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Approach: (Presently Optimized of SGP! See Mace et al., Part 1 and 2 in JGR 2006)

Toward Continuous Cloud Microphysics and Cloud Radiative Forcing Using Continuous ARM Data : TWP Darwin Analysis Goal: Characterize the physical properties of the atmospheric column continuously using a suite of cloud property retrieval algorithms and parameterizations.

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Approach: (Presently Optimized of SGP! See Mace et al., Part 1 and 2 in JGR 2006)

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  1. Toward Continuous Cloud Microphysics and Cloud Radiative Forcing Using Continuous ARM Data: TWP Darwin Analysis Goal: Characterize the physical properties of the atmospheric column continuously using a suite of cloud property retrieval algorithms and parameterizations. Corollary: Create an operational description (complete with uncertainty) of the thermodynamic state, properties of condensate (mass and particle size), radiative properties, and solar, IR, clear and cloudy radiative fluxes over the ARM sites. Purpose: 1) For comparison with large-scale models 2) To develop understanding of the role of clouds in the climate system using long-term data.

  2. Approach: (Presently Optimized of SGP! See Mace et al., Part 1 and 2 in JGR 2006) The Easy Part: Profiles with only liquid water and/or cirrus can be addressed using existing retrieval algorithms. The Hard Part: Because the MWR observes the total liquid water path, the challenge is to treat profiles that contain supercooled clouds/mixed phase volumes along with perhaps cloud volumes that are warm (T>273K). Approach to supercooled/mixed phase liquid: Estimate a normalized distribution of LWC using parameterizations and MMCR observations. From this normalized profile and the MWR LWP define a supercooled LWP and a warm LWP. For the warm LWP, goto easy part. For Supercooled liquid: Distribute the supercooled LWP vertically using the normalized parameterization of LWC and the MMCR cloud occurrence. Important: The layer then is guaranteed to have the observed LWP distributed vertically in the column.

  3. Case Study: Darwin, 28 January 2006.

  4. TWP ICE data will allow us to optimize the cloud property algorithms and provide essential validation for this work….

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