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Inquiry into the appropriateness of a TILE/MOSAIC approach for the representation of surface inhomogeneities B. Ritter and J. Helmert
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Objective Concept of aggregation/disaggregation Pro&Con of TILE/MOSAIC Options of TILE and MOSAIC Implications for global and limited area NWP models Outline
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Account for non-linear effects of sub-grid inhomegeneities at surface on the exchange of energy and moisture between atmosphere and surface (cf. Ament&Simmer, 2006) mosaic approach surface divided in N subgrid cells tile approach N dominant classes (e.g. water, snow, grass) (Figure taken from Ament&Simmer, 2006) Objective
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Coupling of coarse atmosphere and high resolution surface E.g. Latent Heat Flux for one patch : atmospheric variables surface variables Grid box average Objective
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Disaggregation: fluxes directed to the surface (downw. Radiation, Precipitation) Aggregation: fluxes from the surface to the atmosphere (upw. Radiation, turb. Fluxes) Concept
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Disaggregation: Tiling shortwave radiation Gridbox value of net shortwave radiation (radiation scheme) S net Broadband (or spectral) albedo for each tile Energy conservation Concept
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Pro & Con of TILE/MOSAIC Con: increase in computational effort & complexity additional requirements for external parameter software uncertainty with regard to suitable ‚blending height & depth‘ Pro: unsatisfactory handling of situations like snow melting phase (partial snow cover) of current approach should be alleviated simple integration of submodels (e.g. Flake, Urban) self-adaptation to model resolution
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Options of TILE & MOSAIC MOSAIC (i.e. explicit sub-grid approach) initial selection of resolution enhancement factor, independent of heterogeneity resp. homogeneity of underlying surface sub-optimal self-adaptation to atmospheric model resolution unnecessary computational burden over homogeneous terrain (Stoll et al., 2010) TILE (i.e. weighted averaging of contributions from flexible number of surface classes) self-adaption to atmospheric model resolution and heterogeneity of surface occurs automatically computational burden adjusts to required number of surface classes
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MOSAIC versus TILE approach preference for tile approach (Figure taken from Ament, 2006) Options of TILE & MOSAIC
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Blending height ‚standard‘: assume homogeneity of atmosphere at lowest atmospheric level alternative: allow heterogeneity also in atmosphere near surface (e.g. downscaling/disaggregation of atmospheric variables at the lowest model level; cf. Schomburg et al., 2009) The ‚standard‘ approach creates neither technical problems nor computational overhead, but may not be justified in situations with large surface heterogeneities. A ‚downscaling‘ approach in the spirit of Schomburg et al. may alleviate this problem. Options of TILE & MOSAIC
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Downscaling system for atmospheric variables on lowest model level (A. Schomburg) (Schomburg et al., 2009) 3 Steps: 1. Smooth field with splines 2. Downscale field “deterministically“by regression techniques (use subgrid-scale information like orography) 3. Add noise to reproduce original fine-scale variance If no rules for step 2. can be found (e.g. for precipitation) apply only step 1. and 3 Options of TILE & MOSAIC
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Blending depth A proper tile/mosaic approach requires the simulation of soil internal processes like heat conduction for each indivual class resp.sub-cell Assuming homogeneous conditions within the soil (e.g. ECMWF IFS) leads to a major simplification and saving of computational ressources but is hardly justifiable. In particular in the framework of DWD‘s multi- layer soil model with a top layer depth of only 1 cm, it appears to be a rather crude and unrealistic assumption. implement tile approach in a consistent manner for all soil layers Options of TILE & MOSAIC
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Implemention of tile approach requires: development and implementation of corresponding extensions in external parameter software (i.e. landuse dependend parameters for a no. of dominant classes within each atmospheric grid cell) code structure to support multiple ‚soil columns‘ within each grid cell (TERRA adaptions in COLOBOC) physics interface routine or multi-layer soil model, which controls the computation over (flexible) number of classes within each cell and performs necessary aggregation (&disaggregation) suitable diagnostics (within soil model) to allow proper validation of tile scheme a computationally efficient and flexible implementation (vectorisation?) Implications for NWP
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AROME (SURFEX) 4 tiles: nature, town, sea, inland water Nature: ISBA 3L (Boone et al 1999) 1L snow scheme (Douville, 1995) Town Sea, inland water: constant T_s, Charnock formula UM (Jules) 9 tiles, 5 veg + 4 non-veg Broadleaf and needleleaf trees, temperate and tropical grasses, Shrubs, urban, inland water, bare soil, land ice. IFS (HTESSEL) 6 land-surface tiles High vegetation, low vegetation, interception reservoir, bare ground, snow on ground and low vegetation, Snow under high vegetation Implications for NWP
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