When plane waves diffract through fractal-patterned apertures, the resulting far-field profiles or diffractals also exhibit iterated, self-similar features. Here we show that this specific architecture enables robust signal processing and spatial multiplexing: arbitrary parts of a diffractal contain sufficient information to recreate the entire original sparse signal.
The main focus of this paper is the time series analysis of the precipitation‐runoff process with transfer functions. Starting from there, a horizontal routing model is constructed to be coupled to the existing land surface parametrization (LSP) schemes which provide the lower boundary conditions in numerical weather prediction and atmospheric general circulation models. As these models currently have a resolution of 10 km−300 km (what we some kind of arbitrary define as the “large scale”), it will be assumed that the horizontal routing process can be lumped as a linear time invariant system. While the main physical properties of the soil (temperature, moisture) and all physical processes (partition of the energy and water fluxes) have to be represented by an LSP scheme, the coupling with a simple routing scheme allows the direct comparison of predicted and measured streamflow data as an integrated quantity and validation tool for both, the atmospheric and the LSP model. The main task of the routing scheme is to preserve the horizontal travel time of water within each grid box as well as from grid box to grid box in the coupled model to first order, while the correct amount of runoff must be given by the LSP scheme. Inverse calculation also allows the direct estimation of runoff which should have been produced by an LSP scheme. As we don't want to deal with snow processes the scheme is applied from February to November.
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