Dataset
Cloud droplet number concentration, calculated from the MODIS (Moderate resolution imaging spectroradiometer) cloud optical properties retrieval and gridded using different sampling strategies
Abstract
This dataset contains cloud droplet number concentrations (CDNC), gridded to 1 by 1 degree resolution using a variety of sampling methods to select valid retrievals. Data from the MODIS (Moderate resolution imaging spectroradiometer) instruments on both the Terra (morning overpass) and Aqua (Afternoon overpass) satellites are available (indicated by a T or A in the filename). This product is gridded using the MODIS collection 6 definition of a day. These sampling methods have been compared against multiple flight campaigns, see Gryspeerdt et al., The impact of sampling strategy on the cloud droplet number concentration estimated from satellite data. Atmos. Meas. Tech. 2022."
Errata: The latitude values in these files are currently inverted, resulting in the data in the files appearing 'upside-down'. As a work-around, the data arrays can be reversed along the latitude axis. Corrected versions of the files will be uploaded shortly.'
Details
| Previous Info: |
2022-10-31 Please note: The latitude values in these files are currently inverted, resulting in the data in the files appearing 'upside-d… Show More 2022-10-31 Please note: The latitude values in these files are currently inverted, resulting in the data in the files appearing 'upside-down'. As a work-around, the data arrays can be reversed along the latitude axis. Corrected versions of the files will be uploaded shortly.' Show Less |
|---|---|
| Previously used record identifiers: |
No related previous identifiers.
|
| 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(s): http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/ When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record. |
| Data lineage: |
Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). This dataset was derived from collection 6.1 of the MODIS cloud product (MOD06_L2, MYD06_L2 |
| Data Quality: |
Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)
|
| File Format: |
Data are in NetCDF format
|
Citations: 2
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.
| Jia, H., Hasekamp, O. & Quaas, J. (2024) Revisiting Aerosol–Cloud Interactions From Weekly Cycles. Geophysical Research Letters 51. https://doi.org/10.1029/2024gl108266 https://doi.org/10.1029/2024gl108266 |
| Wall, C.J., Storelvmo, T. & Possner, A. (2023) Global observations of aerosol indirect effects from marine liquid clouds. Atmospheric Chemistry and Physics 23, 13125–13141. https://doi.org/10.5194/acp-23-13125-2023 https://doi.org/10.5194/acp-23-13125-2023 |
Process overview
Instrument/Platform pairings
Computation Element: 1
| Title | Derivation of the dataset: Cloud droplet number concentration, calculated from the MODIS (Moderate resolution imaging spectroradiometer) cloud optical properties retrieval and gridded using different sampling strategies |
| Abstract | For more information see Gryspeerdt et al., The impact of sampling strategy on the cloud droplet number concentration estimated from satellite data. Atmos. Meas. Tech. 2022. |
| Input Description | None |
| Output Description | None |
| Software Reference | None |
| Output Description | None |
- units: 1
- var_id: Num_all
- long_name: All - Number of valid retrievals
- units: 1
- var_id: Num_all_16
- long_name: All - Number of valid retrievals (1.6um)
- units: 1
- var_id: Num_all_37
- long_name: All - Number of valid retrievals (3.7um)
- units: cm-3
- var_id: Nd_all
- long_name: All - Unadjusted cloud droplet number
- units: cm-3
- var_id: Nd_all_16
- long_name: All - Unadjusted cloud droplet number (1.6um)
- units: cm-3
- var_id: Nd_all_37
- long_name: All - Unadjusted cloud droplet number (3.7um)
- units: 1
- var_id: Num_BR17
- long_name: BR17 - Number of valid retrievals
- units: 1
- var_id: Num_BR17_16
- long_name: BR17 - Number of valid retrievals (1.6um)
- units: 1
- var_id: Num_BR17_37
- long_name: BR17 - Number of valid retrievals (3.7um)
- units: cm-3
- var_id: Nd_BR17
- long_name: BR17 - Unadjusted cloud droplet number
- units: cm-3
- var_id: Nd_BR17_16
- long_name: BR17 - Unadjusted cloud droplet number (1.6um)
- units: cm-3
- var_id: Nd_BR17_37
- long_name: BR17 - Unadjusted cloud droplet number (3.7um)
- units: 1
- var_id: Num_G18
- long_name: G18 - Number of valid retrievals
- units: 1
- var_id: Num_G18_16
- long_name: G18 - Number of valid retrievals (1.6um)
- units: 1
- var_id: Num_G18_37
- long_name: G18 - Number of valid retrievals (3.7um)
- units: cm-3
- var_id: Nd_G18
- long_name: G18 - Unadjusted cloud droplet number
- units: cm-3
- var_id: Nd_G18_16
- long_name: G18 - Unadjusted cloud droplet number (1.6um)
- units: cm-3
- var_id: Nd_G18_37
- long_name: G18 - Unadjusted cloud droplet number (3.7um)
- units: hours
- var_id: Hour
- long_name: Mean retrieval hour
- units: hours
- var_id: Hour_G18
- long_name: Mean retrieval hour (G18 valid pixels)
- units: 1
- var_id: Num_Q06
- long_name: Q06 - Number of valid retrievals
- units: 1
- var_id: Num_Q06_16
- long_name: Q06 - Number of valid retrievals (1.6um)
- units: 1
- var_id: Num_Q06_37
- long_name: Q06 - Number of valid retrievals (3.7um)
- units: cm-3
- var_id: Nd_Q06
- long_name: Q06 - Unadjusted cloud droplet number
- units: cm-3
- var_id: Nd_Q06_16
- long_name: Q06 - Unadjusted cloud droplet number (1.6um)
- units: cm-3
- var_id: Nd_Q06_37
- long_name: Q06 - Unadjusted cloud droplet number (3.7um)
- units: 1
- var_id: Num_Total
- long_name: Total number of MODIS pixels
- units: 1
- var_id: Num_Total_G18
- long_name: Total number of MODIS pixels (G18 sampling)
- units: 1
- var_id: Num_Z18
- long_name: Z18 - Number of valid retrievals
- units: 1
- var_id: Num_Z18_16
- long_name: Z18 - Number of valid retrievals (1.6um)
- units: 1
- var_id: Num_Z18_37
- long_name: Z18 - Number of valid retrievals (3.7um)
- units: cm-3
- var_id: Nd_Z18
- long_name: Z18 - Unadjusted cloud droplet number
- units: cm-3
- var_id: Nd_Z18_16
- long_name: Z18 - Unadjusted cloud droplet number (1.6um)
- units: cm-3
- var_id: Nd_Z18_37
- long_name: Z18 - Unadjusted cloud droplet number (3.7um)
- var_id: lat_bnds
- var_id: lon_bnds
- long_name: time
- var_id: time
- units: days
- 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
2000-01-01T00:00:00
2020-12-31T23:59:59
Geographic Extent
90.0000° |
||
-180.0000° |
180.0000° |
|
-90.0000° |