Trade-offs and responsiveness of the single-layer urban canopy parametrization in WRF: An offline evaluation using the MOSCEM optimization algorithm and field observations

Abstract

For an increasing number of applications, mesoscale modelling systems now aim to better represent urban areas. The complexity of processes resolved by urban parametrization schemes varies with the application. The concept of fitness-forpurpose is therefore critical for both the choice of parametrizations and the way in which the scheme should be evaluated. A systematic and objective model response analysis procedure (Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm) is used to assess the fitness of the single-layer urban canopy parametrization implemented in the Weather Research and Forecasting (WRF) model. The scheme is evaluated regarding its ability to simulate observed surface energy fluxes and the sensitivity to input parameters. Recent amendments are described, focussing on features which improve its applicability to numerical weather prediction, such as a reduced and physically more meaningful list of input parameters. The study shows a high sensitivity of the scheme to parameters characterizing roof properties in contrast to a low response to road-related ones. Problems in partitioning of energy between turbulent sensible and latent heat fluxes are also emphasized. Some initial guidelines to prioritize efforts to obtain urban land-cover class characteristics in WRF are provided.

Multi‐site evaluation of an urban land‐surface model: intra‐urban heterogeneity, seasonality and parameter complexity requirements

“…This results in a total of 27 evaluation periods. All state variables are initialized as in Loridan et al (2010). The soil is assumed to be 'clay-loam' (Chen and Dudhia, 2001, their Table II) with an initial soil moisture content corresponding to field capacity.…”

“…thermal conductivities, heat capacities, albedo and emissivity values: Chen et al, 2011). Of the 30 urban parameters required by the SLUCM (Table III, Loridan et al, 2010), 20 have their values modified in this evaluation depending on the stage considered. For all stages the empirical constant a K , which relates the roughness length for heat to that of momentum (Kanda et al, 2007) is fixed to 1.29; and the relative portion of material layers are kept at their default (but total material depth varies).…”

“…SLUCM runs using these optimum parameter values and site-specific f urb are referred to as Stage 4i (Table I) in the model performance analysis (section 3). The optimization windows (minimum and maximum thresholds) are the same as Loridan et al (2010, their Table III) (Table IV). As building height varies, the height of forcing level is adapted to maintain the same absolute distance between Z R and the measurement height.…”

A comprehensive assessment of land surface-atmosphere interactions in a WRF/Urban modeling system for Indianapolis, IN

“…The inaccuracies have been well documented (Best and Grimmond, 2015) and there have been studies done that have utilized parameterization techniques and different model schemes to try and get better agreement between the model and the observations (e.g. Loridan and Grimmond, 2012;Loridan et al, 2010). Loridan and Grimmond (2012) conducted a study using an offline Noah/SLUCM model to assess errors across 27 observational datasets, which represented 15 different urban sites at different times of the year.…”

Simulating the surface energy balance over two contrasting urban environments using the Community Land Model Urban

“…In addition, the two root distribution parameters r a and r b that are used to determine the root fraction for each soil layer (see Equation 8.21 in Oleson et al, 2010c) are set to 11 and 3, respectively, to get a minimum root fraction at the maximum root depth as described in Peel et al (2005). The parameters described in Table 4 also correspond to the parameters that have the strongest effect on latent heat fluxes as suggested in Loridan et al (2010). Nonetheless, in their approach they optimize the parameters with respect to the performance of some targeted fluxes (in this case Qe) instead of using the best available and realistic information on a specific vegetation type.…”