Robin Hogan

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The Instrument Synergy/Target Categorization
product

• Robin Hogan
• Ewan OConnor

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Overview of changes

• Melting layer identification using Doppler
  velocity
• Previously used only model wet-bulb temperature
• Melting bit in category_bits variable is now
  used
• Sensitivity and error variables
• Notably Z_sensitivity and lwp_error
• Will work without rain gauge data
• Uses radar for rain detection
• Microwave brightness temperatures if available
• Enables LWP to be recalculated using better
  algorithm if required
• Lidar molecular scattering bit for visible lidars
• Enables molecular to be used to estimate optical
  depth in some studies
• Lidar beam divergence and field of view now held
  as variables
• Works with ARM data
• Tested on SGP and NSA data so far
• Documentation!
• http//www.met.rdg.ac.uk/radar/doc/categorization.
  html

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Basics of radar and lidar
Radar ZD6 Sensitive to large particles (ice,
drizzle) Lidar bD2 Sensitive to small
particles (droplets, aerosol)
Radar/lidar ratio provides information on
particle size
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The Instrument synergy/Target categorization
product

• Makes multi-sensor data much easier to use
• Combines radar, lidar, model, raingauge and
  ?-wave radiometer
• Identical format for each site (based around
  NetCDF)
• Performs common pre-processing tasks
• Interpolation on to the same grid
• Ingest model data (many algorithms need
  temperature wind)
• Correct radar for attenuation (gas and liquid)
• Provides essential extra information
• Random and systematic measurement errors
• Instrument sensitivity
• Categorization of targets droplets/ice/aerosol/in
  sects etc.
• Data quality flags when are the observations
  unreliable?

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Target categorization

• Combining radar, lidar and model allows the type
  of cloud (or other target) to be identified
• From this can calculate cloud fraction in each
  model gridbox

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Melting layer identification
v
Z
Divergence
Classification

• Look within 5ºC of Tw0ºC isotherm in model
• Melting layer is where greatest divergence in
  radar Doppler velocity

Melting ice
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Model variables

• T, q, p, u and v taken from model or sonde
• To correct for radar gas attenuation T, q, p
  (but saturated where cloud observed)
• To correct for radar liquid attenuation estimate
  LWC profile using scaled adiabatic method (T, p,
  LWP)
• Subsequent algorithms
• E.g. IWC method requires temperature
• Cloud fraction on model grid requires advection
  speed

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Radar sensitivity

• Z_sensitivity variable is estimated as a function
  of height each day, using the Z distribution
• Includes range-squared law, mean gas attenuation
  and ground clutter
• Used for iwc_sensitivity and to modify model
  cloud fraction
• Code tries to avoid erroneous Z values below the
  real radar sensitivity
• Also calculated
• Z_bias calibration accuracy
• Z_error random error

Incorrect Z_sensitivity!
A day of Z values
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Now applied to ARM data

• Currently use two of the four modes (robust
  rain) as others can have artefacts that lead to
  erroneous cloud fraction
• Recently use simple merging of ceilometer and
  micropulse lidar

Example from US ARM site Need to distinguish inse
cts from cloud
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Melting layer identification

•                                                 
                                                    
       

• Previously rain was often diagnosed as ice
  because the melting layer height was taken purely
  from the model wet-bulb temperature