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 have developed the first systematic retrieval algorithm for the Fengyun-3G (FY-3G) satellite’s Microwave Radiation Imager for Rainfall Mission (MWRI-RM). The algorithm converts passive microwave observations into an operational product describing surface rainfall and vertical hydrometeor profiles, addressing a gap in exploiting the satellite’s 26 channels spanning 10.65–183 GHz.
The work, Published (DOI: 10.34133/remotesensing.1057) on August 11, 2026, in the Journal of Remote Sensing, matters because 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.
The algorithm combines Bayesian retrieval with a physically optimized prior database linking hydrometeor profiles and atmospheric states to microwave brightness temperatures. It produced a multiple-hydrometeor profile (MHP) product that agreed well with two established Global Precipitation Monitoring (GPM) products—the Goddard profiling algorithm (GPROF) and the 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 root mean square errors 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 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,” adding that its precipitation-related variables can reveal storm microphysics and support improved model parameterization.
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 team sees potential for extending the retrieval framework beyond FY-3G to other Fengyun microwave imagers and future satellite missions, with further refinement of heavy-rain representation, high-elevation retrievals, and ice-particle models needed to improve accuracy and broaden operational use.
