## 1 Introduction  
Accurate characterization of cloud base height (CBH) information is essential for cloud parameterization in climate models, which depend on precise observations to simulate atmospheric processes effectively [1]. Additionally, CBH is an important parameter in the radiative balance of the Earth-atmosphere system [2]. [PERSON] et al. [3] demonstrated that a 100-m deviation in CBH can lead to an error of 1.5 W m\({}^{-2}\) in downward longwave radiation measurements. Furthermore, CBH is also a critical parameter for ensuring aviation safety because of its value in identifying potential icing hazards and strong convection [4]. Therefore, accurate and continuous CBH measurements are critical for both atmospheric studies and aviation applications. Satellite remote sensing has become a powerful tool for obtaining global cloud properties. There are various methods for estimating CBH from satellite radiometric observations [5; 6]. [PERSON] et al. [5] presented a classical CBH retrieval method that classifies clouds into six types, assumes empirical cloud microphysical properties for each type, calculates cloud geometric thickness (CGT) based on these properties, and estimates CBH as the difference between cloud top height (CTH) and CGT. This method was adopted to produce the operational CBH product of the Visible Infrared Imaging Radiometer (VIIRS) onboard the Suomi-National Polar-Orbiting Partnership (SNPP) satellite and the Joint Polar Satellite System (JPSS) [6], making a milestone as one of the first CBH products derived from passive satellite observations. However, there are significant spatial and temporal variations in the microphysical characteristics of clouds in the atmosphere, leading to a large root-mean-square error of 3.7 km for all clouds globally in the CBH estimates derived from the empirical assumptions based on cloud type [7]. Thus, [PERSON] et al. [6] developed a statistical algorithm that utilized a piecewise fitting method to relate CGT with cloud water path (CWP) under varying cloud vertical distribution conditions. The algorithm demonstrated improvement of their CBH retrieval accuracy over the method proposed by [PERSON] et al. [5], and thus was adopted to produce the new version of the CBH product of VIIRS. More recently, [PERSON] et al. [8] established the relationship between CWP and CGT via a systematic look-up table (LUT) of effective cloud water content (ECWC). With the ECWC LUT constrained by different cloud properties and environmental conditions (e.g., latitude, surface type, and season), the algorithm achieved high accuracy in CBH retrievals with a mean bias of \(-0.12\pm 2.34\) km for Advanced Himawari Imager (AHI) in single-layer cloud cases. To conclude, these methods have demonstrated the feasibility of retrieving CBH from space using upstream cloud property products (e.g., CTH and CWP) as main inputs. However, current CWP products are typically derived from the observations in visible and near-infrared channels, making the retrieval of CBH at night very challenging. Therefore, a large room exists for further improvement on continuous satellite remote sensing of nighttime CBH. In recent years, a couple of deep learning (DL) techniques have been introduced into cloud remote sensing studies [9; 10; 11]. In particular, by capturing the spatial features, [PERSON] et al. [10] developed a convolutional neural network (CNN)-based model to quantify accurate cloud optical and microphysical properties with diurnal continuity from the Moderate Resolution Imaging Spectroradiometer (MODIS) infrared observations. Since the CBH retrieval algorithm proposed by [PERSON] et al. [8] can be performed as well as valid CTH and CWP retrievals are available, the advances in retrieving cloud optical and microphysical properties from satellite infrared observations can provide new possibilities for exploring CBH retrievals in both day and night satellite observations.