Mesoscale nesting interface of the PALM model system 6.0

Abstract

Abstract. In this paper, we present a newly developed mesoscale nesting interface for the PALM model system 6.0, which enables PALM to simulate the atmospheric boundary layer under spatially heterogeneous and non-stationary synoptic conditions. The implemented nesting interface, which is currently tailored to the mesoscale model COSMO, consists of two major parts: (i) the preprocessor INIFOR (initialization and forcing), which provides initial and time-dependent boundary conditions from mesoscale model output, and (ii) PALM's internal routines for reading the provided forcing data and superimposing synthetic turbulence to accelerate the transition to a fully developed turbulent atmospheric boundary layer. We describe in detail the conversion between the sets of prognostic variables, transformations between model coordinate systems, as well as data interpolation onto PALM's grid, which are carried out by INIFOR. Furthermore, we describe PALM's internal usage of the provided forcing data, which, besides the temporal interpolation of boundary conditions and removal of any residual divergence, includes the generation of stability-dependent synthetic turbulence at the inflow boundaries in order to accelerate the transition from the turbulence-free mesoscale solution to a resolved turbulent flow. We demonstrate and evaluate the nesting interface by means of a semi-idealized benchmark case. We carried out a large-eddy simulation (LES) of an evolving convective boundary layer on a clear-sky spring day. Besides verifying that changes in the inflow conditions enter into and successively propagate through the PALM domain, we focus our analysis on the effectiveness of the synthetic turbulence generation. By analysing various turbulence statistics, we show that the inflow in the present case is fully adjusted after having propagated for about two to three eddy-turnover times downstream, which corresponds well to other state-of-the-art methods for turbulence generation. Furthermore, we observe that numerical artefacts in the form of grid-scale convective structures in the mesoscale model enter the PALM domain, biasing the location of the turbulent up- and downdrafts in the LES. With these findings presented, we aim to verify the mesoscale nesting approach implemented in PALM, point out specific shortcomings, and build a baseline for future improvements and developments.

Multiscale meteorological controls and impact of soil moisture heterogeneity on radiation fog in complex terrain

“…Future fog studies should consider using non-cyclic boundary conditions from NWP models to conduct more realistic fog simulations. This can be achieved using the offline nesting feature embedded in PALM using tools such as WRF4PALM (Lin et al, 2021) and INIFOR (Kadasch et al, 2021). In addition, idealised simulations similar to those configured in Maronga and Bosveld (2017) should be carried out along with observational data to provide greater insight into the impact of soil moisture heterogeneity on fog development and to support the findings of this study.…”

Challenges of high-fidelity air quality modeling in urban environments – PALM sensitivity study during stable conditions

“…They include e.g. the land surface model (LSM; Gehrke et al, 2021), the building surface model (BSM; Resler et al, 2017;Maronga et al, 2020), the radiative transfer model and the plant canopy model (RTM and PCM; Krč et al, 2021), online nesting Hellsten et al (2021), mesoscale nesting Kadasch et al (2021), and the chemical transport model (Khan et al, 2021).…”

Crowdsourcing air temperature data for the evaluation of the urban microscale model PALM—a case study in central Europe

“…For this purpose, the synthetic turbulence generator was applied. It is based on a method by Xie and Castro [46] which adds perturbations to the wind components at the lateral boundaries [47].…”

“…A detailed description of the model is given by Maronga et al [20]. Further information on the model components can be found in Gehrke et al [41] for the LSM, Resler et al [42] for the BSM, Salim et al [43] for the RTM, Kadasch et al [47] for the offline nesting and Hellsten et al [48] for the online nesting. The mesoscale simulation was run on 126 CPU cores.…”