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

Robbins, Daniel; Poulsen, Caroline; Siems, Steven; Proud, Simon

doi:https://doi.org/10.5194/amt-15-3031-2022

Articles | Volume 15, issue 9

https://doi.org/10.5194/amt-15-3031-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-15-3031-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 9

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, Caroline Poulsen, Steven Siems, and Simon Proud

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

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.