Level 2a Cloudnet product:

Liquid water content (linear scaled adiabatic method)

Summary

Designation

lwc-adiabatic-method

Level

2a

Algorithm design/Product maintainer

Institute

University of Reading

Description

Liquid water content derived from radar and lidar cloud boundaries scaled by using the liquid water path from the dual-wavelength microwave radiometer measurements.

Documentation

None yet available

Relevant publication(s)

Pending

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 liquid water content data, which was produced by the University of Reading using measurements from [SITE].

Dataset contents

Sample NetCDF file: 20031231_chilbolton_lwc-adiabatic-method.nc
Header of sample NetCDF file in ASCII: 20031231_chilbolton_lwc-adiabatic-method.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: Liquid water content from Chilbolton

This dataset contains liquid water content calculated by using radar and lidar to identify the liquid cloud base and top in each profile, using the model temperature and pressure to calculate the adiabatic liquid water content in each cloud layer, and then using dual-wavelength microwave radiometers to scale the liquid water content values to yield the correct liquid water path. While this simple partitioning with height is somewhat uncertain, liquid water clouds are frequently thin, occupying only a few vertical model levels, so this dataset is adequate for testing liquid water content in models. Also available for comparison is the adiabatic liquid water content (i.e. unscaled by the microwave radiometer liquid water path).

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

Dimensions and coordinate variables

Units:hours since 2003-12-31 00:00:00 +0:00
Type:single-precision floating-point vector

Units:m
Type:single-precision floating-point vector

Variables

Units:degrees_north
Type:single-precision floating-point scalar

Units:degrees_east
Type:single-precision floating-point scalar

Units:m
Type:single-precision floating-point scalar

This variable was calculated for the profiles where the "categorization" data has diagnosed that liquid water is present and liquid water path is available from a coincident microwave radiometer. The model temperature and pressure were used to estimate the theoretical adiabatic liquid water content gradient for each cloud base and the adiabatic liquid water content is then scaled so that its integral matches the radiometer measurement so that the liquid water content now follows a quasi-adiabatic profile. If the liquid layer is detected by the lidar only, there is the potential for cloud top height to be underestimated and so if the adiabatic integrated liquid water content is less than that measured by the microwave radiometer, the cloud top is extended until the adiabatic integrated liquid water content agrees with the value measured by the microwave radiometer. Missing values indicate that either liquid water was diagnosed but no microwave radiometer data was available, or that rain is present in the profile and therefore the vertical extent of liquid layers is difficult to ascertain.

Units:kg m^-3
Type:single-precision floating-point array
Variable containing the random error in lwc:lwc_error

This variable is an estimate of the random error in liquid water content due to the uncertainty in the microwave radiometer liquid water path retrieval and the uncertainty in cloud base and/or cloud top height. This is associated with the resolution of the grid used, 60 m, which can affect both cloud base and cloud top. If the liquid layer is detected by the lidar only, there is the potential for cloud top height to be underestimated. Similarly, there is the possibility that the lidar may not detect the second cloud base when multiple layers are present and the cloud base will be overestimated. It is assumed that the error contribution arising from using the model temperature and pressure at cloud base is negligible.

Units:dB
Type:single-precision floating-point array

This variable was calculated for the profiles where the "categorization" data has diagnosed that liquid water is present. The model temperature and pressure were used to estimate the theoretical adiabatic liquid water content gradient for each cloud base.

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

This variable describes whether a retrieval was performed for each pixel, and its associated quality, in the form of 6 different classes. The classes are defined in the definition and long_definition attributes. The most reliable retrieval is that when both radar and lidar detect the liquid layer, and microwave radiometer data is present, indicated by the value 1. The next most reliable is when microwave radiometer data is used to adjust the cloud depth when the radar does not detect the liquid layer, indicated by the value 2, with a value of 3 indicating the cloud pixels that have been added at cloud top to avoid the profile becoming superadiabatic. A value of 4 indicates that microwave radiometer data were not available but the liquid layers were well defined. If cloud top was not well defined then this is indicated by a value of 5. The full retrieval of liquid water content, which requires liquid water path from the microwave radiometer, given in the lwc variable was only performed for classes 1-3. A retrieval of adiabatic liquid water content, which does not include any liquid water path information from the microwave radiometer, was performed for classes 1-5 where class 1 is equivalent to class 4 and classes 2-3 correspond to class 5. No attempt is made to retrieve liquid water content when rain is present; this is indicated by the value 6.

Type:byte array
Definition:
• 0: No liquid water detected
• 1: Reliable retrieval
• 2: Adiabatic retrieval where cloud top has been adjusted to match liquid water path from microwave radiometer because layer is not detected by radar
• 3: Adiabatic retrieval: new cloud pixels where cloud top has been adjusted to match liquid water path from microwave radiometer because layer is not detected by radar
• 4: Adiabatic retrieval where no liquid water path is available
• 5: Unreliable adiabatic retrieval: liquid water detected only by the lidar and no liquid water path is available: cloud top may be higher than diagnosed cloud top since lidar signal has been attenuated
• 6: Rain present: cloud extent is difficult to ascertain and liquid water path also uncertain

This variable is the vertically integrated liquid water directly over the site. The temporal correlation of errors in liquid water path means that it is not really meaningful to distinguish bias from random error, so only an error variable is provided. Original comment: This variable is the vertically integrated liquid water content, or liquid water path (lwp), obtained using dual-wavelength microwave radiometer, lidar and information from a forecast model. The lwp is assumed to be proportional to microwave optical depth, and the coefficients used to derive it are obtained as follows. For the liquid water coefficients, the lidar is used to locate the cloud and the model to diagnose cloud base temperature. The water vapour coefficients are derived from model temperature and vertical humidity distribution. Note that humidity is not used in an absolute sense but only in the sense of determining the effective emission temperature of a given water vapour path. Finally, periods of clear sky identified by lidar are used to estimate calibration errors in the radiometers using the fact that lwp retrieved in these regions should be zero.

Units:kg m^-2
Type:single-precision floating-point vector
Variable containing the random error in lwp:lwp_error

This variable is a rough estimate of the one-standard-deviation error in liquid water path, calculated as a combination of a 20 g m-2 linear error and a 25% fractional error.

Units:kg m^-2
Type:single-precision floating-point vector

This variable is the liquid water content assuming a tophat distribution. I.e. the profile of liquid water content in each layer is constant.

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