An improved clear air turbulence diagnostic index to account for unbalanced flow in anticyclonically curved jet streams
Gary P. Ellrod, NOAA/NESDIS, Camp Springs, MD; and J. A. Knox
Numerous diagnostic indices have been developed to help forecast the likelihood of high altitude clear-air turbulence (CAT) near the jet stream. A significant shortcoming of many of the indices is that they do not account for unbalanced (i.e. ageostrophic) flow conditions that often occur near upper level ridges on the anticyclonic side of the jet stream (Knox 1997). These non-classical CAT scenarios are characterized by strong vertical and anticyclonic horizontal wind shears that generate gravity waves through the process of geostrophic adjustment (inertial instability). The gravity waves can modulate local vertical wind shears, leading to intermittent but strong turbulence over large areas, which are sometimes distinguishable by transverse cirrus bands in satellite imagery. Studies have been conducted to describe and diagnose unbalanced flow CAT conditions using upper wind data (Knox 1997; McCann 2001). Of six possible parameters examined by McCann, the divergence tendency was found to have the highest correlation with moderate or greater aircraft turbulence. This paper describes efforts to improve a deformation - vertical wind shear CAT index (DVSI) (Ellrod and Knapp 1992) by adding a divergence trend term to account for unbalanced, anticyclonic jet streams. Data used were from the North American Model (NAM, formerly the ETA) and the 20km Rapid Update Cycle (RUC2). The scaled six hour change in divergence was added to the DVSI and run in parallel with the operational version to compare with observed turbulence reports. DVSI maxima were enhanced considerably in strong anticyclonic conditions by including the divergence trend, and related better with turbulence reports. In other regions, there was no negative impact on the index. The greatest benefits so far have occurred using the lower resolution NAM data, since divergence fields from the RUC2 data have been observed to be “noisy.” Several examples will be shown, supported by divergence calculated from GOES cloud motion vectors.
References: Ellrod, G. P. and D. I. Knapp, 1992: An objective clear-air turbulence forecasting technique: Verification and operational use. Wea. Forecasting, 7, 150-165.
Knox, J. A., 1997: Possible mechanisms of clear-air turbulence in strongly anticyclonic flow. Mon. Wea. Rev., 125, 1251-1259.
McCann, D. W., 2001: Gravity waves, unbalanced flow, and clear air turbulence. Nat. Wea. Digest, 25(1,2), 3-14.
Extended Abstract (524K)
Poster Session 1, Conference Posters
Monday, 1 August 2005, 5:30 PM-7:00 PM, Regency Ballroom
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