Model resolution

The refinements made to the entire model setup include a higher model resolution, the implementation of most recent ECH AM, MPIOM and HAMOCC versions, the usage of assimilated satelhte data for surface phytoplankton distribution, and the usage of a more realistic description of sinking organic matter in the ocean. 

With the abovementioned technique Hendriks et al. [30] estimated that road dust resuspension contribute around 10-15% of modelled PM10 on a European scale and up to 30% in densely populated area of South Europe (Fig. 5). These estimates are relative to the modelled concentrations, which are lower than observed ones due to uncertainties in primary organic emissions and the lack of secondary organic compounds. However, this overestimate is probably compensated by the fact that in the model, peak contributions (in cities) are not captured due to the model resolution (25 x 25 km). 

The model resolution and its evaluation in order to give the models the requested exits in their relations with the entries. 

It is known that the boundary layer in the urban areas has a complex structure due to multiple contributions of different parameters, including variability in roughness and fluxes, etc. All these effects can be included to some extend into models. For research purposes, the DMI-HIRLAM-UOl/IOl models (resolution of 1.4 km) with domains shown in Fig. 16.2 are employed for high resolution urbanized modelling (example is shown in Fig. 16.6b). The land-use classification is based on CORINE dataset (http //etc-lusi.eionet.europa.eu/CLC2000) and climate generation files. 

The estimated model parameters are the wt ighled averages of t.hc true model parameters, where the weights are deti rmined l.)v the rows of the modt l resolution matrix. In the case when R, I. llu model ])aranietcrs are exactly determined. Like the data re.solutioii matrix, tlu model resolution matiix is corntiletely dt termined l)y the matrix of the opt iator of the forward problem. 

Different parameterisations of the urban sublayer for NWP and urban-scale meteorological models have been analysed and several options for the integrated FUMAPEX urban module usable with NWP models have been suggested (Baklanov et al., 2005 [38]). The first version of the module includes three main submodels, which can be chosen depending on the specific problem, model resolution or city area ... 

A critical issue is the proper choice of the horizontal and vertical model resolution. The baroclinic Rossby Radius is a typical horizontal scale for eddies and wavelike processes. If this scale, that varies in the Baltic from less than 1 km to 7 km (Fennel et al., 1991), is not resolved, adjustment processes by waves are filtered out and the model dynamics is... 

The quality of numerical simulations is limited by the horizontal and vertical model resolution. An obvious aspect of enhanced model resolution is the better representation of the bathymetry. Especially the structured coastline of the Danish Straits and the sills between the basins of the Baltic Sea are represented more accurately. Another aspect is the representation of processes with small characteristic dynamic scales in the model. Although in many cases details on unresolved processes are not needed, parameterizations for such processes are sources of numerical uncertainty. Enhanced model resolution means less parameterization and the model dynamics is represented preferably by the hydrodynamic equations. 

Horizontal and vertical advection of heat and dissolved substances implies numerical mixing, which is the less important the smaller the grid cell volume is. Hence, abetter model resolution conserves the signamre of inflowing saltwater, which is essential for the simulation of bottom water renewal in the deep basins of the Baltic Sea. Also for the representation of frontal or river plume dynamics, a sufficient model resolution is required. 

To illustrate the effects of the enhancement of the model resolution, we show an example... 

Nested grid capability This is required for multiscale simulations with a very fine model resolution over the areas of interest. 

In this paragraph the wall function concept is outlined. The wall functions are empirical parameterizations of the mean flow variable profiles within the inner part of the wall boundary layers, bridging the fully developed turbulent log-law flow quantities with the wall through the viscous and buffer sublayers where the two-equation turbulence model is strictly not valid. These empirical parameterizations thus allow the numerical flow simulation to be carried out with a finite resolution within the wall boundary layers, and one avoids accounting for viscous effects in the model equations. Therefore, in the numerical implementation of the k-e model one anticipates that the boundary layer flow is not fully resolved by the model resolution. The first grid point or node used at a wall boundary is thus placed within the fully turbulent log-law sub-layer, rather than on the wall itself [95]. In effect, the wall functions amount to a synthetic boundary condition for the k-e model. In addition, the limited boundary layer resolution required also provides savings on computer time and storage. 

Sirignano WA (1986) The Formulation of Spray Combustion Models Resolution Compared to Droplet Spacing. Journal of Heat Transfer 108 633-639... 

Fig. 15 Modeled resolution of SPR configuration with linear CCD, 2D CCD, and large area photodiode array (PDA) as a function of wavelength...

Table II. Summary of Band Model Resolutions of the Np(V) Hypersensitive...

The model of Ganopolski et al. (1998) is the only true climate model according the IPCC definition as it includes all components of the climate system relevant to describe mid-Holocene climate. However, it has a rather coarse horizontal resolution. Hence to be certain of their results, one must confirm that these results are independent of the model resolution. 

The domain of an atmospheric model—that is, the area that is simulated—varies from a few hundred meters to thousands of kilometers (Table 25.1). The computational domain usually consists of an array of computational cells, each having a uniform chemical composition. The size of these cells, that is, the volume over which the predicted concentrations are averaged, determines the spatial resolution of the model. Variation of concentrations at scales smaller than the model resolution cannot easily be resolved. For example, concentration variations over the Los Angeles basin cannot be described by a synoptic scale model that treats the entire area as one computational cell of uniform chemical composition. 

The two extremes, using instantaneous and cumulative phase predictions discussed above, provide only the framework for the total variability, which can be expected in the reservoir filling history studied here. Unravelling the evolution of petroleum fluid compositions in the Snorre Field through time would require a model resolution far exceeding what can be handled in reasonable computing time. The approach shown allows, however, a prediction of fluid properties, which is much closer to the natural fluid compositions than previously possible. This compositional kinetic scheme is the first of its kind to allow reasonable petroleum phase behaviour assessment in the simulation of basin evolution and hydrocarbon migration. 

Bernauer et al. [18] introduced a knowledge-based model that doesn t employ a conventional force field. Rather, interatomic distances are constrained to stay within prescribed bounds and follow a certain position distribution. Using these distance-based potentials, there is no need to explicitly account for electrostatics or other terms, as they are already captured. Bernauer et al. have two model resolutions, a CG model and an all atom. The CG model consists of five pseudoatoms one at the phosphate, C4 of the ribose, and one on the C2, C4, and C6 atoms of the nucleobase. The knowledge-based potentials required the use of a set of presolved structures the authors chose these structures based on stringent accuracy requirements. The authors score their potentials by implementing an REMD protocol. 

The parametrization of cumulus cloud rainfall utilizes some form of one-dimensional cloud model. These are called cumulus cloud parametrization schemes. Then-complexity ranges from instantaneous readjustments of the temperature and moisture profile to the moist adiabatic lapse rates when the relative humidity exceeds saturation, to representations of a set of one-dimensional cumulus clouds with a spectra of radii. These parametrizations typically focus on deep cumulus clouds, which produce the majority of rainfall and diabatic heating associated with the phase changes of water. Cumulus cloud parametrizations remain one of the major uncertainties in mesoscale models since they usually have a number of tunable coefficients, which are used to obtain the best agreement with observations. Also, since mesoscale-model resolution is close to the scale of thunderstorms, care must be taken so that the cumulus parametrization and the resolved moist thermodynamics in the model do not double count this component of the and Sq.. 

---