The microscale obstacle-resolving meteorological model MITRAS v2.0: model theory

Abstract

This paper describes the developing theory and underlying processes of the microscale obstacle-resolving model MITRAS version 2. MITRAS calculates wind, temperature, humidity, and precipitation fields, as well as transport within the obstacle layer using Reynolds averaging. It explicitly resolves obstacles, including buildings and overhanging obstacles, to consider their aerodynamic and thermodynamic effects. Buildings are represented by impermeable grid cells at the building positions so that the wind speed vanishes in these grid cells. Wall functions are used to calculate appropriate turbulent fluxes. Most exchange processes at the obstacle surfaces are considered in MITRAS, including turbulent and radiative processes, in order to obtain an accurate surface temperature. MITRAS is also able to simulate the effect of wind turbines. They are parameterized using the actuator-disk concept to account for the reduction in wind speed. The turbulence generation in the wake of a wind turbine is parameterized by adding an additional part to the turbulence mechanical production term in the turbulent kinetic energy equation. Effects of trees are considered explicitly, including the wind speed reduction, turbulence production, and dissipation due to drag forces from plant foliage elements, as well as the radiation absorption and shading. The paper provides not only documentation of the model dynamics and numerical framework but also a solid foundation for future microscale model extensions.Published by Copernicus Publications on behalf of the European Geosciences Union.

Development of an atmospheric chemistry model coupled to the PALM model system 6.0: implementation and first applications

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“…KPP offers a variety of numerical solvers for the system of coupled ordinary differential equations describing the chemical reactions. Tests comparing the performance of the Rosenbrock solvers implemented in KPP have shown that the use of the most simple Rosenbrock solver, Ros-2, did not lead to significantly different results than the use of the Rosenbrock solvers with higher orders (Sandu and Sander, 2006;Jöckel et al, 2010). Therefore, the Ros-2 solver was chosen as the default solver for the PALM-4U chemistry model.…”

Semi-Lagrangian Subgrid Reconstruction for Advection-Dominant Multiscale Problems with Rough Data

“…It is our goal to avoid such assumptions keeping in mind that processes that are advectively driven by flows in climate and weather applications have divergence, do not have mean zero and do not exhibit a scale separation. Yet, they dominate many parameterized processes that can manifest in precomputed diffusion coefficients coming from other sources, e.g., see [33,34] for a simulation environment of urban areas and canopies.…”

City-descriptive input data for urban climate models: Model requirements, data sources and challenges

“…Masson (2006) describes several types of UCM according to their degree of complexity and realism, and Grimmond et al (2010Grimmond et al ( , 2011 further classify each physical component of these schemes, underlining the need for data to describe the city. When going to the microscale, individual buildings are explicitly resolved by Obstacle Resolving Models (ORMs) (Krayenhoff et al, 2015;Resler et al, 2017;Salim et al, 2018). These ORMs create a high demand for detailed input data.…”