Matthew Shupe Ola Persson Paul Johnston Cassie Wheeler Michael Tjernstrom Surface-Based Remote-Sensing of Clouds during ASCOS Univ of Colorado, NOAA and.

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Presentation transcript:

Matthew Shupe Ola Persson Paul Johnston Cassie Wheeler Michael Tjernstrom Surface-Based Remote-Sensing of Clouds during ASCOS Univ of Colorado, NOAA and 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 Retrievals Ice 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 Retrievals Assume “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 Example 29 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-cumulus 3)Multiple high cirrus clouds and a suggestion of possible liquid water at times. Cloud Radar Moments

Case Study Example 29 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 m 60-GHz Potential Temperature and Buoyancy Profiles

Case Study Example 29 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 Example 29 August 2008 Retrieval Results: BL-Cloud Interactions During first ½ of day (decoupled cloud 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 water 2)Thicker liquid layer. 3)Turbulence constant towards surface

Case Study Example 29 August 2008 Examine Profiles at 3 times 1)Decoupled 2)Multi-layer 3)Well-mixed 123

Case Study Example 29 August 2008 Average profiles 2) 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 Example 29 August ) Multilayer, upper Smaller scale motions 2) Multilayer, lower Similar size but weaker 1) Decoupled: km scales 3) Well-mixed: km, stronger

Case Study Example 29 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!