An Adaptive Tracking Algorithm for Convection in Simulated and Remote Sensing Data

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  • 1 Indian Institute of Tropical Meteorology, Pune. Ministry of Earth Sciences, India.
  • 2 Environmental Science Division, Argonne National Laboratory, Argonne, Illinois
  • 3 ARC Centre of Excellence for Climate Extremes, School of Earth Science, The University of Melbourne, Australia.
  • 4 ARC Centre of Excellence for Climate Extremes, School of Earth, Atmosphere and Environment, Monash University, Melbourne, Australia.
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Abstract

A robust and computationally efficient object tracking algorithm is developed by incorporating various tracking techniques. Physical properties of the objects, such as brightness temperature or reflectivity, are not considered. Therefore, the algorithm is adaptable for tracking convection-like features in simulated data and remotely sensed two-dimensional images. In this algorithm, a first guess of the motion, estimated using the Fourier phase shift, is used to predict the candidates for matching. A disparity score is computed for each target-candidate pair. The disparity also incorporates overlapping criteria in the case of large objects. Then the Hungarian method is applied to identify the best pairs by minimizing the global disparity. The high disparity pairs are unmatched, and their target and candidate are declared expired and newly initiated objects, respectively. They are tested for merger and split, based on their size and overlap with the other objects. The sensitivity of track duration is shown for different disparity and size thresholds. The paper highlights the algorithm’s ability to study convective life cycles using radar and simulated data over Darwin, Australia. The algorithm skillfully tracks individual convective cells (few pixels in size) and large convective systems. The duration of tracks and cell size are found to be log-normally distributed over Darwin. The evolution of size and precipitation types of isolated convective cells is presented in the Lagrangian perspective. This algorithm is part of a vision for a modular platform (viz. TINT and tobac) that will evolve into a sustainable choice to analyze atmospheric features.

Corresponding author: Bhupendra Raut, bhupendra.raut@gmail.com

Abstract

A robust and computationally efficient object tracking algorithm is developed by incorporating various tracking techniques. Physical properties of the objects, such as brightness temperature or reflectivity, are not considered. Therefore, the algorithm is adaptable for tracking convection-like features in simulated data and remotely sensed two-dimensional images. In this algorithm, a first guess of the motion, estimated using the Fourier phase shift, is used to predict the candidates for matching. A disparity score is computed for each target-candidate pair. The disparity also incorporates overlapping criteria in the case of large objects. Then the Hungarian method is applied to identify the best pairs by minimizing the global disparity. The high disparity pairs are unmatched, and their target and candidate are declared expired and newly initiated objects, respectively. They are tested for merger and split, based on their size and overlap with the other objects. The sensitivity of track duration is shown for different disparity and size thresholds. The paper highlights the algorithm’s ability to study convective life cycles using radar and simulated data over Darwin, Australia. The algorithm skillfully tracks individual convective cells (few pixels in size) and large convective systems. The duration of tracks and cell size are found to be log-normally distributed over Darwin. The evolution of size and precipitation types of isolated convective cells is presented in the Lagrangian perspective. This algorithm is part of a vision for a modular platform (viz. TINT and tobac) that will evolve into a sustainable choice to analyze atmospheric features.

Corresponding author: Bhupendra Raut, bhupendra.raut@gmail.com
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