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Intermittency is the uneven concentration of turbulent fluctuations or dissipation into relatively intense events separated by quieter regions or intervals. It often appears statistically as increasingly non-Gaussian velocity or magnetic-field increments at smaller separations. A turbulent signal can therefore have a moderate average variance while rare events dominate its higher moments.^[ref]

One useful diagnostic is the increment flatnessusing the same structure function component and convention in both moments. A centered Gaussian has $F=3$. Increasing $F$ towards small scales indicates increasingly strong relative tails, although measurement noise and nonstationary mixtures also need checking.

A toy distribution isolates the rare-event effect. Let $X$ vanish with probability $1-p$ and take values $+\sigma /\sqrt{p}$ and $-\sigma /\sqrt{p}$ with probability $p/2$ each. ThenAt $p=0.05$, the variance is still ${\sigma }^2$ but the flatness is $20$. This is an illustrative marginal distribution, not a construction of a complete turbulent field.

Spacecraft measurements provide a concrete application. Analysis of Helios 2 magnetic and velocity records compared increments at different lags and heliocentric distances, finding scale-dependent flatness in both fast and slow solar wind. Mapping time lag to spatial separation requires an advection assumption. Flatness characterizes the fluctuations; by itself it does not establish the sign or magnitude of an energy cascade.^[ref]

Luca Sorriso-Valvo is a physicist who studies turbulence in space plasmas, particularly the solar wind. His CNR institutional profile lists these specialties and publications connecting spacecraft observations to the statistical transfer of energy between scales.^[ref]

His 1999 first-authored paper examined the probability density functions of solar wind speed and magnetic field increments. Small-scale fluctuations departed strongly from a Gaussian distribution. The team fitted a model that mixes Gaussian distributions whose variances follow a log-normal distribution; a scale-dependent width then quantified how the distribution changed.^[ref]

The idea has a simple interpretation. Many quiet intervals mixed with occasional highly variable intervals can produce a distribution with more extreme events than one Gaussian predicts. A good fit does not uniquely prove the underlying mechanism, but following its parameter across scales makes intermittency quantitatively testable. Together with his later work on the energy cascade, this connects the visible irregularity of a time series to specific statistical questions about the flow.