Calibrated Probabilistic Nowcasting of Coastal Sea Fog from Co-Located Microwave Radiometer and Millimeter-Wave Cloud Radar Observations
Sea fog that lowers horizontal visibility below 1 km is a recurrent hazard to port operations and near-shore navigation, yet its objective, continuous short-range warning remains difficult. Using a microwave radiometer and a 35 GHz millimeter-wave cloud radar co-located at the Xiaoyangshan station near the Yangshan deep-water port, eastern China, supervised by a continuous minute-resolution visibility ground truth (about 0.65 million records, 2025–2026), we develop a calibrated probabilistic sea-fog nowcasting model for lead times of 0–3 h. Under strict date-grouped cross-validation—in which neither the input window nor any forecast label crosses a fold boundary—and at the operationally realistic fog base rate of ∼1.9%, the model attains a fog-state ROC-AUC of 0.95–0.96 across lead times and an onset-AUC of about 0.94 at the 3 h lead. After out-of-group isotonic calibration the output probabilities are reliable (expected calibration error 0.007), and a single-stage alarm reaches an event hit rate of 0.84 over the 2026 hold-out period—a figure that is unchanged under a strictly out-of-time protocol in which the model, the calibrator, and the alarm parameters are all frozen on data through 2025—with a median first alert about 3.1 h before fog onset. A compact near-surface scalar model already saturates discrimination; adding vertical profiles and radar microphysics through a mask-aware fusion network yields only a small, statistically non-significant gain, indicating that independent fog events, not model capacity, limit further improvement. The scheme is lightweight, locally deployable, and can be re-evaluated as observations accumulate.