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| Titel |
TopoSCALE v.1.0: downscaling gridded climate data in complex terrain |
| VerfasserIn |
J. Fiddes, S. Gruber |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 7, no. 1 ; Nr. 7, no. 1 (2014-02-21), S.387-405 |
| Datensatznummer |
250115548
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| Publikation (Nr.) |
 copernicus.org/gmd-7-387-2014.pdf |
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| Zusammenfassung |
Simulation of land surface processes is problematic in heterogeneous
terrain due to the the high resolution required of model grids to
capture strong lateral variability caused by, for example, topography, and the
lack of accurate meteorological forcing data at the site or scale it
is required. Gridded data products produced by atmospheric models
can fill this gap, however, often not at an appropriate spatial
resolution to drive land-surface simulations. In this study we
describe a method that uses the well-resolved description of the atmospheric column provided by climate models, together with high-resolution digital elevation models (DEMs), to downscale coarse-grid climate variables to a fine-scale subgrid. The main aim of this approach is to provide high-resolution driving data for a land-surface model (LSM).
The method makes use of an interpolation of pressure-level data according to
topographic height of the subgrid. An elevation and topography correction is
used to downscale short-wave radiation. Long-wave radiation is downscaled by
deriving a cloud-component of all-sky emissivity at grid level and using
downscaled temperature and relative humidity fields to describe variability
with elevation. Precipitation is downscaled with a simple non-linear lapse
and optionally disaggregated using a climatology approach.
We test the method in comparison with unscaled grid-level data and a set of
reference
methods, against a large evaluation dataset (up to 210 stations per
variable) in the Swiss Alps. We demonstrate that the method can be
used to derive meteorological inputs in complex terrain, with most
significant improvements (with respect to reference methods) seen in
variables derived from pressure levels: air temperature, relative
humidity, wind speed and incoming long-wave radiation.
This method may be of use in improving inputs to numerical simulations in
heterogeneous and/or remote terrain, especially when statistical methods are
not possible, due to lack of observations (i.e. remote areas or future
periods). |
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