In this paper, we describe the PALM model system 6.0. PALM (formerly an abbreviation for Parallelized Large-eddy Simulation Model and now an independent name) is a Fortran-based code and has been applied for studying a variety of atmospheric and oceanic boundary layers for about 20 years. The model is optimized for use on massively parallel computer architectures. This is a follow-up paper to the PALM 4.0 model description in Maronga et al. (2015). During the last years, PALM has been significantly improved and now offers a variety of new components. In particular, much effort was made to enhance the model with components needed for applications in urban environments, like fully interactive land surface and radiation schemes, chemistry, and an indoor model. This paper serves as an overview paper of the PALM 6.0 model system and we describe its current model core. The individual components for urban applications, case studies, validation runs, and issues with suitable input data are presented and discussed in a series of companion papers in this special issue.

In the frame of the project “MOSAIK – Model-based city planning and application in climate change”, a German-wide research project within the call “Urban Climate Under Change” ([UC]²) funded by the German Federal Ministry of Education and Research (BMBF), a biometeorology module was implemented into the Parallelized Large-Eddy Simulation Model (PALM) system. The new biometeorology module is comprised of methods for the calculation of UV-exposure quantities, a human–biometeorologically weighted mean radiant temperature (T_mrt), as well as for the estimation of human thermal comfort or stress. The latter is achieved through the implementation of the three widely used thermal indices: perceived temperature (PT), Universal Thermal Climate Index (UTCI), as well as physiologically equivalent temperature (PET). Comparison calculations were performed for the PT, UTCI and PET indices based on the SkyHelios model and showing PALM calculates higher values in general. This is mostly due to a higher radiational gain leading to higher values of mean radiant temperature. For a more direct comparison, the PT, PET and UTCI indices were calculated by the biometeorology module, as well as the programs provided by the attachment to Verein Deutscher Ingenieure (VDI) guideline 3787, as well as by the RayMan model based on the very same input dataset. Results show deviations below the relevant precision of 0.1 K for PET and UTCI and some deviations of up to 2.683 K for PT caused by repeated unfavorable rounding in very rare cases (0.027 %).

Thermal perception and stress for humans can be best estimated based on appropriate indices. Sophisticated thermal indices, e.g., the Perceived Temperature (PT), the Universal Thermal Climate Index (UTCI), or the Physiologically Equivalent Temperature (PET) do require the meteorological input parameters air temperature ( $T_a$ ), vapour pressure ( $VP$ ), wind speed (v), as well as the different short- and longtime radiation fluxes summarized as the mean radiant temperature ( $T_{mrt}$ ). However, in complex urban environments, especially v and $T_{mrt}$ are highly volatile in space. They can, thus, only be estimated by micro-scale models. One easy way to apply the model for the determination of thermal indices within urban environments is the advanced SkyHelios model. It is designed to estimate sky view factor ( $SVF$ ), sunshine duration, global radiation, wind speed, wind direction, $T_{mrt}$ considering reflections, as well as the three thermal indices PT, UTCI, and PET spatially and temporarily resolved with low computation time.

The effect of weather on sport events is largely discussed in the sports medicine and exercise physiology context. It is important, both for event organizers and for medical staff, to know whether the competition is happening at a time and place with extreme weather or in general not appropriate weather and climatic conditions. In order to find out, whether a place or time is appropriate, two factors should be included when establishing the effect of atmospheric conditions on visitors and athletes. These are the main climatic conditions, based on long term data, and the quantification of extreme events, like heat waves. The present analysis aims at determining what kind of data are required for an appropriate quantification of weather and climate thermal stress. For the analysis, indices like Physiologically Equivalent Temperature (PET) and mPET (modified PET) are applied. The advantage of these indices is the consideration of both, thermo-physiological and meteorological factors to provide results and information that can be used for decision making. In this paper, we analyzed the Tokyo area with regards to the upcoming Tokyo 2020 Summer Olympic Games. The results show that this kind of event may not be appropriate for visitors, if it is placed during months with extreme conditions. For Tokyo, this is the period from May to September, when conditions cause strong heat stress to the visitors for the vast majority of hours of the day. A more appropriate time would be the months from November to February or the early morning and the late afternoon hours, when thermally comfortable conditions are much more frequent. The methods that are applied here can quantify the thermal conditions and show limitations and possibilities for specific events and locations. Should the organizers still want to have these competitions organized during these months with extreme conditions, they should promote and propose all possible countermeasures for the spectators, workforce, and athletes.