## 1 Introduction
For a certain pixel, making the decision, whether it is a cloudy or cloudless one, is required for subsequent cloud or surface parameter retrieval. For typical passive sensors, like shortwave and infrared-band spectral radiometers, the scene classification is mainly based on spectral contrasts between cloudy and clear scenes. Texture or structural information emerging from spatially successive pixels were also used to help distinguish cloudy pixels from clear ones for those imaging sensors that have fine horizontal resolution. When cloudy scenes are acquired, these measurements are used to identify various cloud forms and perform cloud retrieval at the pixel scale [11]. For instance, [PERSON] et al. [12] developed a fuzzy-logic based scheme by using the Advanced Very High Resolution Radiometer (AVHRR) measurements, which is based on a domain of \(32\times 32\) pixels (the pixel is about \(1.1\) km \(\times\) 1.1 km) and outputs four types of cloudy scenes, including low-level clouds, middle-level clouds, high-level clouds, and multilayered clouds. Although many efforts [13; 14; 15; 16] have been carried out identifying multilayered clouds based upon passive measurements, their availability is limited. This deficiency is in principle due to the shallow optical depth that visible and infrared radiation can penetrate into clouds. Thus, it is hard for passive measurements to be sensitive to lower clouds in a multilayered system, and only if the upper ones are optically thin. Multilayered systems with optically thick upper clouds cannot be correctly identified while using passive measurements. When compared with spaceborne radiometers, the active sensors, like microwave radars, are inherently advantageous for detecting the multilayered structure of cloud systems, which benefit from their capability of radial resolution [17; 18]. For general configurations of operational meteorological radars, the vertical resolution reaches the order of magnitude of 100 m, which is sufficient for differentiating the most detached cloud layers. The Cloud Profiling Radar (CPR) aboard CloudSat is designed for cloud observations. With a high signal sensitivity of about \(-30\) dBZ and a horizontal resolution of \(1.4\) km \(\times\) 1.7 km, the CPR is of great value for cloud structure detection from space. [PERSON] et al. [19] conducted a comparison of cloud layer data from Moderate Resolution Imaging Spectroradiometer (MODIS) and CPR. It was found that the consistent ones only account for 25.4% of the samples, and it is 17.7% for single-layered clouds and 7.7% for multilayered clouds. [PERSON] et al. [20] suggested that, on the global scale, about 30% of low-level clouds overlap with various clouds at higher levels, accounting for 12% of total observations. They also found a notable cloudless separation between low-level and high-level cloud layers, with a thickness of least 5 km in terms of zonal averaging. The lower cloud layers in the multilayered structures tend to be obstructed in the top-down perspective, especially for passive measurements [21]. These cloud layers are mostly composed of pure liquid hydrometeors and they are generally termed as warm liquid clouds or warm clouds. In traditional investigations that concern warm clouds, those that are embedded in the multilayered system were probably ignored due to the usage of cloud top temperature criterion, since only the cloud top temperature of the uppermost layer can be derived by using the upward radiance measurement in most cases. This inevitably leads to insufficient sampling of warm cloud layers. Therefore by using active measurements from spaceborne radar, the present study focuses on the warm clouds contained in multilayered systems. Their appearances would be analyzed on the global scale and relevant geometric features of these clouds would be examined.