanced Microwave Scanning Radiometer 2 (AMSR2) all-weather SSWS product in 2021. The SSWS product from AMSR2, soil moisture active passive (SMAP), buoys, and the ERA5 reanalysis data have been utilized to validate the LWDNN under all weather conditions. The overall root mean square error (RMSE) of MWRI SSWS is less than 2.0 m/s under all weather conditions and less than 1.5 m/s in the clear-sky region. In order to test the retrieval effectiveness of LWDNN in cyclone regions, the scenes of cyclones are collected. Results show that the RMSEs of the MWRI maximum wind speed (VMAX) product relative to the AMSR2 and SMAP data are 6.31 and 6.66 m/s, respectively, in the wind speed range of 20–70 m/s, and there are no systematic biases. The RMSEs of the MWRI SSWS relative to the Stepped-Frequency Microwave Radiometer (SFMR) is 5.21 m/s in the wind speed range of 6–56 m/s, and the SSWS of MWRI and SFMR is generally consistent.

Cloud liquid water path (LWP) quantifies liquid water amount within the atmosphere and is closely related to water cycle, weather, and climate. Passive microwave (MW) observations are powerful tools for retrieving LWP. An empirical relationship between the LWPs and MW brightness temperatures (BTs) can be obtained for conventional retrievals, which consider only the influence of LWP on BTs. However, besides LWP, the cloud vertical extent [e.g., cloud top height (CTH)] can affect MW emission, absorption, and corresponding channel BTs, but it is ignored in conventional retrievals. This study investigates the influences of CTH on MW LWP retrievals, and a CTH-dependent algorithm is developed using CTHs from infrared retrievals. Synthetic radiative transfer simulations are performed to quantify CTH effects on MW channel BTs and to establish the CTH-dependent retrieval coefficients. We use the Advanced MW Scanning Radiometer 2 (AMSR2) observations. Cloud products from Moderate Resolution Imaging Spectroradiometer (MODIS) are collocated to provide the necessary CTH information. Thus, we develop an LWP retrieval algorithm by combining AMSR2 BTs with MODIS CTHs. The results indicate that incorporating CTH information into LWP retrievals enhances the consistency between MW and visible/infrared retrievals. Specifically, the CTH-dependent algorithm showed an improvement in the intraclass correlation coefficient (ICC) and a reduction in mean relative differences (MRDs) by approximately 4% (from 18% to 14%) compared to AMSR2 operational retrievals. The CTH-dependent results are slightly more consistent with the MODIS results than the CTH-independent ones, though it remains important to note that the CTH-dependent retrievals introduce less differences compared to their CTH-independent retrievals.

The first C- and Ku-band dual-frequency scatterometer (WindRAD) onboard the Chinese FengYun-3E (FY-3E) satellite was successfully launched in July 2021. The WindRAD scatterometer uses an advanced fan-beam conical scanning mechanism to acquire wind vector data of global ocean surfaces and other geophysical parameters through backscattering measurements of the Earth. This article provides an introduction to the WindRAD instrument, an overview of the data preprocessing, and assessment of WindRAD measurements. The numerical weather prediction-based ocean calibration (NOC) approach, natural targets, and cross-calibration against the Ku-band scatterometer onboard the HY-2B satellite based on collocated backscatter measurements, were used to validate WindRAD backscatter results. The evaluation results revealed that the performance of the WindRAD data is generally in good agreement with other scatterometer data currently in use, while WindRAD backscatter data may contain nonlinear calibration issues that require further investigation. WindRAD has the ability to provide high-quality global backscattering measurements, which can be used for the inversion of various geophysical parameters and assimilation applications.

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.

Based on the data simulated by satellite radar simulator unit, two frequencies have been chosen and dual-frequency retrieval algorithm of cloud microwave parameters has been studied. Results suggest that: (1) 94/220GHz is sensitive to the tiny change of drop size parameter and a large frequency difference is advantage to the parameter retrieval. So, 94/220GHz can be chosen as the detection frequency of space borne cloud radar in the future after the consideration of detective ability, attenuation and manufacturing level in the industrial sector. (2) The relationship between dual wavelength ratio and median volume diameter rely on the particle density. DWR always increases with D0if particle density changes with the diameter. By contrast, DWR will fluctuate with D0when particle density is a constant. So the retrieval of a constant density is very difficult. (3) Backward iteration retrieval algorithm of dual frequency can be applied by 94/220GHz and the retrieval results are coincident with the true value. What's more, the precision of retrieval is influenced by system noise and calibration precision. So, the noise and calibration precision should be controlled under 1dBZ in order to satisfy the retrieval precision need.