Presentation on theme: "Robin Hogan Ewan OConnor Changes to the Instrument Synergy/ Target Categorization product."— Presentation transcript:
Robin Hogan Ewan OConnor Changes to the Instrument Synergy/ Target Categorization product
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
Melting layer identification Previously rain was often diagnosed as ice because the melting layer height was taken purely from the model wet-bulb temperature
Melting layer identification Look within 5ºC of T w =0ºC isotherm in model –Melting layer is where greatest divergence in radar Doppler velocity Z v Classification Divergence Melting ice
Radar sensitivity Z_sensitivity variable is estimated as a function of height –Includes range-squared law, mean gas attenuation and ground clutter –Used for iwc_sensitivity and to modify model cloud fraction Currently susceptible to erroneous Z values below the real radar sensitivity Z_sensitivity A day of Z values
1 year of CloudNet data PDF of dissipation rate for different types of cloud Note that aircraft measurements have lower limit of detectability of ~10 – 6 due to aircraft vibrations Previous range for cirrus found from aircraft Bouniol, Hogan and Illingworth (2004)
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