Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 17, issue 14
Articles | Volume 17, issue 14
https://doi.org/10.5194/amt-17-4337-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-4337-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 14
Research article
 | 
23 Jul 2024
Research article |  | 23 Jul 2024

The Chalmers Cloud Ice Climatology: retrieval implementation and validation

Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson

Data sets

Meteorological and Navigation (Met/Nav) Data Ryan Bennett https://doi.org/10.26023/KJDH-MXGE-HK0V

ERA5 hourly data on pressure levels from 1940 to present H. Hersbach et al. https://doi.org/10.24381/cds.bd0915c6

GPM Ground Validation Cloud Radar System (CRS) OLYMPEX Gerald M. Heymsfield and Tian Lin https://doi.org/10.5067/GPMGV/OLYMPEX/CRS/DATA101

NCEP/CPC L3 Half Hourly 4km Global (60S - 60N) Merged IR V1 John Janowiak et al. https://doi.org/10.5067/P4HZB9N27EKU

NOAA Climate Data Record (CDR) of Gridded Satellite Data from ISCCP B1 (GridSat-B1) Infrared Channel Brightness Temperature Kenneth R. Knapp and NOAA CDR Program https://doi.org/10.7289/V59P2ZKR

GPM Ground Validation UND Citation Cloud Microphysics OLYMPEX Michael R. Poellot et al. https://doi.org/10.5067/GPMGV/OLYMPEX/MULTIPLE/DATA201

French Doppler Radar RASTA Data SAFIRE https://doi.org/10.26023/72PW-V4FB-E0C

French Falcon Isokinetic Evaporator Probe (IKP2) Data Walter Strapp https://doi.org/10.5065/D61N7ZV7

French Falcon Isokinetic Evaporator Probe (IKP2) Data Walter Strapp https://doi.org/10.5065/D6WW7GDS

Ice Water Content from the Isokinetic Probe Walter Strapp https://doi.org/10.5065/D6RN36KJ

Ice Water Content from the Isokinetic Probe Walter Strapp https://doi.org/10.26023/8V5Y-GB2E-CX07

Model code and software

The Chalmers Cloud ice Climatology Adrià Amell and Simon Pfreundschuh https://doi.org/10.5281/zenodo.8278127

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Short summary
The representation of clouds in numerical weather and climate models remains a major challenge that is difficult to address because of the limitations of currently available data records of cloud properties. In this work, we address this issue by using machine learning to extract novel information on ice clouds from a long record of satellite observations. Through extensive validation, we show that this novel approach provides surprisingly accurate estimates of clouds and their properties.
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