Description:
<jats:p>Abstract. Lagrangian particle dispersion models require interpolation of all
meteorological input variables to the position in space and time of
computational particles. The widely used model FLEXPART uses linear
interpolation for this purpose, implying that the discrete input fields
contain point values. As this is not the case for precipitation (and other
fluxes) which represent cell averages or integrals, a preprocessing scheme is
applied which ensures the conservation of the integral quantity with the
linear interpolation in FLEXPART, at least for the temporal dimension.
However, this mass conservation is not ensured per grid cell, and the scheme
thus has undesirable properties such as temporal smoothing of the
precipitation rates. Therefore, a new reconstruction algorithm was developed,
in two variants. It introduces additional supporting grid points in each time
interval and is to be used with a piecewise linear interpolation to
reconstruct the precipitation time series in FLEXPART. It fulfils the desired
requirements by preserving the integral precipitation in each time interval,
guaranteeing continuity at interval boundaries, and maintaining
non-negativity. The function values of the reconstruction algorithm at the
sub-grid and boundary grid points constitute the degrees of freedom, which
can be prescribed in various ways. With the requirements mentioned it was
possible to derive a suitable piecewise linear reconstruction. To improve the
monotonicity behaviour, two versions of a filter were also developed that
form a part of the final algorithm. Currently, the algorithm is meant
primarily for the temporal dimension. It was shown to significantly improve
the reconstruction of hourly precipitation time series from 3-hourly input
data. Preliminary considerations for the extension to additional dimensions
are also included as well as suggestions for a range of possible applications
beyond the case of precipitation in a Lagrangian particle model.
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