Jacob Carlin-March 3

Assimilation of ZDR Columns for Improving the Spin-Up and Forecast of Convection in Storm-Scale Models Achieving accurate storm-scale analyses and reducing the spin-up time of modeled convection is a primary motivation for assimilating radar data and an active area of research. One popular technique for accomplishing this is diabatic initialization,

Start

March 3, 2017 - 3:30 pm

End

March 3, 2017 - 4:00 pm

Address

National Weather Center, 120 David L. Boren Blvd., Rm 5600, Norman, OK 73072   View map

Assimilation of ZDR Columns for Improving the Spin-Up and Forecast of Convection in Storm-Scale Models

Achieving accurate storm-scale analyses and reducing the spin-up time of modeled convection is a primary motivation for assimilating radar data and an active area of research. One popular technique for accomplishing this is diabatic initialization, in which latent heat and/or moisture increments are added to induce and sustain updraft circulations. Polarimetric radar data has the ability to provide enhanced insight into the microphysical and dynamic structure of convection, but thus far, relatively little has been done to leverage these data for numerical weather prediction.

 

In this study, the ARPS Cloud Analysis is modified from its original Z-based formulation to provide moisture and latent heat adjustments based on the detection of ZDR columns, which serve as proxies for updrafts in deep moist convection and subsequently areas of saturation and latent heat release. Cycled model runs using both the legacy cloud analysis and the newly modified cloud analysis are performed for two cases: the 19 May 2013 central Oklahoma tornadoes and the 25 May 2016 north-central Kansas tornadoes. The ZDR column detection algorithm is shown to be reliable, and qualitative and quantitative improvements in the analysis and forecast of convection is seen for both cases. These improvements include more coherent analyzed updrafts, more realistic forecast reflectivity structures, forecast tracks of convection that better match observed tornado tracks, and improved equitable threat scores seen for both cases. Current limitations of the method and proposed future work will be discussed.