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Level 2b Cloudnet product:

Cloud fraction on Météo France model grid

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Summary

Designation
cloud-fraction-meteo-france-grid
Level
2b
Algorithm design
Product maintainer
Institute
University of Reading and Météo France
Description
This product consists of cloud fraction calculated from the observations by area and volume on grid of the Météo France model.
Documentation
None yet available
Relevant publication(s)
Hogan, R. J., C. Jakob and A. J. Illingworth, 2001: Comparison of ECMWF winter-season cloud fraction with radar-derived values. J. Appl. Meteorol., 40, 513-525. PDF
Quicklooks
http://www.met.rdg.ac.uk/radar/cloudnet/quicklooks/
Suggested acknowledgement
We acknowledge the Cloudnet project (European Union contract EVK2-2000-00611) for providing the cloud fraction on Météo France model grid data, which was produced by the University of Reading and Météo France using measurements from [SITE].

Dataset contents

Sample NetCDF file: 20031001_chilbolton_cloud-fraction-meteo-france-grid.nc
Header of sample NetCDF file in ASCII: 20031001_chilbolton_cloud-fraction-meteo-france-grid.cdl

The following is a description of the variables in this product, produced automatically from the metadata in the sample file (using the nc2html unix utility):

Dataset title: Cloud fractions by area and volume, as observed at Chilbolton, averaged to the Meteo-France ARPEGE grid.

This dataset contains cloud fraction both from a forecast model and derived from the high-resolution observations on the grid of that model. There are a number of different cloud fraction variables. In the case of the observations, cloud fraction has been calculated "by volume" (i.e. the volume of a gridbox containing cloud) and "by area" (i.e. the area of the gridbox when viewed from above that is obscured by cloud). It has also been calculated both from the time taken for the wind to advect 1 model grid-box of cloud across the site, and using a constant 1-hour sample window. In the case of the model, the "model_Cv" variable contains cloud fraction taken directly from the model, while "model_Cv_filtered" contains cloud fraction after filtering to remove tenuous ice clouds that are not believed to be likely to be detected by the radar. Generally the observed values should be compared to "model_Cv_filtered", with "model_Cv_filtered_min" and "model_Cv_filtered_max" providing an estimate of the range of uncertainty in the filtering procedure. Note that the problem of some ice cloud not being detected is limited to above around 8 km. The filtering has been performed as discussed in Hogan et al. (2001, J. Appl. Meteorol., 40, 513-525), but accounting for sub-grid variability as described by Hogan and Illingworth (2003, J. Atmos. Sci., 60, 756-767).

    Institution:Data processed at the Department of Meteorology, University of Reading.
    Software version:1.3

Dimensions and coordinate variables

time
Hours UTC
    Units:hours since 2003-10-01 00:00:00 +00:00
    Type:single-precision floating-point vector
height
Height above ground
    Units:m
    Type:single-precision floating-point array

Variables

horizontal_resolution
Horizontal resolution of model
    Units:km
    Type:single-precision floating-point scalar
altitude
Height of radar above mean sea level
    Units:m
    Type:single-precision floating-point scalar
forecast_time(time)
Time since initialization of forecast

    For each profile in the file this variable contains the time elapsed since the initialization time of the forecast from which it was taken. Note that the profiles in this file may be taken from more than one forecast.

    Units:hours
    Type:single-precision floating-point vector
rain_rate_threshold
Rain rate threshold

    Measurements above a surface rain rate of greater than this value were excluded due to the possibility of strong attenuation leading to an underestimate of cloud fraction.

    Units:mm hr-1
    Type:single-precision floating-point scalar
height(time, height)
Height above ground
    Units:m
    Type:single-precision floating-point array
latitude
Latitude of site
    Units:degrees_north
    Type:single-precision floating-point scalar
longitude
Longitude of site
    Units:degrees_east
    Type:single-precision floating-point scalar
Cv(time, height)
Observed cloud fraction by volume, 1 hour sampling

    This variable is the observed cloud fraction, by volume, averaged onto the model grid with height, and 1 hour in time.

    Units:dimensionless
    Type:single-precision floating-point array
Ca(time, height)
Observed cloud fraction by area, 1 hour sampling

    This variable is the observed cloud fraction, by area, averaged onto the model grid with height, and 1 hour in time.

    Units:dimensionless
    Type:single-precision floating-point array
Cv_adv(time, height)
Observed cloud fraction by volume, 23.4km sampling

    This variable is the observed cloud fraction, by volume, averaged onto the model grid with height, and over the time taken to advect the model's horizontal resolution (max 1 hour, min 10 minutes).

    Units:dimensionless
    Type:single-precision floating-point array
Ca_adv(time, height)
Observed cloud fraction by area, 23.4km sampling

    This variable is the observed cloud fraction, by area, averaged onto the model grid with height, and over the time taken to advect the model's horizontal resolution (max 1 hour, min 10 minutes).

    Units:dimensionless
    Type:single-precision floating-point array
column_Ca(time)
Total column cloud cover, 1 hour sampling

    This variable is the total column cloud cover (by area), averaged over 1 hour.

    Units:dimensionless
    Type:single-precision floating-point vector
column_Ca_adv(time)
Total column cloud cover, 23.4km sampling

    This variable is the total column cloud cover (by area), averaged over the time taken to advect the model's horizontal resolution (max 1 hour, min 10 minutes).

    Units:dimensionless
    Type:single-precision floating-point vector
n(time, height)
Number of radar pixels, 1 hour sampling

    This variable is the number of radar pixels used to derive the cloud fractions by area and volume, for 1 hour sampling.

    Units:dimensionless
    Type:single-precision floating-point array
n_adv(time, height)
Number of radar pixels, 23.4km sampling

    This variable is the number of radar pixels used to make the cloud fractions by area and volume, averaging over the time taken to advect 1 model grid box, using model winds.

    Units:dimensionless
    Type:single-precision floating-point array
model_Cv(time, height)
Model cloud fraction
    Units:dimensionless
    Type:single-precision floating-point array
model_iwc(time, height)
Model ice water content
    Units:kg m-3
    Type:single-precision floating-point array
model_lwc(time, height)
Model liquid water content
    Units:kg m-3
    Type:single-precision floating-point array
model_Cv_filtered(time, height)
Model cloud fraction by volume with undetectable cirrus removed using best guess of radar sensitivity

    This variable is model cloud fraction by volume, filtered to remove ice cloud deemed to be too tenuous to be detected by the radar. The best guess of the radar sensitivity has been used, and the spread of IWC across the grid box assumed to be described by a gamma distribution (Hogan and Illingworth 2003).

    Units:dimensionless
    Type:single-precision floating-point array
model_Cv_filtered_max(time, height)
Model cloud fraction by volume with undetectable cirrus removed assuming radar 3 dB more sensitive than best guess

    This variable is the same as "model_Cv_filtered" except that the sensitivity of the radar is taken to be 3 dB better. It therefore provides an approximate estimate of the uncertainty associated with the removal of undetectable ice clouds from the model.

    Units:dimensionless
    Type:single-precision floating-point array
model_Cv_filtered_min(time, height)
Model cloud fraction by volume with undetectable cirrus removed assuming radar 3 dB less sensitive than best guess

    This variable is the same as "model_Cv_filtered" except that the sensitivity of the radar is taken to be 3 dB worse. It therefore provides an approximate estimate of the uncertainty associated with the removal of undetectable ice clouds from the model.

    Units:dimensionless
    Type:single-precision floating-point array
model_iwc_filtered(time, height)
Model ice water content with undetectable cirrus removed using best guess of radar sensitivity

    This variable is model ice water content, filtered to remove ice cloud deemed to be too tenuous to be detected by the radar. The best guess of the radar sensitivity has been used, and the spread of IWC across the grid box assumed to be described by a gamma distribution (Hogan and Illingworth 2003).

    Units:kg m-3
    Type:single-precision floating-point array
model_iwc_filtered_max(time, height)
Model ice water content with undetectable cirrus removed assuming radar 3 dB more sensitive than best guess

    This variable is the same as "model_iwc_filtered" except that the sensitivity of the radar is taken to be 3 dB better. It therefore provides an approximate estimate of the uncertainty associated with the removal of undetectable ice clouds from the model.

    Units:kg m-3
    Type:single-precision floating-point array
model_iwc_filtered_min(time, height)
Model ice water content with undetectable cirrus removed assuming radar 3 dB less sensitive than best guess

    This variable is the same as "model_iwc_filtered" except that the sensitivity of the radar is taken to be 3 dB worse. It therefore provides an approximate estimate of the uncertainty associated with the removal of undetectable ice clouds from the model.

    Units:kg m-3
    Type:single-precision floating-point array
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