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The customize image generation code computes images and line plots
directly from the raw netcdf lidar data archive. By default it
reads data from the last two hours; however, it can be used to
generate custom images from any time or altitude interval within the
archive. Calibration coefficients are computed from a default
calibration scan and the radiosondes nearest in time to the
data. The web interface activates
a Python script which in turn launches a Matlab image processing
program. This program: 1)locates and reads the raw data, 2) locates
and reads the nearest time radiosonde files, 3) locates and reads the
default system calibration files for the selected time period, 4)
computes the inversion coefficients used to separate particulate and
molecular scattering from the radiosonde and calibration files, 5)
performs time and altitude averaging to match data to the
display resolution, 6) generates the inverted data, 6) generates the
plots. The delay between the web request and the image display
increases as the time interval and/or altitude range is increased. Images
of MMCR, PAERI and MWR data are also generated if requested. Data for these
images are interpolated if the pixel spacing is smaller than the instrument resolution
and averaged if the pixel spacing is larger than the instrument resolution. Once
again it takes approximately one day for these data sets to appear in the archive.
If selected, data from the lidar and radar are used together to compute cloud microphysical quantities. These assume a default crystal type of 'bullet rosette'.
The generate housekeeping data command produces plots of internal lidar
parameters for the selected time period. This is mostly of interest to the lidar
operators.
TheView System Online LogBook provides a record
of lidar system hardware maintenance.
The Process and Export Data as NetCDF creates a
downloadable NetCDF data file containing data from all instruments
placed on a common time and altitude grid. Lidar data are processed
from raw data files with time and altitude resolutions selected by the
user. Data from the MMCR, PAERI, and MWR are interpolated if the
selected resolution is higher that the instrument resolution and
averaged if the selected resolution is less than the instrument
resolution. After a NetCDF is created the user can create data mask
fields using Matlab compatible logical statements to select data on
basis of variables contained in the NetCDF. Quick look images are
generated to guide the selection process. These show selected data
points in color with the rest of the image depicted in shades of
gray. The mask fields allow the user to isolate particular cloud types
or signal regimes. The mask fields do not modify the data, but are
provided in the NetCDF as arrays of ones and zeros having the same
dimension as the data arrays. Cloud microphysical properties can also
be computed from the lidar and radar data. A gamma size distribution
is assumed along with power law representation of the particle volume
and area relative to a sphere as presented by Mitchell JAS Vol 53,no
12, 1996. The parameters of the the size distribution and the power
law coefficents can be selected from preset values or entered by the
user.
The Download Archived
Instrument Data as Received option allows users to download
data received from instruments other than the lidar exactly as it was
received from NOAA or the University of Idaho.
The Pre-processed monthly
data files option provides access to HSRL, and MMCR data
that has been processed using a 3-minute time and 45 m range
averaging. Results are stored in monthly NetCDF files that extend from
the surface to an altitude of 20km. The netcdf files include a default
data quality mask field "qc_mask" that that can be used to reject
flawed data. However it is unlikely that the default selection
criteria will match any particular users requirements. To aid users we
have provided a set of Matlab routines to read, display and compute
data quality mask fields using user selected thresholds. Matlab routines for use with NetCDF files
downloaded from this site.. Note: Rather than use these
pre-processed monthly data files, most users will benefit by creating
custom processed data files with time and altitude averaging selected
to best match their needs. This will also assure use of the latest
version of the data processing code.
