Physically constrained retrieval of fog visibility from millimeter-wavelength cloud radar observations

Key numbers

• 38.61 % to 77.08

Abstract. Fog severely degrades visibility (Vis) and poses substantial risks to transportation and aviation, yet its quantitative monitoring remains challenging, because conventional optical and passive remote sensing techniques lose sensitivity under dense fog conditions. Millimeter-wave cloud radar (MMCR) provides a promising alternative due to its strong penetration capability and fine spatial resolution; however existing Vis retrieval algorithms largely rely on empirical parameterizations, resulting in considerable uncertainties. In this study, we develop a physically constrained fog Vis retrieval framework that explicitly links radar reflectivity(Z) to fog microphysical properties through radiative transfer theory. Visibility is inferred by estimating the extinction coefficient from independently derived fog liquid water content (LWC) and effective droplet radius (re), thereby avoiding purely empirical formulations. The method is applied to a Ka band MMCR observations at the Datan Observation Base (DOB) Fujian province, China. LWC is retrieved using a mass-absorption based algorithm developed in our previous study. A power-law relationship between the geometric mean radius (rm) and radar reflectivity (Z) is then established using 5 years of droplet size distribution data from an FM-120 fog droplet spectrometer, enabling a reasonable estimation of re from observation. Based on the obtained LWC and droplet size parameters, the extinction coefficient (βext) and Vis are subsequently derived. Validation against ten sea-fog events in spring 2025 demonstrates that the proposed algorithm achieves a fractional error of 36.61 %, outperforming commonly used empirical approaches, which exhibit errors ranging from 38.61 % to 77.08 %. These results indicate that our proposed framework provides a more reliable and physically interpretable solution for high-precision fog visibility retrieval using single-frequency MMCR observations.

Xiaodong Li, Jinming Ge, Yucheng Qiu, Qiaoyi Lv, Wei Song, Jie Zhang, Chi Zhang, Bochun Liu, Jiapeng Wang, Hongwei Xia, Yuhang Zhu

Leverage3 / 5

A method or resource many groups across the field will adopt within a year.

16% of this desk’s rubric

Magnitude3 / 5

Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.

20% of this desk’s rubric

Evidence3 / 5

Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.

20% of this desk’s rubric

Novelty3 / 5

A genuinely new approach to an open problem.

10% of this desk’s rubric

Trajectory2 / 5

Some room to improve with obvious engineering.

14% of this desk’s rubric

Stakes2 / 5

Benefits a professional community (practitioners, clinicians, engineers).

20% of this desk’s rubric

• Citations0 (reference 15, via openalex, Oct 22, 2026, 07:29 UTC)

• Reviewed by heuristic-v7 on Oct 22, 2026, 07:29 UTC. Paper type: method.
• Categories: Atmospheric aerosols and clouds, Meteorological Phenomena and Simulations, Precipitation Measurement and Analysis, Global and Planetary Change, Environmental Science
• BRIEF, No.4 in the Climate & Energy edition of October 22, 2026.