Articles | Volume 15, issue 9
Atmos. Meas. Tech., 15, 3031–3051, 2022
https://doi.org/10.5194/amt-15-3031-2022
Atmos. Meas. Tech., 15, 3031–3051, 2022
https://doi.org/10.5194/amt-15-3031-2022
Research article
17 May 2022
Research article | 17 May 2022

Improving discrimination between clouds and optically thick aerosol plumes in geostationary satellite data

Daniel Robbins et al.

Data sets

AHI-CALIOP Collocated Data for Training and Validation of Cloud Masking Neural Networks Daniel Robbins, Caroline Poulsen, Simon Proud, and Steven Siems https://doi.org/10.5281/zenodo.5773420

Model code and software

dr1315/AHINN: AHINN Initial Release D. Robbins and S. Proud https://doi.org/10.5281/ZENODO.6538854

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Short summary
A neural network (NN)-based cloud mask for a geostationary satellite instrument, AHI, is developed using collocated data and is better at not classifying thick aerosols as clouds versus the Japanese Meteorological Association and the Bureau of Meteorology masks, identifying 1.13 and 1.29 times as many non-cloud pixels than each mask, respectively. The improvement during the day likely comes from including the shortest wavelength bands from AHI in the NN mask, which the other masks do not use.