Inquiry into the appropriatenessof a TILE/MOSAIC approach

for the representation of surface inhomogeneities

B. Ritter and J. Helmert

• Objective

• Concept of aggregation/disaggregation

• Pro&Con of TILE/MOSAIC

• Options of TILE and MOSAIC

• Implications for global and limited area NWP models

Outline

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)

if

if1

1

i

N

i

f

Nfi

1

Objective

Coupling of coarse atmosphere and high resolution surface

E.g. Latent Heat Flux for one patch :

atmospheric variables

surface variablesGrid box average

Objective

• Disaggregation: fluxes directed to the surface

(downw. Radiation, Precipitation)

• Aggregation: fluxes from the surface to

the atmosphere (upw. Radiation, turb. Fluxes)

Concept

• Disaggregation: Tiling shortwave radiation

Gridbox value of net shortwave radiation (radiation scheme) Snet

Broadband (or spectral) albedo for each tile

netineti

N

i

SSf

,1

Energy conservation

Concept

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

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

MOSAIC versus TILE approach

preference for tile approach(Figure taken from

Ament, 2006)

Options of TILE & MOSAIC

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

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

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

AROME (SURFEX)4 tiles: nature , town, sea, inland water

Nature: ISBA 3L (Boone et al 1999)1L snow scheme

(Douville, 1995)TownSea, inland water: constant T_s, Charnock formulaUM (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 tilesHigh vegetation, low vegetation, interception reservoir, bare ground, snow on ground and low vegetation,Snow under high vegetation

Implications for NWP

Implications for NWP