The high-latitude winter atmospheric boundary layer of interior Alaska continually exhibits a complex layered structure as a result of extreme meteorological conditions. In this paper the occurrence of elevated inversions (EI), surface-based inversions (SBI), and stratified layers in the sub-Arctic from January 2000 to December 2009 is reported. This statistical analysis is based on radiosonde observation data from the Fairbanks National Weather Service station complemented by Winter Boundary Layer Experiment observations in the period 2010-11. This study found that SBIs occurred 64% of the time. An SBI occurred in combination with one, two, three, or four simultaneous EIs 84.86%, 48.49%, 21.23%, and 7.99% of the time, respectively, in 2326 total cases. The calculated mean SBI height was 377 m; EIs occurred at 1231, 2125, 2720, and 3125 m, respectively. This analysis was able to discriminate between locally controlled inversion layers and synopticdependent inversions and to identify their formation mechanisms. It was found that, in the presence of an SBI layer, the first EI layer formed 35.8% of the time under anticyclonic conditions at a mean height of 1249 m and 22% of the time in warm-air-advection situations at a mean height of 1049 m. The remaining 23.4% resulted from combined synoptic situations, and 18.8% were unclassified.
The computation of turbulent fluxes of heat, momentum, and greenhouse gases requires measurements taken at high sampling frequencies. An important step in this process involves the detection and removal of sudden, short-lived variations that do not represent physical processes and that contaminate the data (i.e., spikes). The objective of this study is to assess the performance of several noteworthy despiking methodologies in order to provide a benchmark assessment and to provide a recommendation that is most applicable to high-frequency micrometeorological data in terms of efficiency and simplicity. The performance of a statistical time window–based algorithm widely used in micrometeorology is compared to three other methodologies (phase space, wavelet based, and median filter). These algorithms are first applied to a synthetic signal (a clean reference version and then one with spikes) in order to assess general performance. Afterward, testing is done on a time series of actual CO2 concentrations that contains extreme systematic spikes every hour owing to instrument interference, as well as several smaller random spike points. The study finds that the median filter and wavelet threshold methods are most reliable, and that their performance by far exceeds statistical time window–based methodologies that use the median or arithmetic mean operator (−34% and −71% reduced root-mean-square deviation, respectively). Overall, the median filter is recommended, as it is most easily automatable for a variety of micrometeorological data types, including data with missing points and low-frequency coherent turbulence.
Scintillometer measurements of the turbulence inner-scale length lo and refractive index structure function C 2 n allow for the retrieval of large-scale areaaveraged turbulent fluxes in the atmospheric surface layer. This retrieval involves the solution of the non-linear set of equations defined by the Monin-Obukhov similarity hypothesis. A new method that uses an analytic solution to the set of equations is presented, which leads to a stable and efficient numerical method of computation that has the potential of eliminating computational error. Mathematical expressions are derived that map out the sensitivity of the turbulent flux measurements to uncertainties in source measurements such as lo. These sensitivity functions differ from results in the previous literature; the reasons for the differences are explored.
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