IceCloudNet: 3D reconstruction of cloud ice from Meteosat SEVIRI - data

doi:10.26050/WDCC/IceCloudNet_3Drecon

Jeggle, Kai et al.

ExperimentDOI
cirrus cloudscloudsdeep learningdeep learning-based 3D reconstructionmixed-phase cloudsneural renderingremote sensingvertical reconstruction
Summary
IceCloudNet is a novel method based on machine learning able to obtain high-
quality vertically resolved predictions for ice water content and ice crystal number concentration of clouds containing ice. The predictions come at the spatio-temporal coverage and resolution of Meteosat SEVIRI and the vertical resolution of DARDAR. IceCloudNet consists of a ConvNeXt-based U-Net and a 3D PatchGAN discriminator model and is trained by predicting DARDAR profiles from co-located SEVIRI images. Despite the sparse availability of DARDAR data due to its narrow overpass, IceCloudNet is able to predict cloud occurrence, macrophysical shape, and microphysical properties with high precision.
We release 10 years of vertically resolved ice water content (IWC) and ice crystal number concentration (Nice) of clouds containing ice with a 3 km×3 km×240 m×15 minute resolution on a spatial domain of 30°W to 30°E and 30°S to 30°N. The resulting data set increases the availability of vertical cloud profiles for the period when DARDAR is available by more than six orders of magnitude and moreover, is able to provide vertical cloud profiles beyond the lifetime of the recently ended satellite missions underlying DARDAR.
Project
IceCloudNet (IceCloudNet: 3D reconstruction of tropical cloud ice from Meteosat SEVIRI)
Contact
Kai Jeggle (
 kai.jeggle@nullenv.ethz.ch
 0000-0002-3098-9484)
Ulrike Lohmann (
 ulrike.lohmann@nullenv.ethz.ch
 0000-0001-8885-3785)
Spatial Coverage
Longitude -30 to 30 Latitude -30 to 30 Altitude: 4000 m to 17000 m
Temporal Coverage
2010-03-01 to 2010-08-31
Use constraints
Creative Commons Attribution 4.0 International (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/)
Data Catalog
World Data Center for Climate
Size
1.68 TiB (1844081239931 Byte)
Format
NetCDF
Status
will be continued
Creation Date
Future Review Date
2034-05-10
Find data Export dataset acronyms
Cite as
Jeggle, Kai; Czerkawski, Mikolaj; Serva, Federico; Le Saux, Bertrand; Neubauer, David; Lohmann, Ulrike (2024). IceCloudNet: 3D reconstruction of cloud ice from Meteosat SEVIRI - data. World Data Center for Climate (WDCC) at DKRZ. https://doi.org/10.26050/WDCC/IceCloudNet_3Drecon

BibTeX RIS
Funding
European Commission - Horizon 2020 Framework Programme
Grant/Award No: 860100 - innovation program iMIRACLI under Marie Skłodowska-Curie (iMIRACLI)
Description
Summary:
Findable: 6 of 7 level;
Accessible: 2 of 3 level;
Interoperable: 3 of 4 level;
Reusable: 5 of 10 level
Method
F-UJI online v3.2.0 automated
Method Description
Checks performed by WDCC. Metrics documentation: https://doi.org/10.5281/zenodo.4081213 Metric Version: metrics_v0.5
Result Date
2024-10-09
Result Date
2024-10-09
Description
1. Number of data sets is correct and > 0: passed;
2. Size of every data set is > 0: passed;
3. The data sets and corresponding metadata are accessible: passed;
4. The data sizes are controlled and correct: passed;
5. The spatial-temporal coverage description (metadata) is consistent to the data: passed;
6. The format is correct: passed;
7. Variable description and data are consistent: passed
Method
WDCC-TQA checklist
Method Description
Checks performed by WDCC. The list of TQA metrics are documented in the 'WDCC User Guide for Data Publication' Chapter 8.1.1
Method Url
https://www.wdc-climate.de/docs/WDCC_User_Guide_Data_v1.pdf
Result Date
2024-10-09
Contact typePersonORCIDOrganization
Contact
Kai Jeggle
0000-0002-3098-9484
ETH Zürich
Contact
Ulrike Lohmann
0000-0001-8885-3785
ETH Zürich
Author
Kai Jeggle
0000-0002-3098-9484
ETH Zürich
Author
Dr. Mikolaj Czerkawski
0000-0002-0927-0416
European Space Agency
Author
Federico Serva
0000-0002-7118-0817
Consiglio Nazionale delle Ricerche
Author
Dr. Bertrand Le Saux
0000-0001-7162-6746
European Space Agency
Author
Dr. David Neubauer
0000-0002-9869-3946
ETH Zürich
Author
Ulrike Lohmann
0000-0001-8885-3785
ETH Zürich
Investigator
Kai Jeggle
0000-0002-3098-9484
ETH Zürich
Funder
-
-
European Commission - Horizon 2020 Framework Programme

Is compiled by

[1] DOI Ronneberger, Olaf; Fischer, Philipp; Brox, Thomas. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. doi:10.48550/arXiv.1505.04597
[2] DOI Isola, Phillip ; Zhu, Jun-Yan; Zhou, Tinghui ; Efros, Alexei A. (2016). Image-to-Image Translation with Conditional Adversarial Networks. doi:10.48550/arXiv.1611.07004

Is derived from

[1] European Organisation for the Exploitation of Meteorological Satellites. (2009). High Rate SEVIRI Level 1.5 Image Data - MSG - 0 degree . https://data.eumetsat.int/product/EO:EUM:DAT:MSG:HRSEVIRI

Is documented by

[1] DOI Jeggle, Kai; Czerkawski, Mikolaj; Serva, Federico; Le Saux, Bertrand; Neubauer, David; Lohmann, Ulrike. (2024). IceCloudNet: 3D reconstruction of cloud ice from Meteosat SEVIRI. doi:10.48550/arXiv.2410.04135
[2] DOI Cazenave, Quitterie; Ceccaldi, Marie; Delanoë, Julien; Pelon, Jacques; Groß, Silke; Heymsfield, Andrew. (2019). Evolution of DARDAR-CLOUD ice cloud retrievals: new parameters and impacts on the retrieved microphysical properties. doi:10.5194/amt-12-2819-2019
[3] DOI Sourdeval, Odran; Gryspeerdt, Edward; Krämer, Martina; Goren, Tom; Delanoë, Julien; Afchine, Armin; Hemmer, Friederike; Quaas, Johannes. (2018). Ice crystal number concentration estimates from lidar–radar satellite remote sensing – Part 1: Method and evaluation. doi:10.5194/acp-18-14327-2018
[4] DOI Liu, Zhuang; Mao, Hanzi; Wu, Chao-Yuan;Feichtenhofer, Christoph; Darrell, Trevor; Xie Saining. (2022). A ConvNet for the 2020s. doi:10.48550/arXiv.2201.03545

Attached Datasets ( 1 )

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Parent project(s)

IceCloudNet: 3D reconstruction of tropical cloud ice from Meteosat SEVIRI

[Entry acronym: IceCloudNet_3Drecon] [Entry id: 5275192]