This study investigates the application of an improved multi-threshold method based on the K-means algorithm for clutter filtering in W-band cloud and fog radar observations. Utilizing W-band millimeter-wave cloud and fog radar data collected from March to July 2023 in the Qingdao area, a dataset of cloud and fog echo of different types was constructed and statistically analyzed. Subsequently, a multi-threshold clutter filtering method was proposed to identify and eliminate abnormal interferences such as noise spikes, radial interference, and suspended matter clutter. This method employs the basic data and spatiotemporal information from the cloud radar as feature variables for K-means clustering and dynamically adjusts thresholds based on the clustering results. The clutter-filtered data were further used for the verification analysis of cloud and fog identification. The results demonstrate that the proposed multi-threshold method effectively removes clutter and significantly reduces its impact on cloud and fog echo under weather conditions of clouds, fog, and coexisting cloud–fog, while controlling the loss of cloud and fog echo within the required accuracy range.

For quantitative precipitation estimation (QPE) based on polarimetric radar (PR) and rain gauges (RGs), the quality of the radar data is crucial for estimation accuracy. This paper proposes a combined radar quality index (CRQI) to represent the quality of the radar data used for QPE and an algorithm that uses CRQI to improve the QPE performance. Nine heavy rainfall events that occurred in Guangdong Province, China, were used to evaluate the QPE performance in five contrast tests. The QPE performance was evaluated in terms of the overall statistics, spatial distribution, near real-time statistics, and microphysics. CRQI was used to identify good-quality data pairs (i.e., PR-based QPE and RG observation) for correcting estimators (i.e., relationships between the rainfall rate and the PR parameters) in real-time. The PR-based QPE performance was improved because estimators were corrected according to variations in the drop size distribution, especially for data corresponding to 1.1 mm < average D_m < 1.4 mm, and 4 < average log₁₀N_w < 4.5. Some underestimations caused by the beam broadening effect, excessive beam height, and partial beam blockages, which could not be mitigated by traditional algorithms, were significantly mitigated by the proposed algorithm using CRQI. The proposed algorithm reduced the root mean square error by 17.5% for all heavy rainfall events, which included three precipitation types: convective precipitation (very heavy rainfall), squall line (huge raindrops), and stratocumulus precipitation (small but dense raindrops). Although the best QPE performance was observed for stratocumulus precipitation, the biggest improvement in performance with the proposed algorithm was observed for the squall line.

This study investigates the application of an improved multi-threshold method based on the K-means algorithm for data quality control in W-band cloud and fog radar observations. Utilizing W-band millimeter-wave cloud and fog radar data collected from March to July 2023 in the Qingdao area, a dataset of cloud and fog echo of different types was constructed and statistically analyzed. Subsequently, a multi-threshold quality control method was proposed to identify and eliminate abnormal interferences such as noise spikes, radial interference, and suspended matter clutter. This method employs the basic data and spatio-temporal information from the cloud radar as feature variables for K-means clustering and dynamically adjusts thresholds based on the clustering results. The quality-controlled data were further used for the verification analysis of cloud and fog identification . The results demonstrate that the proposed multi-threshold method effectively removes clutter and significantly reduces the impact of clutter on cloud and fog echo under weather conditions where clouds, fog, and cloud-fog coexist, while controlling the loss of cloud and fog echo within the required accuracy range.