The advanced algorithm for tracking objects (AALTO)

Abstract

ABSTRACT:The advanced algorithm for the tracking of objects (AALTO) constructs tracks from objects, such as thunderstorms or mesocyclones, detected by multiple weather radars at irregular time intervals. It is important to have high accuracy in tracking thunderstorms to generate skilful forecasts and high-quality climatologies and, fundamentally, to ensure that any derived product from tracks captures only that particular storm, and in its entirety. AALTO incorporates many of the best practices of existing tracking algorithms and techniques employed by meteorologists in constructing tracks. AALTO differs from existing algorithms designed to track meteorological phenomena that manifest in radar data in the following ways: (1) AALTO is designed to track objects from multiple radars, enabling analysis over a larger domain than if a single radar was used; (2) improved tracking is realized through improved initial motion estimates and directional thresholding and (3) AALTO looks both at the track history and at the subsequent possible positions along a track when constructing the best possible tracks, mimicking the approach that would be taken by a human meteorologist. Verification was done using metrics that were objectively determined to distinguish between good and degraded tracks; a description of the approach to determine the appropriate metrics is presented. An overview of the AALTO tracking procedure and an example case are presented in this study.

Use of polarimetric radar measurements to constrain simulated convective cell evolution: a pilot study with Lagrangian tracking

“…4 and 7. Similar conclusions have been reached in past studies (e.g., Loney et al, 2002). Future simulations can obtain arbitrarily higher spatial resolution and output timing, whereas radar measurements are subject to cell distance from the radar and, in this study, the fixed scanning strategies of the operational weather radar.…”

“…Observations also show differences in K DP versus Z DR column morphology (Zrnić et al, 2001;Loney et al, 2002;Kumjian and Ryzhkov, 2008), which have been attributed to differing sensitivities to hydrometeor size distribution and phase characteristics (e.g., Kumjian et al, 2014b;Snyder et al, 2017b). However, precise attribution of specific morphological features at various wavelengths remains a challenge due to a paucity of colocated in situ measurements, the complexity of updraft microphysics, and uncertainties in calculating hydrometeor electromagnetic properties, especially for mixed-phase particles (e.g., Loney et al, 2002;Ryzhkov et al, 2011;Snyder et al, 2013Snyder et al, , 2017b.…”

“…Polarimetric radar systems such as those operated by the National Weather Service Next-Generation Radar (NEXRAD) program (NOAA, 2017) provide a rich source of information about the size distribution, phase, and shape of hydrometeors (Zrnić and Ryzhkov, 1999), which is especially valuable for the study of convective updraft physics because of the paucity of such data available from aircraft (e.g., Loney et al, 2002;Fridlind et al, 2015;Snyder et al, 2015). Comparison of reflectivity and phase-shift differentials between horizontal and vertical radar polarizations yields differential reflectivity (Z DR ) and specific differential phase (K DP ), which are related to the presence of horizontally aligned oblate or prolate hydrometeors when positive (Bringi and Chandrasekar, 2001).…”

“…Comparison of reflectivity and phase-shift differentials between horizontal and vertical radar polarizations yields differential reflectivity (Z DR ) and specific differential phase (K DP ), which are related to the presence of horizontally aligned oblate or prolate hydrometeors when positive (Bringi and Chandrasekar, 2001). Vertically elongated columns of positive Z DR and K DP that extend above the environmental melting level (Z DR and K DP columns) have been generally attributed to the presence of supercooled liquid associated with a deep convective updraft that is not otherwise identifiable from reflectivity alone (Bringi et al, 1996;Hubbert et al, 1998;Loney et al, 2002;Kumjian et al, 2014a). Recent studies suggest a strong connection between K DP and Z DR columns and other metrics of deep convective activity such as overshooting tops (Homeyer and Kumjian, 2015) and lighting flash rate and updraft mass flux (van Lier-Walqui et al, 2016).…”

“…Many past studies have effectively examined deep convective cells using observations from individual radar scans (e.g., Hubbert et al, 1998;Loney et al, 2002). Tracking convective cells or other identified features in time increases the amount of information that can be gleaned from scanning radar because temporal aspects such as cell lifetime can be quantified (e.g., Stein et al, 2015).…”