Additional result D03: Validation of the PALM model system 6.0 in a real urban environment: a case study in Dejvice, Prague, the Czech Republic
The paper in a top scientific journal presents an extensive validation of the PALM model against data from a dedicated observation campaign done in a previous project Urbi-Pragensi and realized in Prague-Dejvice.
The validation focuses mainly on the thermal processes, but the air quality results were also evaluated. The results brought new information for directing of the future PALM model development. It also provided important information for the design of the observation campaign and validation accomplished inside TURBAN project in area of Legerova and Sokolska streets in Prague.
Additional result D03 is available from Resler et al.: Validation of the PALM model system 6.0 in a real urban environment: a case study in Dejvice, Prague, the Czech Republic Geoscientific Model Development, 2021. Available here and in repository.
Additional result D03: Measurement report: TURBAN observation campaign combining street-level low-cost air quality sensors and meteorological profile measurements in Prague
The paper written by Bauerova et al. was submitted into a top scientific journal Atmospheric Chemistry and Physics The manuscript describes in details observation campaign done in area of Legerova and Sokolska streets. It also shows processes and methods used for calibration of the raw sensor observations and estimation of the errors.
Additional result D03 is available at this link.
Additional result D03: Article by Resler et al.: "Challenges of high-fidelity air quality modelling in urban environments – PALM sensitivity study during stable conditions", currently under review
The paper was submitted to a top scientific journal Geoscientific Model Development. The evaluation of the PALM model simulations against observations obtained during a dedicated campaign in Legerova and Sokolska streets revealed unrealistically high concentrations of modeled air pollutants for a short period during a winter inversion episode. To identify potential reasons, the sensitivities of the model to changes of meteorological boundary conditions and adjustments of model parameters were tested. The model adaptations included adding the anthropogenic heat from cars, setting a bottom limit of the subgrid-scale TKE, adjusting the profiles of parameters of the Synthetic Turbulence Generator in PALM and limiting the model time step. The study confirmed the crucial role of the correct meteorological boundary conditions for realistic air quality modelling during stable conditions. Besides this, the studied adjustments of the model parameters proved to have a significant impact in these stable conditions, resulting in a decrease of concentration overestimation in range 30–66 % while exhibiting negligible influence on model results during the rest of the episode. This suggested that the inclusion or improvement of these processes in PALM is desirable despite their negligible impact in most other conditions. Moreover, the time step limitation test revealed numerical inaccuracies caused by discretization errors, which occurred during such extremely stable conditions.
Additional result D03 is available at this link and in repository.
Additional result D04: Radiative Transfer Model 3.0 integrated into the PALM model system 6.0
This paper in a top scientific journal Geoscientific Model Development describes the newly developed Radiative Transfer Model (RTM). RTM is an explicitly resolved three-dimensional multi-reflection radiation model integrated in the PALM modelling system. It is responsible for modelling of complex radiative interactions within the urban canopy and it represents a key component of modelling of energy processes inside the urban layer, and consequently PALM's ability to provide explicit simulations of urban canopy in meter-scale resolution. This paper describes RTM version 3.0 which is integrated in PALM modelling system version 6.0. This version of RTM has been substantially improved over previous versions with new simulated processes, providing a more realistic representation of a wider range of urban scenarios, as well as with new discretization schemes and algorithms for a significantly better scalability and computational efficiency, enabling larger parallel simulations with up to many thousands of parallel processes.
Additional result D04 is available from Krč et al.: Radiative Transfer Model 3.0 integrated into the PALM model system 6.0 Geoscientific Model Development, 2021. Available here and in repository.
Additional result D04: Article by Belda et al.: FUME 2.0 – Flexible Universal processor for Modeling Emissions
Publication available here and in repository.
Additional result D07: Sensitivity analysis of the PALM model system 6.0 in the urban environment
Sensitivity of the PALM model 6.0 with respect to land-surface and building properties is tested in a real urban environment in the vicinity of a typical crossroads in a densely built-up residential area in Prague, Czech Republic. The turbulence-resolving PALM is able to simulate the urban boundary layer flow for realistic setups. Besides an accurate representation of the relevant physical processes, the model performance also depends on the input data describing the urban setup, namely the building and land-surface properties. Two types of scenario are employed. The first one is the synthetic scenarios altering mainly surface and material parameters such as albedo, emissivity or wall conductivity, testing sensitivity of the model simulations to potentially erroneous input data. Second, urbanistic-type scenarios are analysed, in which commonly considered urban heat island mitigation measures such as greening of the streets or changing surface materials are applied in order to assess the limits of the effects of a particular type of scenario. For the synthetic scenarios, surface parameters used in radiation balance equations are found to be the most sensitive overall followed by the volumetric heat capacity and thermal conductivity of walls. Other parameters show a limited average effect. However, some can still be significant during some parts of the day, such as surface roughness in the morning hours. The second type, the urbanistic scenarios, shows urban vegetation to be the most effective measure, especially when considering both physical and biophysical temperature indicators. The influence of both types of scenario was also tested for air quality, specifically PM2.5 dispersion, which generally shows opposite behaviour to that of thermal indicators, i.e. improved thermal comfort brings deterioration of PM2.5 concentrations.
Additional result D07 is available from Belda et al.: Sensitivity analysis of the PALM model system 6.0 in the urban environment Geoscientific Model Development, 2021. Available here and in repository.
Additional result D07: Dispersion of particulate matter (PM2.5) from wood combustion for residential heating: optimization of mitigation actions based on large-eddy simulations
This study applies a Parallelized Atmospheric Large-eddy simulation Model (PALM) to investigate dynamical phenomena that control variability and pathways of the atmospheric pollution emitted by wood-burning household stoves. The model PALM runs at spatial resolution of 10 m in an urban-sized modelling domain of 29 km by 35 km with a real spatial distribution of the pollution source and with realistic surface boundary conditions that characterize a medium-sized urban area fragmented by water bodies and hills. Such complex geography is expected to favor local air quality hazards, which makes this study of general interest. The case study here is based on winter conditions in Bergen, Norway. We investigate the turbulent diffusion of a passive scalar associated with small-sized particles (PM2.5) emitted by household stoves. The study considers air pollution effects that could be observed under different policy scenarios of stove replacement; modern woodstoves emit significantly less PM2.5 than the older ones, but replacement of stoves is a costly and challenging process. This modelling study has important policy-related implications. Uneven spatial distribution of the pollutants suggests prioritizing certain limited urban districts in policy scenarios. We show that focused efforts towards stove replacement in specific areas may have a dominant positive effect on the air quality in the whole municipality. The case study identifies urban districts where limited incentives would result in the strongest reduction of the population's exposure to PM2.5.
Additional result D07 is available from Wolf et al.: Dispersion of particulate matter (PM2.5) from wood combustion for residential heating: optimization of mitigation actions based on large-eddy simulations Atmospheric Chemistry and Physics, 2021. Available here and in repository.
Additional result D11: Score matching filters for Gaussian Markov random fields with a linear model of the precision matrix
This scientific paper presents a method for combination of the meteorological model and observations. The method is based on a linear model for the precision matrix (the inverse of the covariance) with the parameters determined by Score Matching Estimation. The method provides a rigorous covariance regularization when the underlying random field is Gaussian Markov. The parameters are found by solving a system of linear equations. The analysis step uses the inverse formulation of the Kalman update. Several filter versions, differing in the construction of the analysis ensemble, are proposed, as well as a Score matching version of the Extended Kalman Filter.
Additional result D07 is available from Turčičová et al.: Score matching filters for Gaussian Markov random fields with a linear model of the precision matrix Foundations of Data Science, 2021. Available here and in repository.
Additional result D11: Article by Zhao et al.: Using clustering to understand intra-city warming in heatwaves: insights into Paris, Montreal, and Zurich
Publication available here and in repository.
Additional result D13: Article by Mahura et al.: Towards seamless environmental prediction – development of Pan-Eurasian EXperiment (PEEX) modelling platform
Publication available here and in repository.