Abstract
Abstract. We introduce tobac (Tracking and Object-Based Analysis of Clouds), a newly developed framework for tracking and analysing individual clouds in different types of datasets, such as cloud-resolving model simulations and geostationary satellite retrievals. The software has been designed to be used flexibly with any two- or three-dimensional time-varying input. The application of high-level data formats, such as Iris cubes or xarray arrays, for input and output allows for convenient use of metadata in the tracking analysis and visualisation. Comprehensive analysis routines are provided to derive properties like cloud lifetimes or statistics of cloud properties along with tools to visualise the results in a convenient way. The application of tobac is presented in two examples. We first track and analyse scattered deep convective cells based on maximum vertical velocity and the three-dimensional condensate mixing ratio field in cloud-resolving model simulations. We also investigate the performance of the tracking algorithm for different choices of time resolution of the model output. In the second application, we show how the framework can be used to effectively combine information from two different types of datasets by simultaneously tracking convective clouds in model simulations and in geostationary satellite images based on outgoing longwave radiation. The tobac framework provides a flexible new way to include the evolution of the characteristics of individual clouds in a range of important analyses like model intercomparison studies or model assessment based on observational data.
Highlights
Clouds are a major feature of the Earth’s atmosphere and control many critical processes in the Earth’s energy and water budgets (Trenberth et al, 2009)
The workflow of the software package consists of the detection of suitable features, segmentation of the areas or volumes representative of an individual cloud object, and subsequent linking of objects at individual time steps into trajectories
Cloud volumes or cloud areas are associated based on a watershedding technique featuring a single specific threshold value on two- or three-dimensional input fields
Summary
Clouds are a major feature of the Earth’s atmosphere and control many critical processes in the Earth’s energy and water budgets (Trenberth et al, 2009). Several other approaches that included the tracking of individual updrafts in different types of cumulus clouds in a very detailed manner (Sherwood et al, 2013; Hernandez-Deckers and Sherwood, 2016) would not be transferable to data with a lower temporal and spatial resolution Despite these advances in developing detailed cloud tracking approaches for use in highly resolved model simulations, most current studies are performed with model grid spacings of several hundred metres to a few kilometres, especially when using larger. Providing adequate ways of performing tracking and object-based analyses for different types of clouds, including deep convection, in these kinds of simulations provides a key pathway to better understanding the underlying physical processes This overview clearly shows the wide range of extensive efforts that went into the development of elaborate software and analysis tools to track clouds in different types of datasets. By making use of the framework consistently across different datasets like this, we can compare the tracked clouds in both data sources by examining the statistical properties of the resulting population of convective clouds, thereby facilitating model–observation comparisons
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