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Identification and classification of Cirrus (IC-CIR): A cirrus classification based on satellite and reanalysis data (2003-2013)

Update Frequency: Not Planned
Latest Data Update: 2018-03-16
Status: Completed
Online Status: ONLINE
Publication State: Citable
Publication Date: 2018-03-16
Download Stats: last 12 months
Dataset Size: 4.02K Files | 322MB


Cirrus clouds play an important role in determining the radiation budget of the earth, but many of their properties remain uncertain, particularly their response to aerosol variations and to warming. Part of the reason for this uncertainty is the dependence of cirrus cloud properties on the cloud formation mechanism, which itself is strongly dependent on the local meteorological conditions. This classification system is designed to identify cirrus clouds by the cloud formation mechanism. Using re-analysis and satellite data, cirrus clouds are separated in four main types: orographic, frontal, convective and synoptic. Comparisons with convection-permitting model simulations and back-trajectory based analysis have shown that this classification can provide useful information on the cloud scale updraughts and the frequency of occurrence of liquid-origin ice, with the convective regime having higher updraughts and a greater occurrence of liquid-origin ice compared to the synoptic regimes (see description paper).
This classification is designed to be easily implemented in global climate models - the observational classification results are made available make this comparison easier. The classification has been generated globally for the years 2003-2013 inclusive. Making use of the moderate
resolution imaging spectrometer (MODIS) on-board the Aqua satellite, the classification exists only at 13:30 local solar time each day.

The regimes used within this classification are defined as follows (further details are given in the description paper)
Orographic - proximity to regions of large-scale topography variation
Frontal - satellite detected cirrus clouds that intersect to atmospheric fronts determined from reanalysis data
Convective - satellite detected cirrus clouds in regions of large scale ascent determined from reanalysis data
Synoptic - Not assigned as one of the other regimes.

Data are gridded NetCDF V4 files, provided on a regular longitude-latitude grid at a 1 by 1 degree resolution across the whole globe. The files provide the classification at 13:30 local solar time (the satellite overpass time) and are at a daily resolution, within a folder defining the year. The filename structure is: {year}/IC-CIR.{year}.{day_of_year} where {year} is the year of the data and {doy of year} starts with 001 on the first of January.
Further details about the data, including comparisons to convection-resolving model simulations can be found in the description paper (Gryspeerdt et al., ACP, 2018).

Citable as:  Gryspeerdt, E.; Quaas, J.; Goren, T.; Klocke, D.; Brueck, M. (2018): Identification and classification of Cirrus (IC-CIR): A cirrus classification based on satellite and reanalysis data (2003-2013). Centre for Environmental Data Analysis, date of citation. doi:10.5285/cddfe3093be247d7bac56c9fa9edb3d5.
Abbreviation: Not defined
Keywords: Not defined


Previous Info:
No news update for this record
Previously used record identifiers:
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Access rules:
Public data: access to these data is available to both registered and non-registered users.
Use of these data is covered by the following licence: When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record.
Data lineage:

This classification is derived using data from the moderate resolution imaging spectrometer (MODIS) on-board the Aqua satellite and from the ERA-Interim Reanalysis, created by ECMWF. The MODIS data used is collection 6 of the 1 by 1 degree gridded daily product (MYD08_D3), originating from the NASA Goddard Space Flight Center.

Data Quality:
Research data.
File Format:

Citations: 1

The following citations have been automatically harvested from external sources associated with this resource where DOI tracking is possible. As such some citations may be missing from this list whilst others may not be accurate. Please contact the helpdesk to raise any issues to help refine these citation trackings.

Gryspeerdt, E., Sourdeval, O., Quaas, J., Delanoë, J., Krämer, M., & Kühne, P. (2018). Ice crystal number concentration estimates from lidar–radar satellite remote sensing – Part 2: Controls on the ice crystal number concentration. Atmospheric Chemistry and Physics, 18(19), 14351–14370.

Process overview

This dataset was generated by instruments deployed on platforms as listed below.

Independent Instruments

Aerodyne Research NOx
Output Description


  • long_name: IC-CIR classification for high clouds
  • var_id: IC_CIR_class
  • var_id: lat_bnds
  • var_id: lon_bnds
  • long_name: time
  • var_id: time
  • var_id: time_bnds

Co-ordinate Variables

  • units: degrees_north
  • standard_name: latitude
  • long_name: latitude
  • var_id: lat
  • units: degrees_east
  • standard_name: longitude
  • long_name: longitude
  • var_id: lon
Temporal Range
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End time:
Geographic Extent