A labeled dataset of cloud types using data from GOES-16 and CloudSat
Paula V. Romero Jure Sergio Masuelli Juan Bautista Cabral
In this paper we present the development of a dataset consisting of 91
Multi-band Cloud and Moisture Product Full-Disk (MCMIPF) from the Advanced
Baseline Imager (ABI) on board GOES-16 geostationary satellite with 91
temporally and spatially corresponding CLDCLASS products from the CloudSat
polar satellite. The products are diurnal, corresponding to the months of
January and February 2019 and were chosen such that the products from both
satellites can be co-located over South America. The CLDCLASS product provides
the cloud type observed for each of the orbit's steps and the GOES-16 multiband
images contain pixels that can be co-located with these data. We develop an
algorithm that returns a product in the form of a table that provides pixels
from multiband images labelled with the type of cloud observed in them. These
labelled data conformed in this particular structure are very useful to perform
supervised learning. This was corroborated by training a simple linear
artificial neural network based on the work of Gorooh et al. (2020), which gave
good results, especially for the classification of deep convective clouds.