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Toward an Algorithm for Estimating Latent Heat Release in Warm Rain Systems

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  • 1 Department of Atmospheric and Oceanic Sciences, University of Wisconsin–Madison, Madison, Wisconsin
  • | 2 Colorado State University, Fort Collins, Colorado
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Abstract

This paper outlines an approach for estimating latent heating, surface rainfall rate, and liquid water path in warm rain from downward-viewing W-band radar observations using a Bayesian Monte Carlo algorithm. The algorithm utilizes observed vertical and path-integrated characteristics of precipitating liquid clouds to identify the most appropriate hydrometeor and latent heating structures in a large database of profiles generated using a cloud-resolving model. These characteristics are selected by applying multiple performance metrics to synthetic retrievals. Analysis of the retrievals suggests that a combination of cloud-top, rain-top, and maximum reflectivity heights; vertically integrated reflectivity and attenuation; and a measure of near-surface intensity is sufficient to constrain bulk properties and the vertical structure of warm rain systems. When applied to observations at CloudSat resolution, biases in retrieved liquid water path and surface rainfall rate are small (less than 10%). The algorithm also captures the vertical structure of latent heating, although the magnitudes of integrated heating and cooling exhibit nearly compensating low biases. Random errors are larger owing to the limitations of single-frequency radar observations in constraining drop size distributions. Uncertainties in the altitudes of peak heating and cooling at the pixel scale are typically less than one vertical level, while uncertainties in vertically resolved estimates of heating and cooling rates are on the order of a factor of 2. The utility of the technique is illustrated through application to case studies from airborne radar data from the VAMOS Ocean–Cloud–Atmosphere–Land Study field campaign and satellite observations from CloudSat.

Corresponding author address: Ethan L. Nelson, Department of Atmospheric and Oceanic Sciences, University of Wisconsin–Madison, 1225 West Dayton Street, Madison, WI 53706. E-mail: ethan.nelson@aos.wisc.edu

Abstract

This paper outlines an approach for estimating latent heating, surface rainfall rate, and liquid water path in warm rain from downward-viewing W-band radar observations using a Bayesian Monte Carlo algorithm. The algorithm utilizes observed vertical and path-integrated characteristics of precipitating liquid clouds to identify the most appropriate hydrometeor and latent heating structures in a large database of profiles generated using a cloud-resolving model. These characteristics are selected by applying multiple performance metrics to synthetic retrievals. Analysis of the retrievals suggests that a combination of cloud-top, rain-top, and maximum reflectivity heights; vertically integrated reflectivity and attenuation; and a measure of near-surface intensity is sufficient to constrain bulk properties and the vertical structure of warm rain systems. When applied to observations at CloudSat resolution, biases in retrieved liquid water path and surface rainfall rate are small (less than 10%). The algorithm also captures the vertical structure of latent heating, although the magnitudes of integrated heating and cooling exhibit nearly compensating low biases. Random errors are larger owing to the limitations of single-frequency radar observations in constraining drop size distributions. Uncertainties in the altitudes of peak heating and cooling at the pixel scale are typically less than one vertical level, while uncertainties in vertically resolved estimates of heating and cooling rates are on the order of a factor of 2. The utility of the technique is illustrated through application to case studies from airborne radar data from the VAMOS Ocean–Cloud–Atmosphere–Land Study field campaign and satellite observations from CloudSat.

Corresponding author address: Ethan L. Nelson, Department of Atmospheric and Oceanic Sciences, University of Wisconsin–Madison, 1225 West Dayton Street, Madison, WI 53706. E-mail: ethan.nelson@aos.wisc.edu
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