How long is long enough when measuring fluxes and other turbulence statistics?

These expressions show that the systematic error and the error variance in the limit of large T are both inversely proportional to T which means that the random error, i.e. the square root of the error variance, will in this limit be larger than the systematic error. We demonstrate theoretically, as well as experimentally with aircraft data from the convective boundary layer over the ocean and over land, that the assumption that the time series are Gaussian leads to underestimation of the random errors, while derived processes with a more realistic skewness and kurtosis give better estimates.

For fluxes we estimate the systematic and random errors when the time series are sampled instantaneously, but the samples separated in time by an amount ?. We find that the random error variance and the systematic error increase by less than 8% over continuously-sampled data if ? is no larger than the integral scale obtained from the flux time series and the co-spectrum, respectively.