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Abstract This study evaluates a popular density current propagation speed equation using a large, novel set of radiosonde and dropsonde observations. Data from pairs of sondes launched inside and outside of cold pools along with the theoretical density current propagation speed equation are used to calculate sonde-based propagation speeds. Radar-/satellite-based propagation speeds, assumed to be the truth, are calculated by manually tracking the propagation of cold pools and correcting for advection due to the background wind. Several results arise from the comparisons of the theoretical sonde-based speeds with the radar-/satellite-based speeds. First, sonde-based and radar-based propagation speeds are strongly correlated for U.S. High Plains cold pools, suggesting the density current propagation speed equation is appropriate for use in midlatitude continental environments. Second, cold pool Froude numbers found in this study are in agreement with previous studies. Third, sonde-based propagation speeds are insensitive to how cold pool depth is defined since the preponderance of negative buoyancy is near the surface in cold pools. Fourth, assuming an infinite channel depth and assuming an incompressible atmosphere when deriving the density current propagation speed equation can increase sonde-based propagation speeds by up to 20% and 11%, respectively. Finally, sonde-based propagation speeds can vary by ∼300% based on where and when the sondes were launched, suggesting submesoscale variability could be a major influence on cold pool propagation. 

Abstract. There is a continuously increasing need for reliable feature detection and tracking tools based on objective analysis principles for use with meteorological data. Many tools have been developed over the previous 2 decades that attempt to address this need but most have limitations on the type of data they can be used with, feature computational and/or memory expenses that make them unwieldy with larger datasets, or require some form of data reduction prior to use that limits the tool's utility. The Tracking and Object-Based Analysis of Clouds (tobac) Python package is a modular, open-source tool that improves on the overall generality and utility of past tools. A number of scientific improvements (three spatial dimensions, splits and mergers of features, an internal spectral filtering tool) and procedural enhancements (increased computational efficiency, internal regridding of data, and treatments for periodic boundary conditions) have been included in tobac as a part of the tobac v1.5 update. These improvements have made tobac one of the most robust, powerful, and flexible identification and tracking tools in our field to date and expand its potential use in other fields. Future plans for tobac v2 are also discussed.