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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. [ABSTRACT FROM AUTHOR]

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 two decades that attempt to address this need, but most have limitations on the type of data they can be used with; 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. [ABSTRACT FROM AUTHOR]

The validation of convective processes in general circulation models requires the use of large datasets that provide long term climatologies of the spatial statistics of convection. To that regard, echo top heights (ETHs) retrieved from 17 years of data from C-band POLarization (CPOL) Radar are analyzed in varying phases of the Madden-Julian Oscillation (MJO) and Northern Australian Monsoon in order to provide ample validation statistics for the Department of Energy's next generation Earth Energy Exascale Model. In this paper, ETHs are retrieved using a novel methodology that uses the texture of radial velocity. Comparisons of retrieved ETHs against satellite retrieved cloud top heights from the split window technique show that the estimated ETH are correlated with, and, on average, are within 3 km of satellite retrieved cloud top heights. Using this technique gives comparable ETHs compared to using a reflectivity threshold. Bimodal distributions of ETH, likely attributable to the cumulus congestus and mature stages of convection, are more commonly observed when the active phase of the MJO is away from Australia. The presence of a convectively stable layer at around 5 km altitude over Darwin inhibiting convection past this level can explain the position of the modes at around 5 to 6 km and 12 to 13 km respectively. The spatial distributions show that Hector, a deep convective system that occurs almost daily during the wet season over the Tiwi Islands, and seabreeze convergence lines are likely more common in break conditions. Oceanic mesoscale convective systems (MCSes) are likely more common during the night. Unimodal distributions of ETH are more common during monsoon conditions and during an active MJO over Darwin, consistent with the presence of widespread MCSes that are commonly associated with both the MJO and the Northern Australian Monsoon. In general, the MJO is a greater control of the ETHs observed over Darwin, with generally both lower and more unimodal distributions of ETH when the MJO is active over Darwin. [ABSTRACT FROM AUTHOR]

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program's Southern Great Plains (SGP) site includes a heterogeneous distributed scanning Doppler radar network suitable for collecting coordinated Doppler velocity measurements in deep convective clouds. The surrounding National Weather Service (NWS) Next Generation Weather Surveillance Radar 1988 Doppler (NEXRAD WSR-88D) further supplements this network. Radar velocity measurements are assimilated in a three-dimensional variational (3DVAR) algorithm that retrieves horizontal and vertical air motions over a large analysis domain (100km×100km) at storm-scale resolutions (250m). For the first time, direct evaluation of retrieved vertical velocities with those from collocated 915-MHz radar wind profilers is performed. Mean absolute and root-mean-square differences between the two methods are on the order of 1m-1 and 2m-1, respectively. Moderate time-height correlations on the order of 0.5 are also shown to exist between the two methods. An empirical sensitivity analysis is done to determine a range of 3DVAR constraint weights that adequately satisfy both velocity observations and anelastic mass continuity. It is shown that the vertical velocity spread over this range is on the order of 1m-1. A similar sensitivity analysis reveals that iterative multi-Doppler techniques have difficulty satisfying velocity observations and mass continuity simultaneously. These results provide a form of assurance in the use of 3DVAR retrieved vertical velocities for evaluating numerical simulations of deep convective clouds [ABSTRACT FROM AUTHOR]