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)

Cloud Summary Statistics

Weak diurnal cycles in low-level mixed-phase clouds and LWP

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

• Rich cloud data set

• Provides detailed perspective on cloud-BL interactions

• Nice opportunities for interactions with other groups surrounding retrieval validation, cloud-aerosol interactions, cloud-BL characterization.

Conclusions

Thanks!