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Global Attributes:	cloudnet_Conventions: CF-1.0 cloudnet_title: Liquid water content from Juelich cloudnet_location: Juelich cloudnet_day: 17.0 cloudnet_month: 8.0 cloudnet_year: 2013.0 cloudnet_history: 12 Nov 2014 09:39:45 - Generated from categorization data by Ewan O'Connor <e.j.oconnor@reading.ac.uk> Categorization history: 12 Nov 2014 09:39:36 - Generated from level 1 data by Ewan O'Connor <e.j.oconnor@reading.ac.uk> Radar history: Original data has been averaged to 30 seconds. Lidar history: Wed Nov 12 09:38:13 2014 - NetCDF generated from original data by Ewan O'Connor <e.j.oconnor@reading.ac.uk> on habagat Model history: v1.49 created by Thorsten Reinhardt and Sonja Eikenberg 2012-01-20. Gauge history: unknown Microwave radiometer history: unknown cloudnet_source: CloudNet categorization product (software version 0.10.2) comprising: 35.5 GHz Cloud radar MIRA Radar type: Monostatic, pulsed, magnetron Peak Power 30 kW Antenna Type: Cassegrain 1.2 m Antenna Beam Width: 0.5 deg; Juelich Vaisala 905-nm CT25K lidar ceilometer Wavelength: 905 nm Half-angle beam divergence: 0.75 mrad Half-angle field of view: 0.66 mrad; COSMO-EU; Rain gauge; HATPRO-TOPHAT cloudnet_institution: Data processed at the Department of Meteorology, University of Reading. cloudnet_comment: 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). title: Cloudnet liquid water content dataset Institution: Research Center Juelich, Institute for Energy and Climate research (IEK-8) Contact_person: Ulrich Loehnert (Loehnert@meteo.uni-koeln.de) Source: Cloudnet-processed remote-sensing data. See the global argument cloudnet_source for more details. History: Data processed with convert_cloudnet_lwc_to_hdcp.m by TROPOS, Version: 1.0. Initial release based on convert_cloudnet_categorization_to_hdcp.m Conventions: CF-1.6 where applicable Processing_date: 2016-02-22 15:44:07 Author: Patric Seifert, seifert@tropos.de Comments: This dataset is based on data processed with cloudnet. Cloudnet is member of ACTRIS and data is available for numerous sites in addition to those of HD(CP)2. See http://actris.nilu.no/ for details. There is a distinct data policy agreement between HD(CP)2 and ACTRIS to manage the sharing of datasets between both projects. The nc conventions of the original cloudnet-nc-files were slightly modified to fit HD(CP)2 file conventions. In addition, not all variables of the original cloudnet-nc-files are contained in the HD(CP)2 files. The original comments and global arguments of cloudnet present in the nc files were renamed to cloudnet_ARGUMENT_NAME. Licence: For non-commercial use only. This data is subject to the HD(CP)2 data policy to be found at hdcp2.zmaw.de and in the HD(CP)2 Observation Data Product Standard. In addition the data is subject to the Data Protocol Agreement between ACTRIS and HD(CP)2 for the common use of Cloudnet products: 1. Both ACTRIS and HD(CP)2 scientific steering committees (SSCs) agree, that all Cloudnet products are substantial for multiple applications in atmospheric science related to cloud-processes and should thus be made easily and freely available to atmospheric scientists world-wide. 2. The original and citable Cloudnet data from all ACTRIS and HD(CP)2 atmospheric observatories (AO or termed supersites) are available through the Cloudnet web site (http://www.cloud-net.org) or the ACTRIS data centre (http://actris.nilu.no/). Currently, Cloudnet data is created locally on the respective AO server and then transferred to the Cloudnet server in Reading, UK. 3. An exception to 2.) are the Cloudnet data from the atmospheric observatories LACROS (Leipzig), JOYCE (Juelich) and RAO (Lindenberg) during the course of HD(CP)2. Currently HD(CP)2 is developing an Observation Data Product Standard for its atmospheric column, respectively full-domain data needed for the HD(CP)2 numerical model evaluation and improvement. The main motivation for this is to provide easy access to data from the HD(CP)2 domain in unified formats from one server. HD(CP)2 data will be made available via the ICDC data center (icdc.zmaw.de) in Hamburg. 4. Cloudnet data created at the 3 AOs mentioned above shall be additionally stored in HD(CP)2 compliant file format under the following conditions only. o HD(CP)2-compliant files acknowledge the Cloudnet data origin explicitly in the header in an adequate way. o If Cloudnet data is locally reprocessed and sent to the Cloudnet server, the HD(CP)2 compliant Cloudnet files are also simultaneously recreated and linked with ICDC. o Download statistics of data containing Cloudnet products are created through ICDC once HD(CP)2-compliant Cloudnet files become available. These statistics will be regularly made available to the ACTRIS and HD(CP)2 SSCs to document the use of Cloudnet data. 5. If the above conditions are met during the first phase of HD(CP)2, the HD(CP)2-compliant Cloudnet files may be produced for other AOs in the HD(CP)2 domain. This is subject to the agreement of both ACTRIS and HD(CP)2 SSCs. 6. In the future, ACTRIS and HD(CP)2 agree on an effort towards a unified Cloudnet data portal of all European AOs, strongly linked to the activities already on-going with the US partners from ARM/DOE. Dependencies: external Unlimited_Dimension: time

Variables:	time: Array of 64 bit Reals [time = 0..2879] time: standard_name: time long_name: time of profile units: seconds since 1970-01-01 00:00:00 bounds: time_bnds
	time_bnds: Array of 64 bit Reals [time = 0..2879][nv = 0..1] time: nv:
	zag: Array of 32 bit Reals [height = 0..494] height: standard_name: height long_name: height above ground units: m bounds: zag_bnds
	zag_bnds: Array of 32 bit Reals [height = 0..494][nv = 0..1] height: nv:
	zsl: 32 bit Real zsl standard_name: altitude long_name: Altitude above mean sea level units: m
	lon: 32 bit Real lon standard_name: longitude units: degree_east
	lat: 32 bit Real lat standard_name: latitude units: degree_north
	cllw: Array of 32 bit Reals [time = 0..2879][height = 0..494] time: height: standard_name: mass_concentration_of_cloud_liquid_water_in_air long_name: adiabatically scaled liquid water content units: kg m-3 _FillValue: -999.0 comment: 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 1) a liquid water layer was diagnosed but no microwave radiometer data was available, 2) a liquid water layer was diagnosed but the microwave radiometer data was unreliable; this may be because a melting layer was present in the profile, or because the retrieved lwp was unphysical (values of zero are not uncommon for thin supercooled liquid layers), or 3) that rain is present in the profile and therefore, the vertical extent of liquid layers is difficult to ascertain.
	cllw_error: Array of 32 bit Reals [time = 0..2879][height = 0..494] time: height: long_name: error of mass concentration of cloud liquid water in air units: dB _FillValue: -999.0 comment: 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, 29 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. Cloudnet gives errors in dB, i.e. the error is the relative error in dB. It is determined as follows: error=10*log[ (X+dX)/X ] with X being the measured value and dX being the absolute uncertainty.
	cllw_retrieval_status: Array of 16 bit Unsigned Integers [time = 0..2879][height = 0..494] time: height: long_name: cloud liquid water content retrieval status units: 1 definition: 0: No liquid water 1: Reliable retrieval 2: Adiabatic retrieval: cloud top adjusted 3: Adiabatic retrieval: new cloud pixel 4: Unreliable lwp: no retrieval 5: Unreliable lwp/cloud boundaries: no retrieval 6: Rain present: no retrieval 128: Missing Value long_definition: Use unsigned int8 as reference! 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: No retrieval: either no liquid water path is available or liquid water path is uncertain 5: No retrieval: liquid water layer detected only by the lidar and liquid water path is unavailable or uncertain: 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 128: Missing Value. Probably no measurement performed _FillValue: 128 comment: Use unsigned int8 as reference! 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 or not reliable (melting level present or unphysical values) 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 reliable liquid water path from the microwave radiometer, was only performed for classes 1-3. No attempt is made to retrieve liquid water content when rain is present; this is indicated by the value 6. bit 7 (2^7=128) is not Cloudnet standard. It was added for HDCP2. _Unsigned: True
	cllw_th: Array of 32 bit Reals [time = 0..2879][height = 0..494] time: height: long_name: liquid water content (tophat distribution) units: kg m-3 _FillValue: -999.0 comment: This variable is the liquid water content assuming a tophat distribution. I.e. the profile of liquid water content in each layer is constant.
	clwvi: Array of 32 bit Reals [time = 0..2879] time: standard_name: atmosphere_cloud_liquid_water_content long_name: liquid water path units: kg m-2 _FillValue: -999.0 comment: 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.
	clwvi_error: Array of 32 bit Reals [time = 0..2879] time: long_name: error of atmosphere cloud liquid water content units: kg m-2 _FillValue: -999.0 comment: 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.

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DDS:

Dataset {
    Float64 time[time = 2880];
    Float64 time_bnds[time = 2880][nv = 2];
    Float32 zag[height = 495];
    Float32 zag_bnds[height = 495][nv = 2];
    Float32 zsl;
    Float32 lon;
    Float32 lat;
    Float32 cllw[time = 2880][height = 495];
    Float32 cllw_error[time = 2880][height = 495];
    UInt16 cllw_retrieval_status[time = 2880][height = 495];
    Float32 cllw_th[time = 2880][height = 495];
    Float32 clwvi[time = 2880];
    Float32 clwvi_error[time = 2880];
} ftpthredds/samd/data/cln/00/cllw/l4/sups/joy/2013/sups_joy_cln00_l4_cllw_v00_20130817000015.nc;

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