A Shallow Convection Latent Heating Algorithm for CloudSat

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
Sunday, 4 January 2015
Ethan L. Nelson, University of Wisconsin - Madison, Madison, WI; and T. S. L'Ecuyer

A new algorithm has been developed to derive latent heating from CloudSat observations of shallow precipitation. This Bayesian-type algorithm is assembled with a database of warm rain cloud resolving model simulations, employing the Regional Atmospheric Modeling System initialized with composite soundings from the Atlantic Tradewind Experiment over the tropical Atlantic Ocean. Vertical reflectivity profiles are ingested into the algorithm and compared against the model database with the use of a radar simulator to provide a retrieved vertical latent heating profile, surface rain rate, and liquid water path—all with associated uncertainties.

Synthetic retrievals demonstrate that latent heating in shallow precipitating clouds can be constrained using characteristic properties of the reflectivity profile including path integrated reflectivity, path integrated attenuation, and cloud and precipitation top heights. The information provided by ancillary measurements of proxies for atmospheric stability and aerosol concentrations will be examined within this framework. Initial retrievals from CloudSat data will be presented, and retrieved precipitation rates and liquid water path will be compared against existing CloudSat datasets. Finally, considerations for expanding this algorithm to address other precipitation regimes will be outlined along with the possibility of including ancillary measurements into the algorithm.