Improved clear air turbulence diagnostics based on adjustment dynamics

Clear air turbulence (CAT) diagnostic forecast techniques developed over the past several decades are typically based on parameters derived from upper air or numerical weather prediction model (NWP) data such as vertical wind shear, scalar wind speed, horizontal wind shear, deformation, and the non-dimensional Richardson Number. Among the shortcomings of many of the indices is that they are unable to account for CAT caused by gravity waves induced by unbalanced (ageostrophic) flow within the jet stream.

To rectify this, we have developed CAT diagnostics based on principles inherent in the dynamics of adjustment to balance. Results from the NAM and RUC2 will be presented for one such diagnostic, the D-DVSI (Divergence-Deformation Vertical wind Shear Index), which incorporates the "divergence trend" into Ellrod and Knapp's Turbulence Index (TI). The utility of the D-DVSI in regions of anticyclonic flow will be highlighted. The prospects for permanent implementation of the D-DVSI will be discussed.

In addition, initial work on an innovative diagnostic based on the Lighthill-Ford theory of spontaneous adjustment to balance will also be discussed. This work, in conjunction with Paul Williams of the University of Reading, links experimental fluid mechanics work with rigorous dynamical theory to develop an operationally usable CAT diagnostic. The background for this approach, as well as any initial results, will be presented.