Abstract. 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.

City-descriptive input data for urban climate models: Model requirements, data sources and challenges Abstract 1) Introduction 1.1 Brief overview of urban atmospheric modelling 1.2 Scale issues: mesoscale and microscale 1.3 Coverage issues: from city-scale to global modelling 1.4 Fit for purpose 2) Land use and land cover classes 2.1 Description of the parameters and their relevance 2.2 Methodologies to gather land cover data 2.2.1. Remote sensing methods 2.2.2. From vector topographical databases and land registries 2.2.3. Data fusion 3) Morphological parameters 3.1 Description of the parameters and their relevance 3.2 Links between morphological parameters 3.3 Methodologies to gather morphological parameters 3.3.1 Data from remote sensing 3.3.2 GIS treatment of 2.5D cadaster vector data of individual buildings 3.3.4 Crowdsourcing or deep learning methods 4) Architectural parameters 4.1 Description of the parameters and their relevance 4.2 Developing comprehensive architectural databases 4.3 Methodologies to gather architectural information 4.3.1 Identification of representative archetypes 4.3.2 Remote sensing and image processing 4.3.3 Crowdsourcing 5) Socioeconomic data and building use 5.1 Description of the parameters and their relevance 5.2 Methodologies to gather uses, socioeconomic and anthropogenic heat parameters 5.2.1 From inventories 5.2.2 Crowdsourcing 6) Urban vegetation 6.1 Description of the parameters and their relevance 6.2 Methodologies to collect vegetation parameters at mesoscale 28 6.3 Methodologies to collect vegetation parameters at microscale 29 7) Discussion 30 7.1 Licensing issues 30 7.2 Cataloguing issues 31 7.3 Data quality 7.4 Open data 31 7.5 Research challenges for the next decade 32 7.6 From data of various origins to Urban Climate Services 33 8 Conclusions 33 Appendix 1: Overview of several global land cover data sets with an urban description 34 Acknowledgements 36 References 36

Abstract. In this paper we describe the PALM model system 6.0. PALM 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 by 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.

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The paper presents a methodology on how to consistently deal with the future change and management options in integrated water resources management (IWRM). It is based on a conceptual framework with a five step procedure for the formulation and analysis of a so-called 'parameterised regional futures'. Developing and testing the approach for IWRM is realised for the upper part of the Western Bug River catchment (Ukraine). Special attention is paid to scenarios of change covering climate and land use. The future regional climate is downscaled with the model CCLM. Land cover is projected after retrospective change detection and the derivation of prospective algorithms. Parameters of the interrelations between land use and the water cycle are tackled through using the concept of the model PWF-LU. The methodology is currently being tested to analyse the impacts of mid-term regional change and management options on the water cycle of the catchment.

Abstract. The PALM model system 6.0 is designed to simulate micro- and mesoscale flow dynamics in realistic urban environments. The simulation results can be very valuable for various urban applications, for example to develop and improve mitigation strategies related to heat stress or air pollution. For the accurate modelling of urban environments, realistic boundary conditions need to be considered for the atmosphere, the local environment and the soil. The local environment with its geospatial components is described in the static driver of the model and follows a standardized format. The main input parameters describe surface type, buildings and vegetation. Depending on the desired simulation scenario and the available data, the local environment can be described at different levels of detail. To compile a complete static driver describing a whole city, various data sources are used, including remote sensing, municipal data collections and open data such as OpenStreetMap. This article shows how input data sets for three German cities were derived. Based on these data sets, the static driver for PALM can be generated. As the collection and preparation of input data sets is tedious, prospective research aims at the development of a semi-automated processing chain to support users in formatting their geospatial data.

Abstract. The PALM model system 6.0 is designed to simulate micro- and mesoscale flow dynamics in realistic urban environments. The simulation results can be very valuable for various urban applications, for example to develop and improve mitigation strategies related to heat stress or air pollution. For the accurate modelling of urban environments, realistic boundary conditions need to be considered for the atmosphere, the local environment, and the soil. The local environment with its geospatial components is described in the static driver of the model and follows a standardized, hereafter called PALM input data standard. The main input parameters describe surface type, buildings and vegetation. Depending on the desired simulation scenario and the available data, the local environment can be described at different levels of detail. To compile a complete static driver describing a whole city, various data sources are used, including remote sensing, municipal data collections and open data such as OpenStreetMap. This manuscript shows how input data sets for three German cities can be derived. Based on these data sets, the static driver for PALM can be generated. As the collection and preparation of input data sets is tedious, prospective research aims at the development of a semi-automated processing chain to support users in formatting their geospatial data.

The paper presents the approach and empirical findings of a study on systematic land-cover change in the upper Western Bug River catchment in Ukraine. Landsat and SPOT images as remote sensing data are used for land-cover classification for the time steps 1989, 2000 and 2010. Thereby, three inner-annual scenes represent the vegetation development for each time step and facilitate classification with the Maximum Likelihood Classifier. Six classes are detected: artificial surface, broad-leaved and coniferous forests, arable land, grassland and water bodies. After this step, land-cover change detection over two decades is conducted. The observed against the expected gross loss and gross gain are statistically analyzed to identify the systematic and random land-cover changes in the study region. Results show that arable land changes not into artificial surface. Arable land changes into grassland and vice versa. This systematic change is very strong. The forest classes interchange whereat broad-leaved forest gains more from coniferous forest in the last decade.