Multiple spectral infrared (IR) observations onboard geostationary satellites are effective and widely used in estimating precipitation with high spatiotemporal resolutions. Currently, FengYun-4A (FY-4A) and FengYun-4B (FY-4B), representing the most advanced Chinese geostationary meteorological satellites, are equipped with Advanced Geosynchronous Radiation Imager (AGRI) to continuously observe the climate and weather over vast eastern Asia region. However, geographic factors, such as viewing zenith angle (VZA) and solar zenith angle (SZA), could result in systematic errors in estimating and merging precipitation. Therefore, motivated by analyzing the influence patterns of these physical factors on precipitation estimation, the precipitation estimation using the chromatographic analysis method by merging enhanced multispectral IR observations (PECAM) is proposed to generate the merged precipitation data covering observed fields of both FY-4A and FY-4B. The main conclusions are summarized as follows: 1) latitude, view zenith angle (VZA), and elevation exert a negative influence (up to 20 K) on averaged TBBs of single IR band (as$T_{10.8}$), causing precipitation overestimation; 2)${\Delta T}_{7.1A-13.5A}$and${\Delta T}_{6.95B-13.3B},\Delta T_{3.75H-13.5A}$and${\Delta T}_{3.75H-13.3B}$, and${\Delta T}_{3.75H-3.75L}$are mainly influenced by ecliptic obliquity angle (EOA), SZA, and shadow effect, causing seasonal patterns, diurnal fluctuations, and shadow effects, respectively; 3) compared to PERSIANN-CCS, FY4A-Official, and FY4B-Official, at hourly scale, PECAM-FY4A&B consistently outperforms them across CC, RMSE, and CSI metrics, with minimum improvements of approximately 0.046 in CC, 0.30 mm/h in RMSE, and 0.033 in CSI; and 4) meanwhile at daily scales, merged data from FY-4A&B shows overall improvements in CC, RMSE, and CSI, with at least 0.041, 0.80 mm/day, and 0.022, respectively. Foreseeably, the signal processing and merging strategy in PECAM has significant potential to serve as references for the estimation and integration of precipitation data from the FY-4 series, as well as the GOES, Meteosat, and Himawari series.

Infrared (IR) observations from geostationary satellites have been widely used in estimating the precipitation with high spatiotemporal resolutions. Currently, it is acknowledged that single-channel IR observations are capable to extract the properties of cloud tops, while are insufficient for deeper vertical information. However, based on developments of multispectral IR imagers onboard geostationary satellites, distinguishing different precipitation regions with different vertical structures becomes possible, which has not been fully explored yet. Therefore, combining FengYun-4A/advanced geosynchronous radiation imager (FY-4A/AGRI) and global precipitation measurement/dual-frequency precipitation radar (GPM/DPR), we innovatively proposed frontal precipitation cloud index (FPCI) and saturated precipitation cloud index (SPCI) for IR signals by exploiting the characteristic responses of channel combinations to different precipitation systems. It is proven that multispectral IR observations are capable of responding to certain precipitation systems with different vertical structures. Meanwhile, IR imagers onboard geostationary satellites have significant advantages on tracking developing processes of precipitation systems with high spatiotemporal resolutions than microwave detectors onboard low-earth-orbit (LEO) satellites. In addition, this study is beneficial for impelling IR precipitation estimation to consider more radiation-based physical theory, especially for FY-4A official precipitation product.

Based on a statistical analysis of the currently available 3-year on-orbit Cyclone Global Navigation Satellite System (CYGNSS) data (2017–2019), this study proposes a two-step calibration method to improve the accuracy of the CYGNSS-derived land surface reflectivity (SR) and the resulting soil moisture (SM) estimates. The method is designed for two purposes: the one is to correct the system errors of the SR estimates induced by the calibration of the CYGNSS Version 2.1 L1B data, and the other is to eliminate vegetation attenuation in the SR of the soil layer. Mean SR corrections of ~ −0.9 and 2.2 dB are achieved through the first and second steps of calibration, respectively. This resulted in better SM estimates compared with the Soil Moisture Active Passive (SMAP) product and thein situmeasurements, i.e., improved correlations between the CYGNSS SR and the SMAP SM (from$R = 0.46$to$R = 0.74$), improved correlations between the CYGNSS SR and thein situSM (from$R = 0.47$to$R = 0.62$), and between the CYGNSS SM and thein situSM with the best$R = 0.64$.

Calculating the motion vectors based on the infrared observations can complement the gaps of the passive microwave and radar observations. So, it is an essential step in numerical weather models and other meteorological applications. FengYun-4A (FY4A) geosynchronous satellite was launched in 2016 by the Chinese government. Carrying on board the Advanced Geosynchronous Radiation Imager (AGRI), FY4A can provide instantaneous observations from visible to infrared wavelength. However, the ability of FY4A to capture precipitation motions still needs to be explored, and the optimal scale for generating the motion vectors remains unclear. In this study, a reasonable sliding window strategy is applied to calculate the correlation coefficients (CCs) between two image sequences to determine the motion vectors. Based on this, the China Merged Precipitation Analysis (CMPA) data are recognized as the true precipitation data and are compared with the 10.7-$\mu \text{m}$observations of AGRI aboard FY4A. The results in August 2018 show that based on the quantified directions of the motion vectors between the CMPA and FY4A-TBB (the Temperature of Black Body from FY4A), the CC and root mean square error (RMSE) are about 0.64 and 2.1, respectively, which are acceptable at the resolution of 0.5°. The result indicates that FY4A can capture precipitation motions. Meanwhile, as the resolution becomes lower, the probability distributions of the motion vectors in FY4A-TBB are more similar to those in CMPA, and they become stable at 0.5°. Therefore, 0.5° is considered the optimal resolution to generate the motion vectors for FY4A-TBB. The study results have great potentials for precipitation estimate retrieval and fusion for FY4A and also FY4B.

Accurate and near-real-time rain information at fine scales is critical for forecasting local weather and floods. Traditional classical infrared (IR) cloud-top brightness temperature data alone do not contain sufficient precipitation-related information, and the introduction of visible (VIS) observations limits their applications to daytime. Methods for the efficient and comprehensive utilization of multichannel IR observations for accurately retrieving all-day near-real-time rain rates with consistent high quality warrant further exploration. In this study, we propose an all-day near-real-time quantitative precipitation estimation framework based on multispectral analysis (MSA) from the Advanced Geosynchronous Radiation Imager (AGRI) onboard Chinese FY-4 series satellites; the proposed framework is called FY4QPE- MSA. Multiple IR bands are comprehensively and efficiently considered by adopting the principal component analysis technique to reduce the dimensionality to a few independent features while preserving most of the variations. The main conclusions include, but are not limited to, the following aspects: 1) the MSA from IR channels provides valuable information that facilitates the more accurate delineations of the precipitation occurrences; 2) FY4AQPE -MSA outperforms FY4AQPE- Offical [$\sim 20$% gain in the Pearson correlation coefficient (CC),$\sim 25$% gain in the root mean square error (RMSE), and$\sim 15$% gain in the critical success index (CSI)], FY4AQPE-Single ($\sim 10$% gain in CC,$\sim 15$% gain in RMSE, and$\sim 15$% gain in CSI), and Precipitation Estimation from Remotely Sensed Information using artificial neural network-cloud classification system (PERSIANN-CCS) ($\sim 30$% gain in CC,$\sim 20$% gain in RMSE, and$\sim 25$% gain in CSI); and 3) compared with the international IR-based baseline precipitation product, i.e., PERSIANN-CCS, FY4AQPE-MSA demonstrates no spatial gaps over the Tibetan Plateau. In addition, the results of this study suggest that the proposed general framework is promising and applicable for Chinese FY-4 series satellites in generating all-day near-real-time rain-rate information.

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.