Abstract. This paper presents the Meso-NH model version 5.4. Meso-NH is an atmospheric non hydrostatic research model that is applied to a broad range of resolutions, from synoptic to turbulent scales, and is designed for studies of physics and chemistry. It is a limited-area model employing advanced numerical techniques, including monotonic advection schemes for scalar transport and fourth-order centered or odd-order WENO advection schemes for momentum. The model includes state-of-the-art physics parameterization schemes that are important to represent convective-scale phenomena and turbulent eddies, as well as flows at larger scales. In addition, Meso-NH has been expanded to provide capabilities for a range of Earth system prediction applications such as chemistry and aerosols, electricity and lightning, hydrology, wildland fires, volcanic eruptions, and cyclones with ocean coupling. Here, we present the main innovations to the dynamics and physics of the code since the pioneer paper of Lafore et al. (1998) and provide an overview of recent applications and couplings.

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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

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