The Advanced Radiative transfer Modeling System (ARMS) is a fast radiative transfer model for satellite radiance calculations and is also used for the direct assimilation of radiances for numerical weather prediction models (NWP). ARMS can support most visible, infrared, and microwave instruments onboard both polar-orbiting and geostationary orbiting satellites from the US, Europe, and China. Both ARMS and RTTOV are employed as satellite observation operators in the China Meteorological Administration Global Forecasting System (CMA-GFS). To evaluate the performance between ARMS and RTTOV in CMA-GFS V4.0, three groups of data assimilation experiments from August 15, 2021, to August 31, 2022, were conducted. It’s shown that good agreement exists in the simulation bias of satellite radiance from both ARMS and RTTOV. Moreover, from the performance of the analysis field and forecast field, the use of ARMS as the satellite observation operator in the CMA-GFS model achieved better results than RTTOV.

A cloud-dependent 1-D variational (1DVAR) precipitation retrieval algorithm applied to FengYun-3D microwave soundings (CD1DVAR-FY3DMS) is developed in this study. Compared with the current 1DVAR precipitation retrieval framework, cloud scene identification (CSI) is first proposed to delineate the different weather conditions including clear sky, stratiform clouds, and convective clouds. Results of this study in a typical tropical cyclone event demonstrate that: 1) the precipitation retrievals considering CSI (correlation coefficient ~0.72 and root mean square error ~1.63 mm/h) outperform those without distinguishing cloud scenes (correlation coefficient ~0.66 and root mean square error ~1.82 mm/h); 2) identifying cloud scenes in a variational scheme could significantly improve the accuracy of retrieving heavy precipitation volumes, with the capturing abilities improved by ~100%; and 3) the FengYun-3 constellation has great potential to complement global microwave retrievals. In addition, these findings could provide valuable references and pathfinders for further improving the retrieval accuracy of microwave-based precipitation estimates, especially for strong convective zones.

Radio frequency signals can interfere with the radiation emanated from the earth atmospheres and affect the quality of the data received from spaceborne microwave instruments. For microwave radiation imager (MWRI) carried on China’s Fengyun-3 series satellites, the data contaminated by radio frequency interference (RFI) are usually identified and labeled as poor quality. In this study, using the high correlation between the observed brightness temperatures (TB) of MWRI channels, an RFI identification and correction method is developed through machine learning techniques. Compared with traditional methods, the new method can simultaneously identify and correct RFI affected data. Since it is trained with global MWRI data, the method works well for both land and oceans. Our analysis show that the MWRI data affected by RFI can be corrected to the quality level close to RFI-free regions.

Radio frequency interference (RFI) is a key interference source affecting image quality of spaceborne microwave imager. In this study, a channel simulation method based on machine learning is adopted to identify RFI in the low-frequency channel in spaceborne microwave imager. By defining the difference between the observed and the simulated brightness temperatures as the RFI index, the range and intensity affected by RFI in the low-frequency channel can be clearly identified.

Gridded precipitation products with fine resolutions and qualities are of great importance for understanding the global water–carbon-energy cycles at various spatiotemporal scales. Though continuous developments in Satellite Remote Sensing fields have been providing great strengths for measuring the precipitation from space, merging precipitation products from different sources, especially the gauge observations, is still the optimal way for obtaining high-quality precipitation data. Currently, the mainstream merging methods mainly focus on merging the rain rates without the considerations of rain events. In this study, we propose a new assumption that both rain events and rain rates should be considered in the merging procedures rather than only the rain rates. To meet our assumption, a morphology-based adaptive spatio-temporal merging algorithm (MASTMA) for combining various precipitation products is proposed, in which the morphology theory is first introduced to comprehensively consider the influences from both rain events and rain rates. The multisource and multiscale precipitation products including the gauge-based data (CPC-U, 0.5°, daily), the satellite-based data [Global Satellite Mapping of Precipitation by Moving Vector with Kalman (GSMaP-MVK), 0.1°, hourly; integrated multisatellite retrievals for global precipitation measurement late run (IMERG-LR), 0.1°, half-hourly], and the reanalysis data (ERA5-land, 0.1°, hourly), have been comprehensively considered in MASTMA for generating the final estimates (MASTMA-F, 0.1°, hourly) over the southeastern regions of the Mainland China in the periods from 2016 to 2019. The main conclusions include but are not limited to: 1) considerations on rain events contribute significantly to the final merged results, especially when eliminating false extreme values over the regions where precipitation is greatly overestimated; 2) the MASTMA could optimally integrate the advantages from multisource precipitation products with different resolutions, particularly from the perspective of the spatial distributions; and 3) the final merged estimates using MASTMA outperform the contemporary state-of-the-art precipitation products especially in terms of modified Kling–Gupta Efficiency (mKGE) and critical success index (CSI). Additionally, the results of this study suggest that MASMTA is a new promising merging approach with great robustness and applicability, and has the foreseeable potentials for the operational run to generate the optimal global merged precipitation products.

Accurate information on the thermal structures of tropical cyclones (TCs) is essential for monitoring and forecasting their intensity and location. In this study, a scene-dependent 1-D variation (SD1DVAR) algorithm is developed to retrieve atmospheric temperature and moisture profiles under all-weather conditions. In SD1DVAR, the background and observation error matrix varies according to the scattering intensity. Especially, the observation error matrix increases in precipitating atmospheres due to a larger uncertainty in the forward operator. With the data from the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership (NPP) satellite, SD1DVAR can retrieve better thermal structures in the storm life cycle than NOAA Microwave Integrated Retrieval System (MIRS). Comparing with the aircraft dropsonde observations, the temperature and humidity errors from SD1DVAR are about 3 K and 20%, respectively, whereas those from MIRS are around 4 K–5 K and 30%, respectively. SD1DVAR is also applied for Microwave Temperature Sounder (MWTS) and Microwave Humidity Sounder (MWHS) onboard FengYun-3D (FY-3D) satellite. The MWTS and MWHS data sets are first combined into a single Comprehensive MicroWave Suite (CMWS) data stream and then used to retrieve the hurricane thermal structures. It is shown that the hurricane structure from CMWS is very similar to that from ATMS. However, due to the availability of 118-GHz measurements from the CMWS, the hurricane temperature vertical structure is better resolved, and the humidity error is also reduced by about 5%.

The accurate information on intensity and location of tropical cyclones (TCs) is essential for weather forecasting and warning. With atmosphere thermal profiles retrieved from microwave sounding instruments, the intensity and location of two major hurricanes are estimated based on the atmospheric hydro-static balance equation. Results show that the use of ATMS products derived from the scene-dependent one-dimensional variation (SD1DVAR) could reduce the errors in estimating the location and intensity of tropical cyclones by 52.79% and 36.91%, respectively, compared with the use of NOAA's operational products. Also, using a combined sounding data from FY-3D MWTS with MWHS that has 118 GHz channels could reduce the intensity estimation error by 37.2%. Moreover, SD1DVAR performs very well for both ATMS and MWTS/MWHS. More instruments will be tested to generate a global TC dataset with high temporal resolution.

Radio-frequency interference (RFI) contaminations hamper the retrieval of geophysical parameters from brightness temperature maps in Synthetic Aperture Imaging Radiometers (SAIRs). This paper is concerned with RFI localization by one snapshot, and an artifact-free detection algorithm based on virtual array and spatial smoothing MUSIC has been proposed. The virtual array is composed of the baseline coverage of the SAIRs. The spatial smoothing method is applied to enhance the rank of the covariance matrix. And the classical MUSIC algorithm is used for direction finding. Experiments on SMOS data are carried out, and the results show better performance than the existing RFI localization algorithms in terms of artifact reduction, which is helpful to make sure whether there are relative weaker RFI sources around a strong RFI source.

Tropical cyclones typically exhibit warm cores aloft and their locations vary, depending on their development stages. In our recent studies, typhoon thermal structures can be derived reasonably well from satellite microwave sounding instruments such as AMSU and ATMS through better constraints of background profiles in 1dvar. The background profiles should be scene- dependent according to cloud type. It is found that the scene-dependent observation error covariances are also required to further improve the retrievals. With newly launched FY-3D satellite, MWHS data at 118 GHz are used to retrieve the atmospheric temperature and humidity profiles. Comparing to ATMS, MWHS can define the warm core locations very well although the warm core magnitude is weaker than that from ATMS.