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.

Spaceborne passive microwave sounding instruments are important for monitoring tropical cyclones (TCs) over oceans. However, previous studies have found large retrieval errors at TCs’ inner region at the lower troposphere where heavy precipitation occurs. In this study, the background error covariance matrix used in the variational retrieval algorithm is designed to vary with atmospheric conditions. It is found that the errors of retrieved temperature and humidity profiles are significantly reduced under the TC conditions, when they are compared with those from using a static covariance matrix. The retrieval errors of temperature and humidity are about 1.5 K and 10–20%, respectively, in the troposphere. Moreover, the influence of different observation operators on the retrievals are also investigated. It is shown that ARMS (Advanced Radiative Transfer Modeling System) used as an observation operator can produce a higher retrieval accuracy, compared to CRTM (Community Radiative Transfer Model). For the relative humidity profile, the error can be reduced by up to 5% from ARMS. The reason may be attributed to the more comprehensive handling of the scattering from various hydrometeors in ARMS, which results in a higher retrieval accuracy under cloudy conditions.

In this study, the polarization characteristics of the newly launched Fengyun-3E (FY-3E) microwave sounding instruments are discussed, and its data quality is also assessed using one month of observation by the double-difference method. By comparison with the equivalent channels onboard Fengyun-3D (FY-3D) and advanced technology microwave sounder (ATMS), the data quality of FY-3E Microwave Humidity Sounder-II (MWHS-II) is improved and comparable to ATMS, while the data of FY-3E Microwave Temperature Sounder-III (MWTS-III) are slightly worse than data of FY-3D. The data of FY-3E MWTS-III are more susceptible to the early-morning orbit than the data of MWHS-II. In addition, striping noise is still present in channels 5–10 of MWTS-III. After the assessments, FY-3E microwave data are preprocessed and assimilated in the global forecast system for the Chinese Meteorology Administration (CMA-GFS). A total of six individual experiments over the period from 16 July to 15 August 2021 were conducted and the impact was evaluated with the composite score used in operation. It is shown that not only the forecasts for the southern hemisphere and tropics are improved significantly, but also the predictions for the northern hemisphere show some improvements in an overall neutral change from adding FY-3E microwave sounding instruments. The impact of FY-3E microwave radiance is equivalent to ATMS as they are assimilated individually. Furthermore, we note that the forecast impact is affected by the cloud detection scheme to a large extent.

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%.

A cloud-dependent 1-dimensional 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 is firstly 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 cloud scene identification (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. Additionally, these findings could provide valuable references and pathfinders for further improving the retrieval accuracy of microwave-based precipitation estimates, especially for strong convective zones.