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Nansat is a Python toolbox for analysing and processing 2-dimensional geospatial data, such as satellite imagery, output from numerical models, and gridded in-situ data. It is created with strong focus on facilitating research, and development of algorithms and autonomous processing systems. Nansat extends the widely used Geospatial Abstraction Data Library (GDAL) by adding scientific meaning to the datasets through metadata, and by adding common functionality for data analysis and handling (e.g., exporting to various data formats). Nansat uses metadata vocabularies that follow international metadata standards, in particular the Climate and Forecast (CF) conventions, and the NASA Directory Interchange Format (DIF) and Global Change Master Directory (GCMD) keywords. Functionality that is commonly needed in scientific work, such as seamless access to local or remote geospatial data in various file formats, collocation of datasets from different sources and geometries, and visualization, is also built into Nansat. The paper presents Nansat workflows, its functional structure, and examples of typical applications.

Urban land surface processes need to be represented to inform future urban climate and building energy projections. Here, the single layer urban canopy model Town Energy Balance (TEB) is coupled to the Weather Research and Forecasting (WRF) model to create WRF‐TEB. The coupling method is described generically, implemented into software, and the code and data are released with a Singularity image to address issues of scientific reproducibility. The coupling is implemented modularly and verified by an integration test. Results show no detectable errors in the coupling. Separately, a meteorological evaluation is undertaken using observations from Toulouse, France. The latter evaluation, during an urban canopy layer heat island episode, shows reasonable ability to estimate turbulent heat flux densities and other meteorological quantities. We conclude that new model couplings should make use of integration tests as meteorological evaluations by themselves are insufficient, given that errors are difficult to attribute because of the interplay between observational errors and multiple parameterization schemes (e.g., radiation, microphysics, and boundary layer).

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