matthew shupe ola persson paul johnston cassie wheeler michael tjernstrom surface-based...
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Matthew Shupe Ola Persson
Paul JohnstonCassie Wheeler
Michael Tjernstrom
Surface-Based Remote-Sensing of
Clouds during ASCOS
Univ of Colorado, NOAAand Stockholm Univ.
Data sets
Millimeter Cloud Radar cloud id, boundaries, phase
Ceilometer cloud id, base
Radiosondes temperature
Microwave Radiometer liquid water path
60-GHz Radiometer temperature
Retrieved Products: Cloud type
Cloud type classification •Utilizes phase-specific signatures from radar, ceilometer, microwave radiometer, radiosondes•Provides a mask of cloud “phase” type
Retrieved Products: Cloud microphysics
Ice particle size
Ice water content
Ice water path
Ice RetrievalsIce mass is derived using a radar reflectivity power law relationship while particle size is related to radar-measured velocity
Liquid droplet size
Liquid water content
Liquid water path
Liquid RetrievalsAssume “adiabatic” profile computed with active sensor cloud boundaries and temperature profile, constrained by microwave radiometer-derived LWP
Dynamics Retrievals•Vertical velocity is derived from radar Doppler spectra using small liquid droplets as tracers of air motion
•Turbulent dissipation rate is related to the time variance of radar Doppler velocity
Retrieved Products: Vertical velocity and turbulence
Turbulent dissipation rate
Vertical velocity
Layer-averaged vertical velocity, 5-pt smooth
Cloud Summary Statistics
Lots of low clouds, most of which were “mixed-phase” (ice crystals falling from a liquid cloud layer)
Case Study Example29 August 2008
From the Cloud Radar Perspective
1)Low-level mixed-phase stratocumulus (ice falling from liquid cloud layer)2)Brief mixed-phase strato/alto-cumulus3)Multiple high cirrus clouds and a suggestion of possible liquid water at times.
Cloud Radar Moments
Case Study Example29 August 2008
Stable layer decouples cloud from surface for
first ½ of day
Strong inversion at about 800 m which
limits the vertical cloud extent
Second ½ of day appears to be well-
mixed from the surface up to the cloud at 700-
800m
60-GHz Potential Temperature and Buoyancy Profiles
Case Study Example29 August 2008
Retrieval Results: Multilayer Cloud Effects
1) Upper layers from 11 – 16 inhibit cloud top radiative cooling by lower layer.
2) As a result, shallow convection, turbulence, ice production, and (probably) liquid production all decrease in lower cloud layer.
3) Circulations and turbulence are significant in upper layer because it can radiatively cool to space.
Case Study Example29 August 2008
Retrieval Results: BL-Cloud Interactions
During first ½ of day (decoupledcloud and surface):1)Relatively more ice than liquid production.2)Thinner liquid layer.3)Turbulence decreases towards surface.
During second ½ of day (well-mixed):1)Less ice production and more liquid water2)Thicker liquid layer.3)Turbulence constant towards surface
Case Study Example29 August 2008
Examine Profiles at 3 times1)Decoupled2)Multi-layer3)Well-mixed
1 2 3
Case Study Example29 August 2008
Average profiles2) Multi-layer•Upper layer turbulence shows radiative cooling•Lower layer turbulence suggests surface forcing•Less ice production in lower layer than upper
3) Well-mixed•Turbulence profile suggests contributions from both surface and radiative cooling
1) Decoupled•Turbulence profile suggests cloud top radiative cooling•Lots of ice
Case Study Example29 August 2008
2) Multilayer, upperSmaller scale motions
2) Multilayer, lowerSimilar size but weaker
1) Decoupled:0.5 -2 km scales
3) Well-mixed:0.5 -2 km, stronger
Case Study Example29 August 2008
Broad updrafts and narrow downdrafts on scales of 1-2 km
Focus on Circulations during “Well-Mixed” period
Higher turbulence near strong down-drafts
Cloud ice forms in updrafts
No clear relationship between LWP-IWP or LWP-updraft but the LWP does increase as the
liquid layer thickness increases