# Construction of Nighttime Cloud Layer Height and Classification of Cloud Types
###### Abstract  
A cloud structure construction algorithm adapted for the nighttime condition is proposed and evaluated. The algorithm expands the vertical information inferred from spaceborne radar and lidar via matching of infrared (IR) radiances and other properties at off-nadir locations with their counterparts that are collocated with active footprints. This nighttime spectral radiance matching (NSRM) method is tested using measurements from CloudSat/Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Moderate Resolution Imaging Spectroradiometer (MODIS). Cloud layer heights are estimated up to 400 km on both sides of the ground track and reconstructed with the dead zone setting for an approximate evaluation of the reliability. By mimicking off-nadir pixels with a dead zone around pixels along the ground track, reconstruction of nadir profiles shows that, at 200 km from the ground track, the cloud top height (CTH) and the cloud base height (CBH) reconstructed by the NSRM method are within 1.49 km and 1.81 km of the original measurements, respectively. The constructed cloud structure is utilized for cloud classification in the nighttime. The same method is applied to the daytime measurements for comparison with collocated MODIS classification based on the International Satellite Cloud Climatology Project (ISCCP) standard. The comparison of eight cloud types over the expanded distance shows good agreement in general. active-passive retrieval; cloud vertical structure; nighttime cloud; cloud classification; A-train satellites; CloudSat and CALIPSO 202 appear brighter and colder than the Earth's surface, and retrieve cloud properties accordingly using forward radiative transfer models supplemented by ancillary data. However, the computation can be difficult when the difference between the cloud and THE underlying surface is small, as the clear sky scene variability is larger than usual [2]. It is difficult to distinguish clouds from highly reflective surfaces such as snow/ice and sun glint and also from very cold surfaces as in high latitudes [5]. Therefore, the analyses in this work were restricted to between latitudes 60\({}^{\circ}\) N and 60\({}^{\circ}\) S. These problems could be even worse during nighttime when visible channels are not available, and thus the algorithms are solely dependent on infrared (IR) measurements. Therefore, most of the cloud studies were focused on the calibration and the application using or partly using visible and near-infrared (NIR) measurements (e.g., [6; 7; 8]), whereas fewer studies have contributed to the analyses based solely on IR retrievals at night (e.g., [9; 10]). The principle of passive sensors limits their ability to separate overlapping cloud layers, which can lead to errors in modeling cloud processes or calculating cloud radiative effects [11; 12]. The development of satellite active sensors, represented by the Cloud Profiling Radar (CPR) on the CloudSat satellite [13] and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite [14], provides a possible solution to the limitation of passive sensors. Flying as a coordinated pair, the synergistic retrieval from the two active sensors combines CALIPSO's strength in resolving nadir profiles of optically thin clouds and CloudSat's ability to penetrate deeply into thick cloud layers before being attenuated. By generating their own light sources, CloudSat and CALIPSO provide continuous measurements without reducing accuracy during nighttime. In fact, CALIPSO is known to have better agreement with ground-based lidar and be able to detect more weakly scattering features during nighttime due to higher signal to noise ratio (SNR) [15; 16]. As both satellites were members of A-Train satellite constellation, they also achieved measurement synergy with passive sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS) on-board Aqua [17], which enabled more comprehensive analysis.