Improved Hydrometeor Detection Method: An Application to CloudSat - Xiaoyu Hu; Jinming Ge; Yanrong Li; Roger Marchand; Jianping Huang; Qiang Fu (Earth and Space Science, 2020)
Abstract Clouds play an important role in the climate system and are a principal source of uncertainty in climate projections. CloudSat has provided an unprecedented opportunity to study the vertical structure of clouds, and its observations are being widely used in scientific studies. However, some clouds are not detected or are only weakly detected by CloudSat. In most studies, the weakest detections, specifically those detected by the so‐called along‐track integration scheme, are typically ignored due to the high rate of false detections, namely, a significant probability that a detected cloud is actually a region of increased measurement noise, rather than a true cloud signal. False detections have been reduced in the latest version (called R05 for release 5) of the CloudSat cloud mask product but at a cost of a significant loss in the true weak signals (i.e., a higher false omission rate). In this study, the CloudSat hydrometeor detection algorithm used in R05 is modified by adding a bilateral filter scheme to improve the detection of weak signals. By comparing with the CALIPSO lidar vertical feature mask, it is shown that the new scheme largely reduces the false detection rate compared to the R04 version, while retaining a large fraction of the true weak signals that have been lost in the R05 version. Implementing this scheme in future CloudSat data processing is expected to lead to a better detection of thin clouds.