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. Plain Language Summary Cloud is important to the Earth‐atmosphere system. Different types of cloud may produce opposing effects, cooling or warming the system. But the simulation of clouds in weather forecast and climate prediction remains challenging, due to the numerous nonlinear processes that govern cloud formation and evolution. Continued accurate observation of clouds remains crucial to improve our knowledge of the processes that control clouds. Data from CloudSat, which carries a 94 GHz cloud radar, are being widely used in climate research. A first step in using the radar data is to separate signals that correspond to energy reflected from real cloud and precipitation from noise in the measurement. This is difficult when the signals from clouds are of similar magnitude to the noise. In this study, we report on an improved algorithm to separate real signals from noise by using a bilateral filter. This improved method can increase the accuracy of CloudSat cloud detection of weak signals and will hopefully lead to a better understanding of thin cloud in the climate system.
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