The IAGL Land Surface Model

Ridder, Koen De; Schayes, G.

doi:10.1175/1520-0450(1997)036<0167:tilsm>2.0.co;2

Cited by 81 publications

(42 citation statements)

References 45 publications

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“…The land surface scheme is based on the soil-vegetation-atmosphere transfer scheme described in De Ridder and Schayes (1997). The vegetation-and soil-related processes are largely the same as in the original scheme, but the latter was extended to account for urban surface physics.…”

Section: Surface Energy Balance Parameterisationsupporting

confidence: 87%

“…(11) as those used for the urban slab. The main difference is that, for soil, the volumetric heat capacity and thermal conductivity are functions of soil moisture content, as in De Ridder and Schayes (1997). Moreover, given that the energy fluxes (21) are expressed per unit of exposed soil surface, while the soil does also extend under the vegetation, the upper boundary condition becomes…”

Section: Surface Energy Balance Parameterisationmentioning

confidence: 99%

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UrbClim – A fast urban boundary layer climate model

2015

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a b s t r a c tWe present a new urban climate model, further referred to as UrbClim, designed to cover agglomeration-scale domains at a spatial resolution of a few hundred metres. This model is composed of a land surface scheme containing simple urban physics, coupled to a 3-D atmospheric boundary layer module. In the land surface scheme, urban terrain is represented as an impermeable slab with appropriate parameter values for albedo, emissivity, and aerodynamic and thermal roughness length, and accounting for anthropogenic heat fluxes. The UrbClim model is subject to several validation exercises, using observations from Toulouse (France), Ghent and Antwerp (Belgium), and Bilbao (Spain), and considering turbulent energy fluxes, wind speed, and urban-rural temperatures. Despite its simplicity, UrbClim is found to be of the same level of accuracy than more sophisticated models, including for the complex terrain characterizing the Bilbao area. At the same time, it is faster than high-resolution mesoscale climate models by at least two orders of magnitude. Because of that, the model is well suited for long time integrations, in particular for applications in urban climate projections.

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Section: Surface Energy Balance Parameterisationsupporting

confidence: 87%

Section: Surface Energy Balance Parameterisationmentioning

confidence: 99%

UrbClim – A fast urban boundary layer climate model

2015

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“…When one is not interested in the details of the urban canopy climate, simpler schemes can be used. In the present study, we use the soil-vegetation-atmosphere transfer scheme of De Ridder and Schayes (1997), upgraded to account for urban surface physics. This 'urbanisation' was achieved basically by including an appropriate parametrization of the thermal roughness length, which may become extremely small over cities (Voogt and Grimmond 2000).…”

Section: Introductionmentioning

confidence: 99%

The Urban Heat Island Intensity of Paris: A Case Study Based on a Simple Urban Surface Parametrization

Sarkar

Ridder

2010

Boundary-Layer Meteorol

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We explore the ability of a simple urban surface parametrization, embedded in a mesoscale meteorological model, to correctly reproduce observed values of the urban heat island (UHI) intensity, which is defined as the urban-rural surface air temperature difference. To do so, a simple urban scheme was incorporated into the Advanced Regional Prediction System (ARPS). Subsequently, a simulation was performed with the coupled model over the wider area of Paris, for a 12-day period in June 2006 that was characterised by conditions prone to UHI development. Simulated 2-m air temperature was compared with observed values for urban and rural stations, yielding mean errors of 1.4 and 1.5 K, respectively. More importantly, it was found that the model also displayed an overall good capability of reproducing the observed temperature differences. In particular, the magnitude (up to 6K) and timing of the diurnal cycle of the UHI intensity was simulated well, the model exhibiting a mean error of 1.15 K. As a result, our conclusion is that the ARPS model, extended with simple urban surface physics, is able to capture observed urban-rural air temperature differences well, at least for the domain and period studied.

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“…The monthly average O 3 concentration for Mondy (derived from the 10-km resolution domain) amounts to 86 μg/m 3 ; this compares well with a measured O 3 concentration of approximately 80 μg/m 3 (averaged over the years 1997-1999Holloway et al 2008).…”

Section: Arps Validationmentioning

confidence: 99%

Air-quality modelling in the Lake Baikal region

Vel

Mensink

Ridder

et al. 2009

Environ Monit Assess

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In this paper, we assess the status of the air quality in the Lake Baikal region which is strongly influenced by the presence of anthropogenic pollution sources. We combined the local data, with global databases, remote sensing imagery and modelling tools. This approach allows to inventorise the air-polluting sources and to quantify the air-quality concentration levels in the Lake Baikal region to a reasonable level, despite the fact that local data are scarcely available. In the simulations, we focus on the month of July 2003, as for this period, validation data are available for a number of ground-based measurement stations within the Lake Baikal region.

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The IAGL Land Surface Model

Cited by 81 publications

References 45 publications

UrbClim – A fast urban boundary layer climate model

UrbClim – A fast urban boundary layer climate model

The Urban Heat Island Intensity of Paris: A Case Study Based on a Simple Urban Surface Parametrization

Air-quality modelling in the Lake Baikal region

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