Abstract
Clouds are a crucial regulator in the Earth's energy budget through their radiative properties, both at the top of the atmosphere and at the surface; hence, determining key factors like their vertical extent is of essential interest. While the cloud top height is commonly retrieved by satellites, the cloud base height is difficult to estimate from satellite remote sensing data. Here, we present a novel method called ORABase (Ordinal Regression Auto-encoding of cloud Base), leveraging spatially resolved cloud properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument to retrieve the cloud base height over marine areas. A machine learning model is built with two components to facilitate the cloud base height retrieval: the first component is an auto-encoder designed to learn a representation of the data cubes of cloud properties and to reduce their dimensionality. The second component is developed for predicting the cloud base using ground-based ceilometer observations from the lower-dimensional encodings generated by the aforementioned auto-encoder. The method is then evaluated based on a collection of collocated surface ceilometer observations and retrievals from the CALIOP satellite lidar. The statistical model performs similarly on both datasets and performs notably well on the test set of ceilometer cloud bases, where it exhibits accurate predictions, particularly for lower cloud bases, and a narrow distribution of the absolute error, namely 379 and 328 m for the mean absolute error and the standard deviation of the absolute error, respectively. Furthermore, cloud base height predictions are generated for an …
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