# Construction of Nighttime Cloud Layer Height and Classification of Cloud Types
###### Abstract
Since using passive or active instruments independently would result in lacking information pertaining to vertical structure or lacking spatial coverage (limited by active sensors' nadir-viewing geometry), respectively, scientists have brought up and tested various methods combining the advantages of passive and active sensors [18; 19; 20; 21; 22]. The general approach is to gain understanding of nearby passive-only retrieved areas with information from the narrow ground track measured by both active and passive sensors. [PERSON] et al. [23] briefly summarized the current methods utilizing collocated active and passive measurements to estimate nearby pixels into two categories, direct measurement extrapolation and semi-empirical estimation. The first category is more or less a \"match-and-substitute\" algorithm--a \"donor\" pixel with a vertical profile from radar or lidar is matched with a nearby \"recipient\" pixel based on the information retrieved by the wide-swath passive sensor [18; 19; 22; 24]. Algorithms in the second category generally use a combination of retrieved cloud products, look-up table, or ancillary data to infer the desired properties of certain pixels from nearby pixels that share other retrieved properties [7; 21; 23; 25; 26]. However, few of these methods have been adapted to and tested during nighttime conditions. In this work, an algorithm is proposed to construct the cloud structure in the region near the ground track of active sensors and classify cloud type using solely IR measurements. The 3D cloud structure of nighttime atmosphere is constructed following the similar radiance matching (SRM) hypothesis [18; 22] that if two pixels have sufficiently similar multi-spectral radiances [or brightness temperatures (BT)], their vertical structures and column properties of clouds can be assumed to be similar. The construction method can provide reliable estimates of nearby cloud vertical structure simultaneously with satellite overpass at nighttime. It could also provide assessment to cloud related studies, such as the cloud-aerosol interaction, over a broader range than the lidar ground track [24; 27]. The method and the adaptation to nighttime conditions are described in detail in the method section. The reliability of the construction is evaluated based on the reconstruction process. The results of the construction are used to infer the cloud type at off-nadir locations, which are compared against the cloud classification of MODIS images according to the standards from the International Satellite Cloud Climatology Project (ISCCP). Note that the comparison used daytime measurements but followed the same algorithm as the construction during nighttime. The paper is outlined as follows: Section 2 lists the satellite sensors and specific datasets used in the study. Section 3 describes the algorithm and the constraints used in the construction. Section 4 discusses the reconstruction results in terms of profiles and classifications. Cloud classification results based on scene construction are also presented and discussed in detail. Section 5 provides a summary and scope for future applications.