tobac 1.2: towards a flexible framework for tracking and analysis of clouds in diverse datasets

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.

Impacts of Varying Concentrations of Cloud Condensation Nuclei on Deep Convective Cloud Updrafts—A Multimodel Assessment

“…To assess the consistency of the aerosol impacts on cloud evolution and life cycles in these ACPC MIP data, individual clouds should be identified and tracked. Recent software [Tracking and Object-Based Analysis of Clouds (tobac)] has been developed for this purpose and tested with some of the ACPC MIP simulation data (Heikenfeld et al 2019). Through tracking individual clouds in this dataset, the robustness of aerosol impacts on the evolution of clouds for the ACPC MIP could be assessed.…”

TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets

“…An interesting result that emerged from this analysis was that upper-level geopotential layer thickness (typically Z300 minus Z500) was the most robust indicator of an upper-level warm core across prod-ucts. In this section we apply the same configuration that provided maximal agreement between earlier-generation reanalyses and IBTrACS to ERA5 input (Hersbach et al, 2020) so as to identify ERA5 TC tracks.…”

TempestExtremes v2.1: A Community Framework for Feature Detection, Tracking and Analysis in Large Datasets

“…It is expected that TE will continue to evolve to meet the needs of the scientific community. New kernels are already being investigated that encompass functionality present in other standalone trackers (Parfitt et al, 2017;Feng et al, 2018;Heikenfeld et al, 2019). It will further continue to maximize its robustness across datasets, so ensuring the framework is useful for standalone users and operational modeling centers, or for comparative analysis across reanalysis products, multi-model ensembles (Eyring et al, 2016) and single model ensembles (Kay et al, 2015).…”

“…Beyond these traditionally tracked features, many recent papers have focused on defining regionally-relevant features such as monsoonal lows and depressions, associated with heavy precipitation in monsoonal regions (Hurley and Boos, 2015;Vishnu et al, 2020). Areal feature tracking algorithms have also been developed for clouds (Heikenfeld et al, 2019), atmospheric rivers (Shields et al, 2018;Rutz et al, 2019), atmospheric blocking (Scherrer et al, 2006), mesoscale convective systems (Prein et al, 2017;Feng et al, 2018), precipitation clusters (Clark et al, 2014;Pendergrass et al, 2016), and frontal systems (Hope et al, 2014;Schemm et al, 2015;Parfitt et al, 2017). Both nodal and areal algorithms generally feature a similar set of kernels, motivating the development of a single package encompassing relevant capabilities.…”