School of Meteorology (Defense)

Improved Retrieval of Hydrometeor Mixing Ratios Using Polarimetric Radar Data and the Hydrometeor Classification Algorithm for Assimilation into Storm-Scale Numerical Weather Prediction Models

Jacob Carlin
OU School of Meteorology

29 April 2014, 10:00 AM

National Weather Center, Room 4500
120 David L. Boren Blvd.
University of Oklahoma
Norman, OK

The Warn-on-Forecast initiative led by the National Severe Storms Laboratory (NSSL) in Norman, OK will require the use of high-resolution, convective-resolving models being run in real-time. The assimilation of radar data is a crucial aspect for the success of these models. The current equations used to retrieve the hydrometeor mixing ratios for snow, hail, and rain from the reflectivity rely on a number of assumptions that may degrade the quality of the retrieval. Polarimetric radars offer much more insight into the microphysical characteristics of the hydrometeorological targets over traditional reflectivity. However, research into how best to capitalize on polarimetry specifically for retrievals remains limited.

In this study, the utility and accuracy of the currently used retrieval equations for pure rain and rain/hail mixtures were investigated using two models with spectral bin microphysics: a one-dimensional melting hail model, and the Hebrew University Cloud Model. Additionally, a disdrometer dataset was used to expand and corroborate the dataset. It was found that the current retrieval equations are inadequate for describing the full range of hydrometeor distributions possible and subsequently fail. These errors were largest for rain when size-sorting or evaporation was occurring or in rain derived from melting hail. Additionally, there is no universal relation for retrieving ice water content within a rain/hail mixture. Instead, this relation must be parameterized based on hail size and height below the environmental melting level. Recommendations for future polarimetric assimilation systems were made, which employ specific attenuation for estimating the liquid water content in pure rain and a combination of reflectivity and specific differential phase shift for estimating the rain mass present within a rain/hail mixture. Finally, impacts of varying the retrieved hail mass field were tested in a convection-resolving model. Limited impacts were noted in the case of a single assimilation cycle, but further impacts are anticipated when using a cycling assimilation system. A future paradigm for polarimetric radar data assimilation is proposed.

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