A downscaling scheme for atmospheric variables to drive soil–vegetation–atmosphere transfer models

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Schomburg, A. ; Venema, V. ; Lindau, R. ; Ament, F. ; Simmer, C. (2011)
  • Publisher: Tellus B
  • Journal: Tellus B (issn: 1600-0889)
  • Related identifiers: doi: 10.3402/tellusb.v62i4.16550
  • Subject:
    arxiv: Physics::Atmospheric and Oceanic Physics

For driving soil–vegetation–transfer models or hydrological models, high-resolution atmospheric forcing data is needed. For most applications the resolution of atmospheric model output is too coarse. To avoid biases due to the non-linear processes, a downscaling system should predict the unresolved variability of the atmospheric forcing. For this purpose we derived a disaggregation system consisting of three steps: (1) a bi-quadratic spline-interpolation of the low-resolution data, (2) a so-called ‘deterministic’ part, based on statistical rules between high-resolution surface variables and the desired atmospheric near-surface variables and (3) an autoregressive noise-generation step. The disaggregation system has been developed and tested based on high-resolution model output (400 m horizontal grid spacing). A novel automatic search-algorithm has been developed for deriving the deterministic downscaling rules of step 2. When applied to the atmospheric variables of the lowest layer of the atmospheric COSMO-model, the disaggregation is able to adequately reconstruct the reference fields. Applying downscaling step 1 and 2, root mean square errors are decreased. Step 3 finally leads to a close match of the subgrid variability and temporal autocorrelation with the reference fields. The scheme can be applied to the output of atmospheric models, both for stand-alone offline simulations, and a fully coupled model system.DOI: 10.1111/j.1600-0889.2010.00466.x
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