# IceCloudNet: Cirrus and mixed-phase cloud prediction from SEVIRI input learned from sparse supervision

arXiv: https://arxiv.org/abs/2310.03499 · HTML: https://mlsanity.com/paper/2310.03499/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 5 Oct 2023.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Understanding cirrus clouds using explainable machine learning](https://mlsanity.com/paper/2305.02090/) (2023)

## Built on by (most cited first, 2 of 2)

- [IceCloudNet: 3D reconstruction of cloud ice from Meteosat SEVIRI](https://mlsanity.com/paper/2410.04135/) (2024)
- [C3DIR: A Deep Learning 3-Dimensional Cloud Property Retrieval Scheme for Passive Satellite Imagers](https://mlsanity.com/paper/2607.16929/) (2026)

## Abstract (arXiv)

Clouds containing ice particles play a crucial role in the climate system. Yet they remain a source of great uncertainty in climate models and future climate projections. In this work, we create a new observational constraint of regime-dependent ice microphysical properties at the spatio-temporal coverage of geostationary satellite instruments and the quality of active satellite retrievals. We achieve this by training a convolutional neural network on three years of SEVIRI and DARDAR data sets. This work will enable novel research to improve ice cloud process understanding and hence, reduce uncertainties in a changing climate and help assess geoengineering methods for cirrus clouds.