Cross-section analyses computed from the dropsonde data showed vertically propagating mesoscale gravity waves in the region of strong vertical wind shear extending from the jet core (and its cyclonic side) into the lower stratosphere. Coherent streaks of moderate or greater turbulence were indicated here as well by a diagnostic turbulent kinetic energy (TKE) parameter applied to the dropsonde data. The G-IV did, in fact, encounter turbulence in this same region on the three lower legs of the stack. Diagnosed TKE in this same region appeared in the RUC simulation fields as mesoscale bands oriented parallel to the flow. Detailed comparisons made between flight-level observations and the RUC fields showed that the general patterns seen in the aircraft data were well represented by RUC, but that variations occurring at scales smaller than ~150 km (e.g., gravity waves) were absent in the model.
Rapid fluctuations in ozone measured by the G-IV correlated nicely with fluctuations in the potential temperature data at the 10.1 km altitude, as the aircraft penetrated a pronounced gravity wave within an upper-tropospheric frontal zone. The ozone variations also correlated well in a more general sense to potential vorticity variations in the RUC model. Spectral analyses of the aircraft 25-Hz data revealed that turbulence occurred in conjunction with this gravity wave. Phase spectrum analyses showed that potential temperature and the jet-normal wind component exhibited a strong in-phase relationship in the frequency range of 0.021 – 0.049 Hz, as did the potential temperature and ozone data – suggesting the presence of either deep propagating gravity waves or decaying (evanescent) waves. Synthesis of these analyses suggests the following hypothesis: turbulence related to energetic fluctuations in the inertial subrange (horizontal wavelength < 400 m) occurred within a packet of gravity waves (with wavelengths of 6 – 80 km) shed within an upper-level front on the cyclonic shear side of the jet core. The challenge now is to attempt to model and understand these features using a cloud-resolving model.
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