Dataset
Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA): ECMWF Integrated Forecasting System Cloudnet outputs
Update Frequency: Not Planned
Latest Data Update: 2023-02-09
Status: Ongoing
Online Status: ONLINE
Publication State: Citable
Publication Date: 2023-03-07
DOI Publication Date: 2023-03-07
Download Stats: last 12 months
Dataset Size: 96 Files | 24MB
Abstract
ECMWF Integrated Forecasting System (IFS) single-site (Oden) Cloudnet output during MOCCHA - data are used in McCusker et al. : Evaluating Arctic clouds modelled with the Unified Model and Integrated Forecasting System, Atmospheric Chemistry and Physics, 2023.
IFS data are passed through the Cloudnet algorithm to produce calibrated model data that may be used for direct comparisons with observations. Cloudnet combines cloud radar, ceilometer, microwave radiometer, and radiosonde profiles averaged to a common grid at the cloud radar resolution to derive a set of retrieved cloud properties. The Cloudnet products are designed to be used for evaluation of weather forecast models as well as fundamental process studies of cloud. From a modelling perspective, Cloudnet converts liquid and ice mass mixing ratios to the respective cloud water contents for direct comparison with observations, as well as filtering ice water contents for values that would be unobservable by radar. Note that the latitude/longitude relevant for each date in question can be found in these Cloudnet files.
In directories:
iwc-Z-T-ecmwf-grid/ - data include ice water content and total ice water path for observations and model.
lwc-scaled-ecmwf-grid/ - data include cloud liquid water content and liquid water path for observations and model.
cloud-fraction-ecmwf-grid/ - data include cloud fractions by volume for observations and model.
Citable as: McCusker, G.; Vuellers, J. (2023): Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA): ECMWF Integrated Forecasting System Cloudnet outputs. NERC EDS Centre for Environmental Data Analysis, 07 March 2023. doi:10.5285/4c0e990d61454c22be9030777c7fbe89. https://dx.doi.org/10.5285/4c0e990d61454c22be9030777c7fbe89
Abbreviation: Not defined
Keywords: MOCCHA, ECMWF, Unified Model, Cloudnet, Oden, cloud microphysics, aerosol
Details
| Previous Info: |
No news update for this record
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| Previously used record identifiers: |
No related previous identifiers.
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| Access rules: |
Access to these data is available to any registered CEDA user. Please Login or Register for an account to gain access.
Use of these data is covered by the following licence: 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: |
Model data were generated using the ECMWF Integrated Forecasting System at 9km horizontal resolution on a global grid. NetCDF generated from original data by Ewan OConnor (ewan.oconnor@fmi.fi) using cnmodel2nc on cloudnet.fmi.fi. Combined from n=38 lat/lon files at 2019-09-03 12:15:02 by Gillian Young McCusker (G.Y.McCusker@leeds.ac.uk) using Python (Iris and netCDF4), then passed on to the CEDA for long-term archiving. Cloudnet data were generated by Jutta Vuellers (j.vuellers@leeds.ac.uk). |
| Data Quality: |
Data quality were checked by the MOCCHA project data authors before supplying to CEDA. Further information on quality may be available in related documentation. No quality control has been performed by the Centre for Environmental Data Analysis (CEDA)
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| File Format: |
Data are netCDF formatted
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Related Documents
Process overview
This dataset was generated by the computation detailed below.
| Title | ECMWF Integrated Forecasting System output generated by ECMWF. This work also used JASMIN, the UK collaborative data analysis facility, to post-process model data. |
| Abstract | |
| Input Description | None |
| Output Description | None |
| Software Reference | None |
- var_id: height
- standard_name: height
- long_name: Height above ground
- units: m
- long_name: Height of lidar above mean sea level
- var_id: altitude
- units: m
- units: degrees_north
- var_id: latitude
- long_name: Latitude of site
- units: degrees_north
- var_id: longitude
- long_name: Longitude of site
- long_name: Mean model wind direction from 0 to 3 km
- var_id: mean_wind_dir_adv_3km
- units: degrees
- units: degrees
- long_name: Mean model wind direction where liquid water path is present in profile
- var_id: mean_wind_dir_adv
- units: m s-1
- long_name: Mean model winds from 0 to 3 km
- var_id: mean_wind_speed_adv_3km
- units: m s-1
- long_name: Mean model winds where liquid water path is present in profile
- var_id: mean_wind_speed_adv
- units: 1
- long_name: Model cloud fraction
- var_id: model_Cv
- units: 1
- long_name: Model cloud fraction by volume with snow added and undetectable cirrus removed assuming radar 3 dB less sensitive than best guess
- var_id: model_snow_Cv_filtered_min
- units: 1
- long_name: Model cloud fraction by volume with snow added and undetectable cirrus removed assuming radar 3 dB more sensitive than best guess
- var_id: model_snow_Cv_filtered_max
- var_id: model_snow_Cv_filtered
- units: 1
- long_name: Model cloud fraction by volume with snow added and undetectable cirrus removed using best guess of radar sensitivity
- units: 1
- long_name: Model cloud fraction by volume with undetectable cirrus removed assuming radar 3 dB less sensitive than best guess
- var_id: model_Cv_filtered_min
- var_id: model_Cv_filtered_max
- units: 1
- long_name: Model cloud fraction by volume with undetectable cirrus removed assuming radar 3 dB more sensitive than best guess
- long_name: Model cloud fraction by volume with undetectable cirrus removed using best guess of radar sensitivity
- var_id: model_Cv_filtered
- units: 1
- long_name: Model cloud fraction including snow
- var_id: model_snow_Cv
- units: 1
- units: kg m-3
- long_name: Model ice water content
- var_id: model_iwc
- long_name: Model ice water content including snow
- var_id: model_snow_iwc
- units: kg m-3
- units: kg m-3
- long_name: Model ice water content with snow added and undetectable cirrus removed assuming radar 3 dB less sensitive than best guess
- var_id: model_snow_iwc_filtered_min
- units: kg m-3
- long_name: Model ice water content with snow added and undetectable cirrus removed assuming radar 3 dB more sensitive than best guess
- var_id: model_snow_iwc_filtered_max
- var_id: model_snow_iwc_filtered
- units: kg m-3
- long_name: Model ice water content with snow added and undetectable cirrus removed using best guess of radar sensitivity
- units: kg m-3
- long_name: Model ice water content with undetectable cirrus removed assuming radar 3 dB less sensitive than best guess
- var_id: model_iwc_filtered_min
- long_name: Model ice water content with undetectable cirrus removed assuming radar 3 dB more sensitive than best guess
- var_id: model_iwc_filtered_max
- units: kg m-3
- units: kg m-3
- long_name: Model ice water content with undetectable cirrus removed using best guess of radar sensitivity
- var_id: model_iwc_filtered
- units: kg m-3
- long_name: Model liquid water content
- var_id: model_lwc
- long_name: Model liquid water path
- var_id: model_lwp
- units: kg m-2
- units: 1
- var_id: n
- long_name: Number of radar pixels, 1 hour sampling
- long_name: Number of radar pixels, 1.5km sampling
- var_id: n_adv
- units: 1
- long_name: Observed cloud fraction by area, 1 hour sampling
- var_id: Ca
- units: 1
- units: 1
- long_name: Observed cloud fraction by area, 1.5km sampling
- var_id: Ca_adv
- units: 1
- long_name: Observed cloud fraction by volume, 1 hour sampling
- var_id: Cv
- units: 1
- long_name: Observed cloud fraction by volume, 1.5km sampling
- var_id: Cv_adv
- long_name: Observed mean ice water content (including attenuated and raining profiles), 1 hour sampling
- var_id: iwc_inc_rain
- units: kg m-3
- units: kg m-3
- long_name: Observed mean ice water content (including attenuated and raining profiles), 1.5 km.
- var_id: iwc_adv_inc_rain
- units: kg m-3
- long_name: Observed mean ice water content (including attenuated profiles), 1 hour sampling
- var_id: iwc_inc_att
- units: kg m-3
- long_name: Observed mean ice water content (including attenuated profiles), 1.5 km.
- var_id: iwc_adv_inc_att
- units: kg m-3
- long_name: Observed mean ice water content, 1 hour sampling
- var_id: iwc
- long_name: Observed mean ice water content, 1.5 km.
- var_id: iwc_adv
- units: kg m-3
- units: kg m-3
- long_name: Observed mean liquid water content (tophat distribution), 1 hour sampling
- var_id: lwc_th
- units: kg m-3
- long_name: Observed mean liquid water content (tophat distribution), 1.5 km.
- var_id: lwc_adv_th
- long_name: Observed mean liquid water content (tophat distribution), 9 km.
- units: kg m-3
- var_id: lwc_adv_th
- units: kg m-3
- long_name: Observed mean liquid water content, 1 hour sampling
- var_id: lwc
- units: kg m-3
- long_name: Observed mean liquid water content, 1.5 km.
- var_id: lwc_adv
- units: kg m-3
- var_id: lwc_adv
- long_name: Observed mean liquid water content, 9 km.
- var_id: lwp
- units: kg m-2
- long_name: Observed mean liquid water path, 1 hour sampling
- units: kg m-2
- long_name: Observed mean liquid water path, 1.5 km
- var_id: lwp_adv_3km
- units: kg m-2
- var_id: lwp_adv_3km
- long_name: Observed mean liquid water path, 9 km
- units: mm hr-1
- long_name: Rain rate threshold
- var_id: rain_rate_threshold
- long_name: Std. of log (observed liquid water content) (tophat distribution), 1.5 km.
- var_id: lwc_adv_th_std_log
- units: kg m-3
- units: kg m-3
- var_id: lwc_adv_th_std_log
- long_name: Std. of log (observed liquid water content) (tophat distribution), 9 km.
- var_id: lwc_th_std_log
- units: kg m-3
- long_name: Std. of log10(observed liquid water content) (tophat distribution), 1 hour sampling
- units: kg m-3
- long_name: Std. of log10(observed liquid water content), 1 hour sampling
- var_id: lwc_std_log
- var_id: lwc_adv_std_log
- units: kg m-3
- long_name: Std. of log10(observed liquid water content), 1.5 km.
- var_id: lwc_adv_std_log
- units: kg m-3
- long_name: Std. of log10(observed liquid water content), 9 km.
- long_name: Std. of observed liquid water content (tophat distribution), 1 hour sampling
- var_id: lwc_th_std
- units: kg m-3
- units: kg m-3
- long_name: Std. of observed liquid water content (tophat distribution), 1.5 km.
- var_id: lwc_adv_th_std
- long_name: Std. of observed liquid water content (tophat distribution), 9 km.
- units: kg m-3
- var_id: lwc_adv_th_std
- var_id: lwc_std
- units: kg m-3
- long_name: Std. of observed liquid water content, 1 hour sampling
- long_name: Std. of observed liquid water content, 1.5 km.
- var_id: lwc_adv_std
- units: kg m-3
- units: kg m-3
- var_id: lwc_adv_std
- long_name: Std. of observed liquid water content, 9 km.
- units: K
- standard_name: air_temperature
- var_id: model_temperature
- long_name: Temperature
- units: hours
- long_name: Time since initialization of forecast
- var_id: forecast_time
- long_name: Total column cloud cover, 1 hour sampling
- var_id: column_Ca
- units: 1
- units: 1
- long_name: Total column cloud cover, 1.5km sampling
- var_id: column_Ca_adv
- units: Pa s-1
- long_name: Vertical wind in pressure coordinates
- var_id: omega_500mb
- standard_name: omega
- var_id: horizontal_resolution
Co-ordinate Variables
- units: hours
- var_id: time
- standard_name: time
- long_name: hours_UTC
Coverage
Temporal Range
Start time:
2018-08-14T00:00:00
End time:
2018-09-14T00:00:00
Geographic Extent
89.9000° |
||
3.9800° |
73.7600° |
|
56.0100° |
Related parties