New FY-3G algorithm sharpens Global Precipitation Monitoring

A precipitation retrieval algorithm for China’s Fengyun-3G (FY-3G) satellite converts passive microwave observations into an operational product describing surface rainfall and hydrometeor profiles. Combining Bayesian retrieval with a physically optimized prior database, the method produces estimates consistent with established Global Precipitation Monitoring (GPM) products while improving cloud liquid water representation, supporting storm monitoring and precipitation research.

Accurate precipitation measurements are crucial for weather forecasting, climate research, water management, and disaster preparedness, yet rainfall remains a major source of uncertainty in environmental models. Passive microwave satellites can monitor precipitation over oceans, mountains, and areas poorly covered by ground instruments, while providing wider coverage than radar. Fengyun-3G (FY-3G) carries the advanced MWRI for the rainfall mission (MWRI-RM) radiometer with 26 channels spanning 10.65–183 GHz, but these observations lacked a dedicated algorithm for comprehensive operational retrieval. Statistical methods depend strongly on reference datasets, whereas physical approaches can be computationally demanding. Given these challenges, deeper research into specialized multichannel precipitation retrieval for FY-3G is needed.

Researchers from the University of Science and Technology of China, the National Satellite Meteorological Center, the Meteorological Observation Centre of the China Meteorological Administration, and collaborators developed the first systematic retrieval algorithm for FY-3G’s MWRI-RM. Published (DOI: 10.34133/remotesensing.1057) on August 11, 2026, in Journal of Remote Sensing, the study addresses a gap: FY-3G collects rich passive microwave information, but no dedicated method had fully exploited these channels to generate an operational precipitation product. The framework retrieves surface precipitation and vertical hydrometeor structure.

The algorithm generated a multiple-hydrometeor profile (MHP) product that agreed well with two established GPM products, Goddard profiling algorithm (GPROF) and combined radar-radiometer algorithm (CMB), in surface precipitation rate, rainwater path, and ice water path patterns. Surface precipitation estimates generally showed relative mean biases within 14% over ocean and land. A key advantage emerged for cloud liquid water: MHP showed a markedly stronger relationship between cloud liquid water path and precipitation intensity than either comparison product, indicating a more physically realistic cloud–rain linkage. The method also retrieves vertical profiles of cloud liquid water, rainwater, and ice water, offering more information on storm microphysics than rainfall-only products.

Calibration tests showed strong agreement between simulated and observed MWRI-RM brightness temperatures. Under clear skies, Pearson correlations exceeded 0.94 across all channels, mean biases stayed within ±0.4 K, and RMSEs were below 2.5 K over land and 2.0 K for most ocean channels. In a typhoon case, all three products captured the eyewall and three spiral rainbands; MHP achieved a cloud-liquid-water-to-rainfall spatial correlation of 0.84, versus 0.72 for GPROF and 0.52 for CMB. In a land rainfall case, correlations were 0.84, 0.76, and 0.47. Validation against 2,486 rain gauges showed that extending accumulation from 10 to 60 minutes increased correlation from 0.455 to 0.542 and reduced Root Mean Square Error (RMSE) from 1.743 to 1.341 mm/h. Remaining biases reflected heavy-rain underestimation and uncertainties related to surface type and ice-particle assumptions. Oceanic rainwater path was underestimated by 27% versus GPROF and 19% versus CMB.

The authors note that “the MHP product exhibits good consistency with GPROF and CMB.” They add that its precipitation-related variables can reveal storm microphysics and support improved model parameterization. The team also sees potential for extending the retrieval framework beyond FY-3G to other Fengyun microwave imagers and future satellite missions.

The team built a Bayesian retrieval around a prior database linking hydrometeor profiles and atmospheric states with microwave brightness temperatures. GMI observations, GPROF profiles, ERA5 variables, and radiative-transfer calculations were combined. A one-dimensional variational method adjusted cloud liquid water, rainwater, ice water, and surface emissivity to improve physical consistency. The database was applied to FY-3G MWRI-RM observations, with separate retrievals for ocean, coast, low-elevation land, and high-elevation regions. Performance was evaluated against GPROF, CMB, case studies, and ground rain gauges.

The prototype could strengthen operational precipitation monitoring over China and globally, especially where ground networks are sparse. Because MHP retrieves rainfall together with cloud and frozen-water structure, it may support typhoon and flood monitoring, cloud–precipitation research, climate-model evaluation, and improved microphysical parameterization. The authors propose transferring the prior database and retrieval architecture to microwave imagers on other FY-3 satellites, particularly future missions. Further refinement of heavy-rain representation, high-elevation retrievals, and ice-particle models could improve accuracy and broaden future operational use.

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References

DOI

10.34133/remotesensing.1057

Original Source URL

https://spj.science.org/doi/10.34133/remotesensing.1057

Funding information

This work was supported by the Meteorological Joint Fund of the National Natural Science Foundation of China (grant nos. U2542205 and U2442214); Anhui Provincial Key Research and Development Project (grant no. 2408055UQ005); the FY Satellite Advanced Planning (grant FY-APP-ZX-2022.0212); The China Meteorological Administration Science the Technology Projects (grant CMAJBGS202502); and the National Natural Science Foundation of China (grant 42075124).

About Journal of Remote Sensing

The Journal of Remote Sensing, an online-only Open Access journal published in association with AIR-CAS, promotes the theory, science, and technology of remote sensing, as well as interdisciplinary research within earth and information science.